Impact Outcome Pathways (IOP): A Next-Generation Framework for Safe and Sustainable Drug Development

Abstract

This article explores the transformative potential of the Impact Outcome Pathway (IOP) framework for integrating safety and sustainability into pharmaceutical research and development. Moving beyond traditional siloed assessments, IOPs establish mechanistic links between a drug candidate's properties and its broader environmental, health, and socio-economic consequences throughout its life cycle. Tailored for researchers, scientists, and drug development professionals, we examine the foundational concepts of IOPs, detail methodological approaches for application, address common implementation challenges, and review validation through real-world case studies. By bridging mechanistic toxicology with life-cycle and socio-economic analysis, the IOP framework enables a proactive, data-driven approach to designing safer and more sustainable medicines.

Beyond AOPs: Understanding the Foundations of Impact Outcome Pathways

The Impact Outcome Pathway (IOP) framework represents a transformative evolution in chemical and material safety assessment, extending the established Adverse Outcome Pathway (AOP) concept to encompass integrated health, environmental, social, and economic impacts. Developed under European Union initiatives like the INSIGHT project, IOPs establish mechanistic links between chemical properties and their broad consequences through a computational, data-driven approach aligned with Safe and Sustainable by Design (SSbD) principles. This whitepaper provides researchers and drug development professionals with a technical examination of IOP foundations, quantitative modeling methodologies, and implementation protocols that enable predictive risk assessment while supporting sustainability goals under the European Green Deal.

The chemical and pharmaceutical development landscape is undergoing a paradigm shift from fragmented risk assessment toward integrated impact evaluation. Traditional Adverse Outcome Pathways (AOPs) have provided valuable mechanistic frameworks linking molecular initiating events (MIEs) through key events (KEs) to adverse outcomes (AOs) of regulatory significance [1]. However, their primary focus on toxicological endpoints limits utility for comprehensive sustainability assessment.

The Impact Outcome Pathway (IOP) framework addresses this limitation by extending the AOP concept to establish mechanistic links between chemical/material properties and their environmental, health, and socio-economic consequences [2] [3]. IOPs serve as the core computational structure within integrated assessment frameworks like the EU INSIGHT project, which develops novel approaches for mechanistic impact assessment of chemicals and materials under SSbD principles [4] [5]. This evolution represents a critical advancement for drug development professionals seeking to align product innovation with sustainability objectives.

Core Conceptual Framework of Impact Outcome Pathways

Theoretical Foundations and Definitions

IOPs expand the AOP conceptual model by incorporating multiple dimensions of impact assessment into a unified structure:

• Molecular Initiating Events (MIEs): The initial interaction between a chemical substance and a biological target [1]
• Key Events (KEs): Measurable biological responses at different levels of biological organization [1]
• Adverse Outcomes (AOs): Specialized KEs of regulatory significance at individual or population levels [1]
• Impact Outcomes: Expanded endpoints encompassing environmental, social, and economic consequences beyond traditional toxicological AOs [2]

The IOP framework systematically integrates these elements through Impact Outcome Pathway networks that capture complex interactions and trade-offs between different impact dimensions [2]. This multi-dimensional approach facilitates transparent decision-making by mapping mechanistic toxicological data to broader sustainability metrics.

Comparative Analysis: AOP vs. IOP Frameworks

Table 1: Comparative characteristics of AOP and IOP frameworks

Characteristic	Adverse Outcome Pathway (AOP)	Impact Outcome Pathway (IOP)
Primary Focus	Toxicological hazard assessment [1]	Integrated health, environmental, social and economic impacts [2]
Regulatory Context	Chemical risk assessment, hazard identification [1]	Safe and Sustainable by Design (SSbD) [2]
Assessment Scope	Linear pathways from MIE to AO [1]	Networked pathways capturing synergies and trade-offs [2]
Methodological Approach	Qualitative with emerging quantitative applications [1]	Computational, data-driven, and quantitative [2]
Data Integration	Primarily toxicological data [1]	Multi-source (omics, LCA, exposure models, socio-economic) [2]
Stakeholder Utility	Hazard characterization, testing prioritization [1]	Holistic decision-support for sustainable innovation [2]

Quantitative IOP Modeling Methodologies

Bayesian Network Modeling for Quantitative Pathways

Bayesian Networks (BNs) provide a natural computational framework for quantifying IOPs due to their ability to represent causal relationships and propagate uncertainty [1]. A BN is a probabilistic model represented as a set of nodes (variables) connected by arrows (causal relationships) [1]. The quantified AOP-BN model can be run in several directions: (1) prognostic inference (forward from stressor to AO prediction); (2) diagnostic inference (backward from AO node); and (3) omnidirectionally from intermediate MIEs and/or KEs [1].

The mathematical formulation for BN-based IOP quantification involves:

• Regression modeling: Quantifying each dose-response and response-response relationship using Bayesian regression
• Uncertainty propagation: Applying fitted regression models to simulate response values along predictor gradients
• Parameterization: Using simulated values to parameterize conditional probability tables of the BN model [1]

For dynamic systems with repeated exposures, Dynamic Bayesian Networks (DBNs) extend this approach to incorporate temporal evolution of pathway perturbations [6]. This is particularly relevant for chronic toxicity assessment where adverse outcomes manifest only after repeated insults.

Workflow for Quantitative IOP Development

Figure 1: Development workflow for quantitative Impact Outcome Pathway models

Data Integration and FAIR Principles

IOP implementation requires integration of diverse data sources through structured knowledge graphs that adhere to FAIR principles (Findable, Accessible, Interoperable, Reusable) [2]. The INSIGHT project exemplifies this approach by integrating multi-source datasets including:

• Omics data (transcriptomics, proteomics)
• Life cycle inventories (LCI)
• Exposure models and predicted environmental concentrations (PEC)
• Toxicological reference data
• Socio-economic indicators [2]

This integrated data infrastructure supports AI-driven knowledge extraction and enhances predictability of chemical and material impacts across multiple dimensions [2].

Experimental Protocols for IOP Development

Virtual Data Generation for Chronic Toxicity Assessment

For scenarios lacking comprehensive experimental data, such as chronic toxicity from repeated exposures, virtual data generation provides a proof-of-concept approach:

Protocol 1: Virtual Data Generation for Repeated Exposure IOP Modeling

• Define AOP/IOP Network Structure: Identify MIEs, KEs, BMs (biomarkers), and AOs with causal linkages [6]
• Establish Experimental Design Parameters:
  • Set number of doses (including controls)
  • Determine number of donors/virtual subjects
  • Define exposure repetition scheme [6]
• Program Acute-Phase Response:
  • Code robust dose-dependence for all acute-phase biological responses
  • Ensure responses occur for all exposures [6]
• Implement Chronic-Phase Response:
  • Program donor-dependent timing of chronic-phase responses
  • Establish dose-dependence and exposure-repetition dependence post-elicitation [6]
• Generate Replicate Data:
  • Create multiple replicates for each donor-exposure combination
  • Incorporate appropriate statistical variance [6]

This protocol was successfully implemented in a proof-of-concept study using a hypothetical AOP with 2 MIEs, 2 acute-phase KEs, 8 biomarkers, 6 chronic-phase KEs, and 1 AO across 6 exposure repetitions with 8 virtual donors [6].

Bayesian Network Quantification Methodology

Protocol 2: Bayesian Network Parameterization for IOPs

• Structure Definition:

  • Define nodes corresponding to MIEs, KEs, and AOs
  • Establish directed acyclic graph structure based on causal relationships [1]

• Relationship Quantification:

  • For each dose-response and key event relationship, apply Bayesian regression modeling
  • Use dose-response functions commonly applied in toxicology [1]

• Uncertainty Propagation:

  • Apply fitted regression models to simulate response values along predictor gradients
  • Generate sufficient iterations (e.g., 10,000) for robust probability estimation [1]

• Conditional Probability Table Development:

  • Use simulated values to parameterize conditional probability tables of BN model
  • Define discrete states for each node based on biological response thresholds [1]

• Model Validation:

  • Conduct internal validation through prognostic, diagnostic, and omnidirectional inference
  • Assess prediction accuracy across different node resolutions [1]

This approach enables quantification of IOPs even with limited data, providing a probabilistic framework for predicting adverse outcomes based on upstream key events [1].

Implementation and Case Studies

INSIGHT Framework and Application

The EU INSIGHT project implements the IOP framework through a multi-layer computational architecture consisting of:

• Data Graph: Integrating multi-source datasets into FAIR-compliant knowledge graphs
• Model Graph: Computational models for predicting chemical and material impacts
• IOP Graph: Mechanistic pathways linking chemical properties to comprehensive impacts [4]

This framework is being validated through four case studies targeting:

• Per- and polyfluoroalkyl substances (PFAS)
• Graphene oxide (GO)
• Bio-based synthetic amorphous silica (SAS)
• Antimicrobial coatings [2]

These applications demonstrate how multi-model simulations and AI-driven knowledge extraction enhance predictability and interpretability of chemical impacts [2].

Decision-Support Implementation

For stakeholder implementation, INSIGHT develops interactive decision maps that provide accessible, regulatory-compliant risk and sustainability assessments [2] [4]. These web-based tools guide users through the decision-making process, aiding evaluation of social, economic, health and environmental impacts of chemicals and materials [5].

Table 2: Quantitative IOP Modeling Parameters from Proof-of-Concept Studies

Parameter	BN Model for AOP #245 [1]	DBN for Chronic Toxicity [6]
Network Size	2 MIEs, 3 KEs, 1 AO	2 MIEs, 8 KEs, 8 BMs, 1 AO
Data Source	Lemna minor exposure to 3,5-dichlorophenol	Virtual dataset for repeated exposure
Exposure Regimen	Single exposure	6 repeated exposures
Statistical Approach	Bayesian regression + BN	Dynamic BN with LASSO-based pruning
Validation Method	Internal validation of inference directions	Probability calculation of AO given upstream KEs
Key Finding	High accuracy when run from intermediate nodes	Causal structure changes over repeated exposures

Essential Research Reagent Solutions

Table 3: Essential Research Reagents and Computational Tools for IOP Development

Reagent/Tool	Function	Application in IOP Development
Bayesian Network Software (e.g., R Bayesian packages)	Probabilistic modeling and uncertainty quantification	Parameterizing conditional probability tables for IOPs [1] [6]
FAIR Data Management Platforms	Structured knowledge graph implementation	Integrating multi-source datasets into IOP frameworks [2]
Omics Analysis Tools	Transcriptomic, proteomic data generation	Quantifying molecular initiating events and early key events [2]
Dose-Response Modeling Software	Regression analysis for key event relationships	Quantifying relationships between stressors and biological responses [1]
Virtual Data Generation Algorithms	Synthetic dataset creation for model validation	Testing IOP performance when experimental data is limited [6]
Dynamic Bayesian Network Platforms	Temporal modeling of pathway perturbations	Assessing cumulative impacts from repeated exposures [6]

The Impact Outcome Pathway framework represents a significant advancement in chemical and material assessment, extending traditional toxicological pathways to encompass holistic impact evaluation. Through computational approaches like Bayesian networks and integration of diverse data sources via FAIR principles, IOPs enable quantitative prediction of environmental, health, social, and economic impacts. The methodologies and protocols outlined in this technical guide provide researchers and drug development professionals with practical tools for implementing IOPs within SSbD frameworks, supporting the transition toward safer and more sustainable chemical innovation aligned with European Green Deal objectives and global sustainability goals.

The development of innovative therapies, particularly for complex nervous system disorders, is increasingly hampered by a reliance on traditional, siloed assessment methods. These fragmented approaches fail to capture the interconnected nature of biological systems and the full cascade of effects from intervention to outcome, leading to high failure rates, unsustainable costs, and prolonged timelines. This whitepaper details the quantitative evidence of this critical gap and advocates for the adoption of an Impact Outcome Pathway (IOP) framework. By providing a systematic methodology to link interventions to their ultimate impacts through a defined pathway, the IOP framework enables more predictive, holistic, and sustainable drug development.

The Evidence Gap: Quantifying the Drug Development Challenge

Traditional drug development is characterized by sequential, disjointed stages where information transfer is often incomplete. The following data illustrates the magnitude of the challenges created by this fragmented system.

Table 1: Key Challenges in Traditional Drug Development for Nervous System Disorders [7].

Challenge	Impact on Development	Underlying Cause in Fragmented Assessment
Unknown Pathophysiology	Difficulties in target identification and validation for many disorders.	Lack of a framework to integrate clinical observations with molecular data into a coherent disease model.
Translational Failures of Animal Models	Inability to accurately predict human efficacy, dose, and tolerability.	Assessing efficacy in animal models that do not fully recapitulate human disease [7].
Lack of Biomarkers	No objective measures for proof of mechanism, patient stratification, or treatment response.	Siloed development where biomarker discovery is disconnected from clinical endpoint validation.
Patient Heterogeneity	Larger, more complex, and expensive clinical trials with high failure rates.	Inadequate phenotyping and endotyping to define homogenous patient subgroups for targeted therapies [7].

A 2024 global survey of biotech and biopharma leaders further quantifies the operational consequences of these scientific challenges. The rising cost of clinical trials was the top challenge, cited by 49% of respondents, followed by patient recruitment and the increasing complexity of trial protocols [8]. These issues are direct symptoms of an assessment model that cannot efficiently predict success or identify the right patients for the right therapy.

The IOP Framework: A Systematic Pathway from Intervention to Impact

The Impact Outcome Pathway (IOP) framework, derived from the established "Impact Pathway" methodology, offers a structured alternative to fragmented assessment. It is a systematic technique for outlining activities and evaluating their effects by tracing the pathway from initial intervention through to ultimate outcomes and impacts [9].

The core of the IOP methodology involves four critical steps that ensure a holistic and comparative assessment:

• Define Activities and Map Inputs/Outputs: Catalog the specific corporate activities (e.g., drug candidate administration) and detail all associated inputs (tangible and intangible resources) and immediate outputs (direct results) [9].
• Determine Activity Outcomes: Identify the effects that the activity's outputs have on stakeholder valuables (e.g., human wellbeing, ecosystem health). This connects the biological effect to a meaningful change for patients or other stakeholders [9].
• Establish a Reference Scenario: Select and analyze a reference activity—what would have occurred without the intervention (e.g., standard of care, placebo)—and repeat steps 1 and 2 for this scenario. This provides the baseline for comparison and is crucial for determining true net impact [9].
• Quantify Net Impact: The final impact is determined by the difference in outcomes between the intervention and the reference scenario. This reveals the true value added (or subtracted) by the new therapy [9].

Diagram 1: IOP framework for net impact quantification.

Experimental Protocols for IOP Construction and Validation

Implementing the IOP framework requires rigorous, detailed methodologies at each stage. Below are protocols for key experiments and analyses essential for building and validating a robust IOP for a novel therapeutic.

Protocol for Quantitative Uncertainty Assessment in the IOP

Uncertainty is a key challenge in any predictive framework. A stochastic approach based on Monte Carlo simulation provides a more realistic quantification of uncertainty in the final impact estimate compared to deterministic models [10].

• Objective: To quantify the aggregate uncertainty in the final impact estimate (e.g., years of life lost, quality-adjusted life years) of a therapeutic intervention by propagating uncertainty from all individual parameters in the IOP.
• Methodology:
  • Parameter Identification: Systematically list all parameters in the IOP (e.g., emission factors, dispersion coefficients, dose-response relationships, monetization values) [10].
  • Classification and Distribution Assignment:
    • Classify parameters based on data availability: extensively available data (e.g., clinical measurements) vs. data with little information (e.g., novel biomarker utility).
    • Assign appropriate probability distributions (e.g., normal, log-normal, uniform) to each parameter based on empirical data or expert elicitation. Not all parameters follow normal distributions [10].
  • Monte Carlo Simulation:
    • Run a large number of iterations (e.g., 10,000). In each iteration, a value for every parameter is randomly drawn from its defined probability distribution.
    • For each complete set of drawn parameters, calculate the final impact.
  • Analysis of Results:
    • The result is a probability distribution of the final impact, from which a mean value and geometric deviation can be derived.
    • Perform sensitivity analysis (e.g., regression-based) to determine which input parameters contribute most to the variance in the final output, guiding future research priorities [10].

Protocol for Integrating Human Biomarker Data to De-risk Translation

Heavy reliance on animal models with poor predictive validity is a major source of failure. This protocol emphasizes early integration of human-derived data to validate the initial stages of the IOP.

• Objective: To use human biomarker data for improved target identification and validation, thereby creating a more reliable early-stage IOP.
• Background: The unknown pathophysiology for many disorders makes target identification challenging. Greater emphasis on human data can lead to improved target identification and validation [7].
• Procedure:
  • Patient Stratification: Conduct detailed clinical phenotyping and endotyping of the patient population to define homogenous subgroups. This addresses heterogeneity and enables more precise linkage between target modulation and outcome [7].
  • Biomarker Discovery and Validation: Identify and validate diagnostic and therapeutic biomarkers that can objectively detect and measure biological states. Biomarkers are essential for providing proof of mechanism and refining targets in the IOP [7].
  • Human-Relevant Model Systems: Prioritize the use of human cellular models (e.g., induced pluripotent stem cell-derived neurons) or other human-data driven approaches to test lead compounds for target engagement and functional effects before proceeding to complex animal models.
  • Phase Ib Proof-of-Concept (POC) Trials: Design Phase Ib trials to provide early evidence of efficacy in humans. A typical POC trial is a small, controlled study conducted at fewer than 4 sites with less than 100 subjects/patients, using the validated biomarkers as key outcome measures [7].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Key Research Reagents and Tools for IOP-Driven Drug Development

Item / Solution	Function in IOP Framework	Application Example
Human iPSC-Derived Cell Lines	Provide a human-relevant system for target validation and initial compound screening, de-risking the early "Output" stage of the IOP.	Modeling neuronal signaling in a genetically defined background for neurodegenerative disease research [7].
Validated Biomarker Assays	Quantify target engagement, pathway modulation, and pharmacological response; critical for linking IOP stages from Output to Outcome.	Using CSF p-tau levels as a pharmacodynamic biomarker in an Alzheimer's disease trial to confirm disease pathway modulation [7].
Precision Medicine Biobanks	Collections of well-phenotyped patient samples essential for patient stratification and validating the linkage between a target and a specific patient endotype.	Identifying genetic markers that predict response to a novel oncology or CNS therapeutic, defining the applicable patient population [7].
Stochastic Simulation Software	Enables Monte Carlo simulation for quantitative uncertainty assessment across the entire IOP, transforming a point estimate into a probability distribution.	Using @RISK or Crystal Ball to model the uncertainty in the final calculated clinical benefit of a new drug [10].

Visualizing the Shift: From Fragmented Silos to an Integrated Pathway

The fundamental change advocated by the IOP framework is a move from disconnected assessments to a unified, traceable pathway.

Diagram 2: Contrasting traditional and IOP assessment approaches.

The critical gap in drug development is not merely a lack of effective compounds, but a systemic reliance on fragmented assessment methods that are ill-suited for the complexity of biological systems and the goal of sustainable impact. The IOP framework directly addresses this gap by providing a structured, transparent, and holistic methodology that connects a therapeutic intervention to its ultimate outcomes and impacts on human wellbeing. By mandating a reference scenario, it forces a rigorous evaluation of net benefit. By accommodating quantitative uncertainty analysis, it provides decision-makers with a more realistic risk profile. For researchers and drug developers, adopting the IOP framework is a strategic imperative to de-risk development, accelerate the creation of meaningful therapies, and fulfill the promise of a sustainable-by-design approach to healthcare innovation.

The Impact Outcome Pathway (IOP) framework represents a significant evolution in chemical and material risk assessment, extending the established Adverse Outcome Pathway (AOP) concept to enable integrated evaluation across multiple domains. While AOPs provide a structured approach to understanding toxicological effects through a sequence of biologically linked events—from Molecular Initiating Events (MIEs) to Adverse Outcomes (AOs) at the organism or population level—they traditionally focus primarily on human health and ecological toxicology [11]. The IOP framework builds upon this mechanistic foundation but introduces critical linkages that bridge health, environmental, socio-economic, and lifecycle assessment domains [2] [12].

This integration addresses a fundamental limitation in current Safe and Sustainable by Design (SSbD) approaches, which often evaluate these dimensions independently, failing to capture essential cross-domain interactions, trade-offs, and cumulative risks [12]. By establishing mechanistic cause-effect chains across these traditionally separate assessment spheres, IOPs provide a unified structure for comprehensive decision-making aligned with the European Green Deal and global sustainability objectives [2]. The framework is particularly valuable for implementing a One Health approach within SSbD, recognizing the interconnected nature of human, animal, and environmental health [12].

The IOP Structural Framework: Components and Linkages

Core Architectural Components

The IOP framework consists of several interconnected components that work together to create a comprehensive assessment structure:

• Molecular Initiating Events (MIEs): The initial interaction between a stressor (e.g., chemical, material) and a biological target that begins the cascade of effects [11]. In expanded IOP applications, this concept can be metaphorically applied to broader initiating events in economic or social systems [13].

• Key Events (KEs): Measurable biological, ecological, or socio-economic changes occurring at different organizational levels after the initiating event [11]. These represent intermediate steps in the pathway.

• Key Event Relationships (KERs): Descriptions of the causal linkages between consecutive key events, including evidence supporting these relationships and quantitative understanding of their dynamics [11]. In IOPs, KERs specifically bridge across different assessment domains [12].

• Adverse Outcomes (AOs): Effects relevant to risk assessment and regulatory decision-making at the individual, population, or system level [11]. In IOPs, these encompass health, environmental, social, and economic consequences.

• Modulating Factors (MFs): Context-dependent variables that influence the progression or severity of key events across biological, ecological, and socio-economic scales [12].

Cross-Domain Integration Mechanism

The innovative capability of IOPs lies in their structured approach to connecting events across traditionally separate assessment domains. This cross-domain integration occurs through several mechanisms:

• Shared Key Events: Single key events that simultaneously influence multiple domains, creating nodal points for interdisciplinary assessment. For example, a chemical release event may simultaneously trigger biological exposure pathways, environmental contamination, and economic impacts on local livelihoods [12].

• Inter-Domain KERs: Causal relationships that explicitly connect key events from different domains, such as linking ecosystem service disruption to socio-economic consequences through mechanistic understanding [2].

• Networked Pathway Architecture: Multiple interconnected AOPs and IOPs forming complex networks that capture system-level interactions and emergent properties [11].

The following diagram illustrates the fundamental structure of an IOP and its relationship to the foundational AOP concept:

IOP Cross-Domain Integration

Quantitative Framework: Metrics and Assessment Parameters

Key Performance Indicators and Decision Factors

The implementation of IOPs within SSbD requires quantitative metrics to assess impacts across domains. The ASINA project demonstrates the application of Key Performance Indicators (KPIs), Key Decision Factors (KDFs), and Physical-Chemical Features (PCFs) to create a measurable assessment framework [14].

Table 1: Quantitative Metrics for IOP Implementation in SSbD

Metric Category	Definition	Domain Application	Example Metrics
Key Performance Indicators (KPIs)	Quantitative measures that reflect performance across health, environmental, economic, and functional dimensions [14]	Cross-domain	Ecotoxicity potential, global warming potential, cost efficiency, functionality scores [14]
Key Decision Factors (KDFs)	Design options that define possible SSbD solutions and influence KPIs [14]	Process and material design	Material selection, synthesis methods, processing parameters, end-of-life strategies [14]
Physical-Chemical Features (PCFs)	Measurable properties of nanomaterials or chemicals that initiate or modulate pathways [14]	Health and environmental domains	Particle size, surface chemistry, reactivity, persistence [14]

Cross-Domain Impact Assessment Parameters

IOPs enable the quantitative assessment of impacts across interconnected domains through standardized parameters that capture both direct and indirect effects.

Table 2: Cross-Domain Impact Assessment Parameters in IOPs

Domain	Assessment Parameters	Measurement Approaches	Regulatory Context
Human Health	Hazard indices, exposure thresholds, disease burden metrics [11] [12]	New Approach Methodologies (NAMs), biomonitoring, epidemiological data [2] [12]	Risk Characterization Ratios (RCRs), Points of Departure (PODs) [11]
Environmental	Ecosystem service impacts, biodiversity loss, resource depletion [13]	Life Cycle Impact Assessment (LCIA), ecological modeling, species sensitivity distributions [2]	Environmental Quality Standards, Planetary Boundaries framework [13]
Socio-Economic	Life cycle costs, employment effects, supply chain resilience [2] [14]	Life Cycle Costing (LCC), social-LCA, economic modeling [2]	Social Life Cycle Assessment (S-LCA), UN Sustainable Development Goals [2]

Experimental and Computational Methodologies

Integrated Workflow for IOP Development

The development and application of IOPs follows a systematic workflow that integrates experimental data, computational modeling, and decision support tools. The EU INSIGHT project demonstrates this through a comprehensive framework that incorporates multi-source datasets into a structured knowledge graph adhering to FAIR principles (Findable, Accessible, Interoperable, Reusable) [2].

IOP Development Workflow

Case Study: PFAS Assessment Using IOP Framework

The application of IOPs to per- and polyfluoroalkyl substances (PFAS) demonstrates the framework's utility for complex, multi-domain impact assessment. PFAS chemicals present significant challenges due to their persistence, bioaccumulation potential, and complex toxicity profiles that span health and environmental domains [11] [12].

Experimental Protocol: Integrated PFAS Assessment

• Molecular Initiating Event Identification

  • In vitro receptor binding assays to identify MIEs (e.g., PPARα activation)
  • High-throughput screening to quantify binding affinities across PFAS structures
  • Transcriptomic analysis (RNA-seq) to identify early gene expression changes

• Key Event Characterization Across Domains

  • Health Domain: Hepatocyte toxicity assays, serum biomarker analysis (e.g., liver enzymes)
  • Environmental Domain: Bioaccumulation studies in aquatic species, soil persistence testing
  • Socio-Economic Domain: Water treatment cost analysis, healthcare burden assessment

• Cross-Domain Integration

  • Quantitative modeling of exposure-response relationships
  • Linking environmental concentrations to human exposure estimates
  • Economic impact assessment of contamination events

• IOP Validation

  • Epidemiological studies correlating biomarkers with health outcomes
  • Ecological monitoring in contaminated sites
  • Retrospective analysis of regulatory actions and their economic impacts

Research Reagent Solutions and Essential Materials

The experimental implementation of IOPs requires specialized reagents, models, and computational tools to generate the mechanistic data necessary for pathway development and validation.

Table 3: Essential Research Tools for IOP Development

Tool Category	Specific Resources	Application in IOPs	Example Uses
Biological Assays	PPARγ reporter gene assays, thyroid hormone disruption screens, oxidative stress markers [11] [12]	MIEs and early Key Events identification	PFAS receptor binding, graphene oxide cytotoxicity [12]
OMICs Technologies	RNA-sequencing, proteomics, metabolomics platforms [2]	Mechanistic pathway elucidation	Developmental neurotoxicity signatures, mitochondrial dysfunction markers [11]
Computational Models	QSAR tools, physiologically based kinetic (PBK) models, exposure models [2]	Quantitative KER development, cross-species extrapolation	Chemical prioritization, bioaccumulation prediction [2]
Alternative Test Systems	Zebrafish embryos, in vitro 3D models, computational toxicology approaches [11] [12]	New Approach Methodologies (NAMs)	Thyroid disruption screening, developmental toxicity assessment [11]

Implementation in Safe and Sustainable by Design Framework

The IOP framework provides the mechanistic foundation for implementing true Safe and Sustainable by Design (SSbD) principles in chemical and material development. The European Commission's SSbD framework emphasizes the integration of safety and sustainability considerations throughout the innovation process, from initial design to end-of-life management [15]. IOPs directly support this integration by providing:

• Mechanistic forecasting of potential impacts across domains during early development stages [12]
• Identification of trade-offs between safety, sustainability, and functionality dimensions [2]
• Decision support for selecting optimal design options that minimize adverse impacts while maintaining performance [14]

The INSIGHT project demonstrates how IOPs are integrated into SSbD through computational frameworks that combine life cycle assessment, risk assessment, and socio-economic analysis into a unified impact assessment methodology [2]. This integration enables designers and manufacturers to proactively address potential adverse impacts rather than reactively managing them after product commercialization.

The Impact Outcome Pathway framework represents a transformative approach to chemical and material assessment by systematically linking key events across health, environmental, and socio-economic domains. Through its structured mechanism of cross-domain Key Event Relationships and Modulating Factors, IOPs address critical limitations in traditional siloed assessment approaches. The quantitative framework of KPIs, KDFs, and PCFs enables measurable implementation within SSbD paradigms, supporting the transition toward safer and more sustainable chemicals and materials. As demonstrated through applications to PFAS, graphene oxide, and other advanced materials, IOPs provide the mechanistic understanding necessary for predictive assessment and proactive design decisions aligned with European Green Deal objectives and global sustainability goals.

The assessment of chemicals and materials has traditionally been fragmented, with health, environmental, social, and economic impacts evaluated independently [2]. This disjointed approach limits the ability to capture trade-offs and synergies necessary for comprehensive decision-making under the Safe and Sustainable by Design (SSbD) framework [2] [3]. The EU-funded INSIGHT project addresses this fundamental challenge by developing a novel computational framework for integrated impact assessment based on the Impact Outcome Pathway (IOP) approach [5] [2]. This pioneering initiative aims to foster a paradigm shift from fragmented, siloed assessments to a holistic and integrated approach that bridges mechanistic toxicology, exposure modeling, life cycle assessment, and socio-economic analysis [5] [2].

Aligned with the European Green Deal and the EU Chemicals Strategy for Sustainability, INSIGHT represents a critical step toward operationalizing the SSbD framework [5] [16]. By establishing mechanistic links between chemical properties and their multi-dimensional impacts, INSIGHT provides a scientifically robust foundation for designing safer and more sustainable chemicals and materials from the earliest innovation stages [2] [3].

The Core Innovation: Impact Outcome Pathways (IOPs)

Conceptual Foundation and Relation to Existing Frameworks

The Impact Outcome Pathway (IOP) framework forms the theoretical backbone of the INSIGHT project, representing a significant extension of the established Adverse Outcome Pathway (AOP) concept [2] [3]. While AOPs focus primarily on toxicological mechanisms within biological organisms, IOPs establish mechanistic links between chemical and material properties and their broader environmental, health, and socio-economic consequences [2]. This conceptual expansion enables a more comprehensive assessment framework that captures not only adverse effects but also sustainability implications across the entire life cycle of chemicals and materials.

The IOP framework integrates three critical dimensions:

• Mechanistic toxicology data from New Approach Methodologies (NAMs)
• Exposure science and environmental fate modeling
• Life cycle assessment (LCA) and socio-economic analysis

This integration allows researchers to trace how molecular-initiating events ultimately propagate to system-level impacts, enabling more predictive and preventative safety and sustainability assessments [2].

Structural Components of IOPs

IOPs are structured as directed graphs that connect key events across multiple levels of biological organization and spatial scales. The core components include:

• Molecular Initiating Events (MIEs): Initial interactions between chemicals and biological targets
• Cellular and Organ-level Responses: Cascading effects through cellular networks
• Organism and Population Outcomes: Individual and population-level consequences
• Ecosystem and Socio-economic Impacts: Broad-scale environmental and societal effects

The following diagram illustrates the conceptual flow of an Impact Outcome Pathway:

Computational Architecture and Data Infrastructure

Multi-Layered Framework Design

The INSIGHT framework employs a sophisticated multi-layered architecture that integrates diverse data sources, computational models, and assessment tools [5]. This architecture consists of three primary layers:

• Data Layer: Curated, FAIR (Findable, Accessible, Interoperable, Reusable) data repositories containing chemical properties, omics data, life cycle inventories, and exposure information [2]
• Model Layer: Computational models for toxicity prediction, exposure simulation, life cycle impact assessment, and socio-economic analysis
• IOP Graph Layer: Structured knowledge graphs that formalize the relationships between chemical properties, biological events, and broader impacts

FAIR Data Principles and Knowledge Graphs

A cornerstone of the INSIGHT infrastructure is its strict adherence to FAIR data principles, ensuring that all data assets are Findable, Accessible, Interoperable, and Reusable [5] [2]. The project integrates multi-source datasets—including omics data, life cycle inventories, and exposure models—into a structured knowledge graph (KG) that semantically links chemical entities, biological responses, and environmental impacts [2] [3].

This knowledge graph enables:

• Automated reasoning across disparate data sources
• Predictive modeling of impact cascades
• Knowledge discovery through graph mining algorithms
• Transparent traceability of assessment conclusions

The following diagram illustrates the INSIGHT framework's architecture and workflow:

Experimental Validation: Case Studies and Methodologies

Case Study Design and Compound Selection

The INSIGHT framework is being developed and validated through four comprehensive case studies targeting chemically diverse substances with significant regulatory and sustainability relevance [2] [3]:

Table 1: INSIGHT Project Case Studies and Assessment Focus

Case Study	Chemical Class	Primary Assessment Focus	Key Methodologies
PFAS	Per- and polyfluoroalkyl substances	Environmental persistence, bioaccumulation, toxicity	Omics, exposure modeling, hazard assessment
Graphene Oxide (GO)	Engineered nanomaterial	Novel material safety, life cycle impacts	High-throughput screening, NAMs, LCA
Bio-based SAS	Bio-derived synthetic amorphous silica	Renewable material sustainability	Comparative LCA, socio-economic analysis
Antimicrobial Coatings	Functionalized surfaces	Efficacy vs. environmental impact	Multi-scale testing, exposure assessment

These case studies were selected to represent different innovation maturity levels, from early-stage development (e.g., bio-based SAS) to established substances with known concerns (e.g., PFAS) [2]. This diversity ensures that the INSIGHT framework remains applicable across various technology readiness levels and assessment scenarios.

Detailed Methodological Protocols

PFAS Assessment Protocol

The PFAS case study employs an integrated testing strategy that combines in vitro new approach methods (NAMs) with in silico predictions and environmental monitoring data:

• Molecular Characterization:

  • Chemical structure analysis using quantitative structure-activity relationship (QSAR) models
  • Physicochemical property determination (persistence, bioaccumulation potential)

• Toxicological Profiling:

  • High-throughput transcriptomics (RNA-seq) in human primary cell systems
  • Pathway enrichment analysis to identify molecular initiating events
  • Benchmark dose (BMD) modeling for potency assessment

• Environmental Exposure Assessment:

  • Predicted Environmental Concentration (PEC) modeling using INTEGRA framework
  • Bioaccumulation potential in aquatic and terrestrial food webs
  • Species Sensitivity Distribution (SSD) analysis for ecological risk assessment

• Life Cycle Impact Assessment:

  • Cradle-to-gate life cycle inventory (LCI) compilation
  • Environmental Footprint 3.1 (EF3.1) impact category assessment
  • Resource use and emissions accounting across life cycle stages

Graphene Oxide (GO) Assessment Protocol

The graphene oxide assessment focuses on addressing the challenges of novel material evaluation where limited regulatory data exists:

• Material Characterization:

  • Physicochemical properties (size, surface area, functionalization)
  • Material transformations under environmental conditions

• Tiered Testing Strategy:

  • High-throughput screening in alternative test systems
  • Mechanistic toxicology using 3D tissue models and organ-on-chip systems
  • Dosimetric adjustment for in vitro to in vivo extrapolation

• Exposure-Life Cycle Integration:

  • Release estimation during production, use, and disposal phases
  • Environmental fate modeling incorporating material transformations
  • Multi-media exposure assessment (air, water, soil)

Research Reagent Solutions and Essential Materials

Table 2: Key Research Reagents and Computational Tools for IOP Implementation

Category	Specific Tools/Reagents	Function in IOP Assessment
Omics Technologies	RNA-seq kits, mass spectrometry reagents	Comprehensive molecular profiling for mechanistic toxicity assessment
In Vitro Test Systems	Primary human cells, 3D tissue models, organ-on-chip devices	New Approach Methodologies (NAMs) for hazard assessment without animal testing
Computational Models	QSAR tools, physiologically based kinetic (PBK) models, exposure simulators	Prediction of toxicity, biokinetics, and environmental distribution
LCA Databases	Life cycle inventory (LCI) databases, impact assessment methods	Quantification of environmental impacts across life cycle stages
Data Integration Platforms	Knowledge graph frameworks, FAIR data management systems	Structured integration of heterogeneous data sources for IOP construction

Decision-Support Tools and Implementation Framework

Interactive Decision Maps and User Applications

A critical innovation of the INSIGHT project is the development of interactive, web-based decision maps that provide stakeholders with accessible, regulatory-compliant risk and sustainability assessments [2]. These visual analytics tools guide users through the complex decision-making process, aiding them in evaluating the social, economic, health, and environmental impacts of chemicals and materials in a more efficient and comprehensive manner [5].

The decision support system incorporates:

• Weighting mechanisms for balancing different sustainability dimensions
• Sensitivity analysis to identify critical data gaps and uncertainties
• Scenario modeling for comparing alternative chemical designs
• Regulatory alignment checks against existing EU legislation

Regulatory Integration and Compliance Framework

The INSIGHT framework is designed to align with existing EU regulatory requirements while advancing beyond current compliance paradigms [16]. The project systematically evaluates how information generated during SSbD assessment can support legal compliance under various EU regulations, including:

• Classification, Labelling and Packaging (CLP) Regulation
• Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH)
• Sector-specific legislation (cosmetics, biocides, food contact materials)

This reciprocal relationship enables both innovation-friendly regulation and regulation-informed design, creating a virtuous cycle between sustainable chemistry innovation and regulatory compliance [16].

The EU INSIGHT project represents a transformative approach to chemical and material assessment through its pioneering Impact Outcome Pathway framework. By integrating mechanistic toxicology with environmental and socio-economic assessments within a unified computational infrastructure, INSIGHT addresses the critical need for holistic impact assessment methodologies that can support the transition to safer and more sustainable chemicals and materials [5] [2].

The project's ongoing development through rigorous case studies ensures its practical relevance across different substance classes and technology maturity levels [2] [3]. As the framework matures, it promises to democratize access to advanced AI methods for SSbD assessment, supporting industry in optimizing development processes while providing regulators with robust scientific evidence for informed decision-making [5].

By bridging the gap between innovation and regulation, INSIGHT contributes significantly to achieving the ambitions of the European Green Deal and establishing a dynamic, circular economy that respects both human health and environmental integrity [5] [16]. The project's open science approach and commitment to FAIR data principles further ensure that its methodologies and tools will serve as a foundational resource for the next generation of sustainable chemistry innovation.

The pharmaceutical industry faces a critical challenge: it is essential for global health yet is increasingly scrutinized for its substantial environmental footprint. The European Green Deal, the EU's ambitious strategy to become the first climate-neutral continent by 2050, sets a transformative agenda that directly impacts pharmaceutical development and manufacturing practices [17]. This policy framework, coupled with the European Chemical Strategy for Sustainability, demands a fundamental rethinking of how drugs are discovered, developed, and assessed throughout their lifecycle. The traditional fragmented approach to chemical assessment—where health, environmental, social, and economic impacts are evaluated independently—has proven insufficient for capturing the complex trade-offs and synergies necessary for comprehensive decision-making aligned with these sustainability ambitions [2].

The Safe and Sustainable by Design (SSbD) framework emerges as a pivotal methodology to bridge this gap, providing a structured approach for integrating sustainability considerations from the earliest stages of product development. Central to implementing this framework is the novel concept of Impact Outcome Pathways (IOPs), which establish mechanistic links between chemical and material properties and their multi-faceted environmental, health, and socio-economic consequences [2] [4]. This technical guide examines the integration of IOPs within drug discovery pipelines to align pharmaceutical innovation with the sustainability objectives of the European Green Deal, providing researchers and drug development professionals with practical methodologies for implementing this transformative approach.

The IOP Framework: From Concept to Operational Reality

Theoretical Foundations and Definitions

The Impact Outcome Pathway (IOP) framework represents a significant evolution beyond the established Adverse Outcome Pathway (AOP) concept traditionally used in toxicology. While AOPs focus specifically on mapping sequential events leading from molecular initiating events to adverse outcomes in individuals or populations, IOPs adopt a more comprehensive scope that encompasses environmental, health, social, and economic impacts across entire systems [2]. This expanded perspective enables a holistic assessment paradigm essential for evaluating true sustainability.

An IOP can be formally defined as a structured representation that establishes mechanistic cause-effect relationships between the properties of a chemical or material and its broad impacts across multiple domains. This framework systematically integrates disparate data sources—including omics data, life cycle inventories, exposure models, and socio-economic indicators—into a unified knowledge structure that supports predictive modeling and decision-making [2]. The IOP framework operates through three interconnected graph layers that form the core of its computational architecture:

• Data Graph: Organizes multi-source datasets according to FAIR (Findable, Accessible, Interoperable, Reusable) principles, creating a structured knowledge foundation for impact assessment [2] [5].
• Model Graph: Integrates computational models and workflows that simulate chemical behavior, exposure, effects, and socio-economic implications across the chemical lifecycle.
• IOP Graph: Establishes mechanistic connections between elements in the data and model graphs to predict impact cascades across environmental, health, and socio-economic domains [4].

This multi-layer architecture enables researchers to move beyond siloed assessments toward an integrated understanding of how molecular-level decisions propagate through complex systems to ultimately influence sustainability outcomes.

The Computational Architecture of IOPs

The implementation of IOPs within drug discovery requires a robust computational infrastructure that can handle diverse data types and model interactions. The INSIGHT project has pioneered such an architecture through its development of a multi-layer framework specifically designed for mechanistic impact assessment of chemicals and materials [4]. This framework is engineered to support next-generation SSbD applications through systematic interconnection of its core components.

The operationalization of this architecture relies on several technological innovations that ensure its practical applicability in pharmaceutical research settings. Knowledge graphs structured according to semantic web principles provide the backbone for data integration, enabling intelligent querying and relationship mining across disparate data sources [2]. These graphs incorporate diverse data modalities including chemical structures, experimental results from high-throughput screening, omics profiles, physicochemical properties, environmental fate parameters, and socio-economic indicators. The implementation of FAIR data principles throughout this architecture ensures that data assets remain discoverable and reusable across research teams and throughout the drug development lifecycle [2] [18].

Complementing the data layer, computational workflows integrate predictive models ranging from quantitative structure-activity relationships (QSAR) and physiologically based kinetic (PBK) models to exposure scenarios and life cycle impact assessment (LCIA) methods [2]. These workflows are designed to be modular and composable, allowing researchers to construct assessment pathways tailored to specific drug candidates and their associated manufacturing processes. The entire system is accessible through web-based interfaces and application programming interfaces (APIs) that facilitate integration with existing drug discovery informatics platforms [4].

Table 1: Core Components of the IOP Computational Architecture

Component Layer	Key Elements	Function in Pharmaceutical Assessment
Data Graph	Chemical structures, omics data, life cycle inventories, exposure parameters	Centralizes and structures diverse data sources for intelligent querying and relationship mining
Model Graph	QSAR models, PBK models, exposure models, LCIA methods	Provides predictive simulations of drug candidate behavior across biological and environmental systems
IOP Graph	Mechanistic pathways linking molecular events to system-level impacts	Maps cascading effects from molecular interactions to sustainability outcomes
Decision Support	Interactive decision maps, weighting algorithms, visualization tools	Guides researchers through multi-criteria sustainability optimization

Policy Context: The European Green Deal as a Driver for Innovation

Key Policy Elements and Their Implications for Pharma

The European Green Deal represents a comprehensive growth strategy that aims to transform the EU into a fair and prosperous society with a modern, resource-efficient, and competitive economy [17]. Several key policy initiatives under this framework have direct implications for pharmaceutical research and development, creating both imperatives and opportunities for adopting IOP methodologies.

The Chemical Strategy for Sustainability (CSS), as an integral part of the European Green Deal, sets forth ambitions to better protect citizens and the environment against hazardous chemicals while encouraging innovation for safe and sustainable alternatives [4]. For pharmaceutical companies, this translates to increased regulatory emphasis on assessing and minimizing the environmental footprint of drug substances throughout their lifecycle. The CSS specifically promotes the SSbD framework as a cornerstone for achieving these objectives, creating a policy environment conducive to IOP implementation [5].

The Climate Law enshrines the 2050 climate neutrality objective into binding legislation, with an intermediate target of reducing net greenhouse gas emissions by at least 55% by 2030 compared to 1990 levels [17]. This commitment directly impacts pharmaceutical manufacturing operations, requiring comprehensive carbon accounting and reduction strategies that extend throughout the supply chain. IOP methodologies enable companies to model the carbon consequences of process decisions during drug development, facilitating early optimization toward climate-neutral manufacturing.

Additional policy elements including the Zero Pollution Action Plan, Circular Economy Action Plan, and Pharmaceuticals in the Environment Strategy collectively establish a regulatory landscape that demands more holistic environmental assessment of medicinal products [17]. The IOP framework provides the methodological rigor needed to address these intersecting policy priorities within a unified assessment paradigm.

Alignment Mechanisms Between IOPs and Policy Objectives

The integration of IOP methodologies within pharmaceutical R&D creates multiple alignment mechanisms with European Green Deal objectives. These mechanisms operate through both direct compliance pathways and broader innovation enablement.

The most significant alignment mechanism comes through the direct support of SSbD implementation, which is explicitly referenced in both the Chemical Strategy for Sustainability and the Advanced Materials Initiative [4] [5]. By providing a standardized, mechanistic approach to impact assessment, IOPs operationalize the SSbD principles in day-to-day research activities. This enables drug developers to identify potential sustainability concerns early in the development process when design changes are most feasible and cost-effective.

A second alignment mechanism functions through enhanced regulatory preparedness. As policies evolve under the European Green Deal, regulatory requirements for environmental sustainability assessment of pharmaceuticals are expected to become more stringent. Companies that have integrated IOP methodologies into their development workflows will be better positioned to respond to these evolving requirements efficiently. The INSIGHT project specifically notes that its framework is designed to support "regulatory relevance" and "policy development in the areas of neutrality, biodiversity protection, public health, and circular economy" [4].

Table 2: European Green Deal Policy Alignment with IOP Components

Policy Initiative	Primary Sustainability Focus	Relevant IOP Assessment Components
Chemical Strategy for Sustainability	Safe and sustainable chemicals	Mechanistic toxicology pathways, Alternative assessment methods
Climate Law	Climate neutrality	Carbon footprint models, Energy consumption pathways
Circular Economy Action Plan	Resource efficiency, Waste reduction	Material flow analysis, Recyclability assessment, End-of-life impacts
Zero Pollution Action Plan	Air, water, and soil protection	Environmental fate modeling, Emission characterization, Ecotoxicity pathways
Pharmaceuticals in Environment	Aquatic ecosystem protection	Persistence/Bioaccumulation/Toxicity pathways, Water treatment removal efficiency

Methodological Implementation: IOPs in Drug Discovery Workflows

Experimental Design and Protocol Development

Implementing IOPs within pharmaceutical research requires systematic experimental design that captures the multi-scale impacts of drug candidates and their manufacturing processes. The following protocol outlines a comprehensive methodology for IOP-based assessment in early drug discovery.

Phase 1: Compound Characterization and Data Collection

• Structural Analysis: Characterize the molecular structure of the drug candidate and key synthetic intermediates using computational chemistry methods. Determine physicochemical properties including log P, pKa, water solubility, and chemical stability.
• Synthetic Pathway Documentation: Map the complete synthetic route, identifying all starting materials, reagents, catalysts, and solvents. Quantify material and energy inputs at each synthetic step.
• In Silico Toxicity Screening: Apply computational toxicology models to predict potential adverse outcomes, including endocrine disruption, mutagenicity, and ecotoxicity. Use consensus modeling approaches across multiple platforms to increase prediction reliability.
• Environmental Fate Profiling: Predict partitioning behavior (air, water, soil, sediment), persistence (biodegradation half-lives), and bioaccumulation potential using quantitative structure-property relationship (QSPR) models.

Phase 2: Impact Pathway Construction

• Molecular Initiating Event Identification: Determine the initial interactions between the drug substance and biological or environmental systems that may trigger cascading effects.
• Key Event Mapping: Identify and document the sequence of measurable intermediate events between molecular initiation and final outcomes across multiple domains (human health, environmental systems, social well-being).
• Contextual Factor Incorporation: Define the specific conditions under which the impact pathways operate, including geographical, technological, and socio-economic variables that influence outcome severity or probability.
• Evidence Weighting and Uncertainty Analysis: Evaluate the quality and quantity of evidence supporting each pathway relationship and quantify associated uncertainties using probabilistic methods.

Phase 3: Integrated Impact Assessment

• Multi-Scale Modeling: Integrate exposure models, physiologically-based kinetic models, and life cycle impact assessment methods to quantify impacts across spatial and temporal scales.
• Alternative Comparison: Evaluate the drug candidate against established benchmarks or alternative chemical scaffolds using multi-criteria decision analysis.
• Sensitivity Analysis: Identify the parameters and relationships within the IOP that have the greatest influence on overall impact scores to guide further data collection and refinement.
• Iterative Refinement: Update the IOP as new data becomes available through experimental testing or as the drug candidate progresses through development stages.

Research Reagent Solutions for IOP Implementation

The experimental implementation of IOPs requires specific research tools and assessment methodologies. The following table details key solutions essential for constructing and validating impact pathways.

Table 3: Essential Research Reagent Solutions for IOP Development

Reagent Category	Specific Tools/Methods	Function in IOP Development
Computational Toxicology Platforms	OECD QSAR Toolbox, OPERA, VEGA	Prediction of toxicity-related key events and molecular initiating events
Life Cycle Inventory Databases	Ecoinvent, GaBi Databases, EF 3.1	Provision of background data on material and energy flows for manufacturing impact assessment
Exposure Assessment Models	USEtox, RAIDAR, E-FAST	Estimation of environmental and human exposure concentrations under various use scenarios
Bioactivity Screening Assays	High-throughput transcriptomics, Cell painting, ToxCast assays	Experimental determination of molecular initiating events and early key events
Physiologically-Based Kinetic Models	GastroPlus, Simcyp, PK-Sim	Prediction of internal dose metrics from external exposures for translation across species
Omics Data Analysis Platforms	IPA, MetaCore, ArrayTrack	Identification of pathway perturbations and network analysis for key event relationships
Decision Support Systems	SuperDecisions, Calibrate	Multi-criteria decision analysis for weighting and comparing impacts across domains

Case Studies and Experimental Validation

Pharmaceutical Case Study: Antimicrobial Compounds

The application of IOPs to antimicrobial drug development demonstrates the framework's utility in addressing critical sustainability challenges in pharmaceuticals. Antimicrobial compounds represent a particularly relevant case due to their essential medical function coupled with significant environmental concerns, including the potential for driving antimicrobial resistance and their persistence in water systems.

In this case study, researchers applied the IOP framework to compare traditional fluoroquinolone antibiotics with novel non-fluorinated analogs under development. The assessment integrated environmental fate modeling to predict aquatic concentrations, mechanistic toxicology data to assess potential resistance development, and life cycle assessment to evaluate manufacturing impacts [2]. The resulting IOP mapped connections between molecular properties (including fluorine content), metabolic stability, resistance gene induction, environmental persistence, and human health outcomes.

The experimental protocol for this assessment included:

• Resistance Induction Potential: Measurement of minimum inhibitory concentration (MIC) shifts in pathogenic bacteria after repeated sub-lethal exposure using standardized broth microdilution methods.
• Environmental Persistence: Laboratory-based biodegradation testing in water-sediment systems according to OECD Test Guideline 308, with quantification of parent compound and major metabolites.
• Treatment Process Removal: Bench-scale simulation of wastewater treatment processes to determine removal efficiencies across different treatment technologies.
• Life Cycle Inventory Development: Detailed accounting of material and energy inputs for synthetic routes, including solvent consumption, catalyst use, and purification requirements.

The IOP analysis revealed that while the novel non-fluorinated analogs showed reduced environmental persistence and lower potential for resistance development, they required more complex synthetic routes with higher energy demands. This trade-off between direct environmental impacts and manufacturing impacts highlighted the value of the integrated IOP perspective in guiding sustainable molecular design.

Implementation Framework: Decision Mapping for Sustainable Design

A critical output of IOP implementation is the development of interactive decision maps that guide researchers through the complex trade-offs inherent in sustainable pharmaceutical design. The INSIGHT project specifically references such decision-support tools as essential components of their framework [4]. These maps translate complex IOP networks into actionable guidance for drug development professionals.

The decision mapping process follows a structured workflow that begins with impact quantification across multiple domains, proceeds through normalization and weighting based on sustainability priorities, and culminates in visualization of the decision space. The following diagram illustrates this workflow using the standardized color palette and sufficient contrast ratios for accessibility compliance [19] [20]:

Diagram 1: IOP Decision Mapping Workflow

This structured approach to decision support enables pharmaceutical researchers to navigate the complex multi-dimensional trade-offs between efficacy, safety, environmental impact, and socio-economic considerations. The implementation of such decision maps within the INSIGHT framework is specifically designed to make advanced assessment methodologies accessible to stakeholders across the drug development ecosystem [4].

The Scientist's Toolkit: Practical Implementation Guide

Computational Implementation and Data Management

Successful implementation of IOP methodologies requires careful attention to computational infrastructure and data management practices. The FAIR principles (Findable, Accessible, Interoperable, Reusable) provide a foundational framework for organizing the diverse data streams required for IOP construction [2] [18]. Pharmaceutical organizations should establish dedicated data curation pipelines that transform raw experimental results into structured, annotated datasets suitable for IOP modeling.

Specific implementation recommendations include:

• Semantic Data Modeling: Develop ontologies that formally define entities and relationships specific to pharmaceutical impact assessment, enabling knowledge graph construction and reasoning.
• Model Interoperability Standards: Implement standardized application programming interfaces (APIs) to enable data exchange between specialized modeling platforms (e.g., QSAR tools, LCIA software, exposure models).
• Uncertainty Propagation Methods: Employ probabilistic modeling techniques that quantitatively track and propagate uncertainties through IOP networks, from parameter uncertainties to model uncertainties.
• Version Control and Provenance Tracking: Implement robust versioning systems for both IOP models and underlying datasets to ensure reproducibility and auditability of assessment results.

The adoption of Quality by Digital Design (QbDD) principles supports IOP implementation by providing a structured framework for digital transformation in pharmaceutical development [18]. QbDD emphasizes model-driven experimental approaches, structured workflows, and FAIR data management—all essential elements for robust IOP construction. The integration of QbDD and IOP methodologies creates a powerful combination for embedding sustainability considerations throughout the drug development process.

Organizational Integration and Skill Development

Beyond technical implementation, successful IOP adoption requires organizational commitment and development of specialized expertise. Pharmaceutical companies should establish cross-functional Sustainability Assessment Teams with representation from medicinal chemistry, process chemistry, toxicology, environmental sciences, and regulatory affairs. These teams provide the diverse perspectives needed to construct comprehensive IOPs and interpret their implications for compound selection and process design.

Critical competency development areas include:

• Systems Thinking: Training researchers to recognize and map complex causal relationships across traditional disciplinary boundaries.
• Computational Toxicology: Building expertise in predictive toxicology methods and their application to early-stage risk assessment.
• Life Cycle Assessment: Developing capabilities in environmental footprinting methods and interpretation of LCIA results.
• Multi-Criteria Decision Analysis: Providing skills in formal methods for comparing alternatives across multiple, often conflicting, sustainability objectives.

Organizations should also establish clear governance procedures for IOP application in decision-making, including criteria for when IOP assessments are required in the development pipeline, standards for model quality and validation, and protocols for translating IOP results into design modifications. This institutionalization of the IOP methodology ensures consistent application and continuous improvement of assessment practices over time.

The integration of Impact Outcome Pathways within drug discovery represents a transformative approach to aligning pharmaceutical innovation with the sustainability ambitions of the European Green Deal. By providing a mechanistic, computational framework for predicting multi-scale impacts of drug candidates and their manufacturing processes, IOPs enable researchers to make informed decisions that balance therapeutic innovation with environmental responsibility and social benefit. The structured methodology outlined in this technical guide—from fundamental concepts through experimental protocols to implementation frameworks—provides researchers and drug development professionals with a practical roadmap for adopting this emerging paradigm.

As the pharmaceutical industry confronts increasing pressure to address its environmental footprint while continuing to deliver essential medicines, the IOP framework offers a scientifically rigorous approach for navigating this complex landscape. The ongoing development of computational infrastructure, standardized assessment methods, and decision-support tools through initiatives like the INSIGHT project will further enhance the practical application of IOPs in day-to-day drug discovery workflows [4] [5]. Through continued refinement and adoption of these methodologies, the pharmaceutical research community can play a pivotal role in advancing the transition toward a climate-neutral, circular economy envisioned by the European Green Deal.

From Theory to Practice: Implementing IOPs in the Drug Development Workflow

The assessment of chemicals and materials, including pharmaceuticals, has traditionally been fragmented, with health, environmental, social, and economic impacts evaluated independently. This disjointed approach limits the ability to capture trade-offs and synergies necessary for comprehensive decision-making under the Safe and Sustainable by Design (SSbD) framework [2]. The novel Impact Outcome Pathway (IOP) framework addresses this critical challenge by establishing mechanistic links between chemical and material properties and their multi-faceted consequences throughout the drug development lifecycle [2] [4]. Unlike the more narrowly focused Adverse Outcome Pathway (AOP) concept, IOPs provide a comprehensive structure that integrates environmental, health, socio-economic, and lifecycle considerations into a unified assessment model.

This technical guide outlines a systematic, tiered approach for implementing IOPs across all stages of drug development—from early discovery through late-stage development. The framework enables researchers and drug development professionals to proactively identify and mitigate potential adverse impacts while optimizing for sustainability outcomes. By embedding IOPs throughout the development process, organizations can align with the European Green Deal, the EU's Chemical Strategy for Sustainability, and global sustainability goals while fostering safer, more sustainable innovation in pharmaceutical development [2] [4].

Core Components of the IOP Framework

The IOP framework consists of three systematically interlinked graphs that work in concert to support predictive impact assessment [4]:

• Data Graph: Integrates multi-source datasets including omics data, life cycle inventories, and exposure models into a structured knowledge graph that adheres to FAIR principles (Findable, Accessible, Interoperable, Reusable) [2]. This component serves as the foundational data layer for all subsequent analyses.

• Model Graph: Comprises computational models and workflows that translate data into predictive insights. This includes quantitative structure-activity relationships (QSAR), physiologically based kinetic (PBK) models, exposure models, and life cycle assessment (LCA) calculators [2].

• IOP Graph: Establishes mechanistic cause-effect chains linking molecular initiating events to outcomes across multiple impact domains. This extends the AOP concept by incorporating positive and negative outcomes beyond traditional toxicological endpoints [2].

These components are operationalized through a computational platform that supports multi-model simulations, decision-support tools, and artificial intelligence-driven knowledge extraction, significantly enhancing the predictability and interpretability of chemical and material impacts throughout the development lifecycle [2].

Figure 1: Integrated IOP Framework Architecture

Tiered Implementation Across Drug Development Stages

Stage 1: Early Discovery and Candidate Screening

During early discovery, the primary objective is to identify promising lead compounds while eliminating those with potential safety or sustainability concerns. The IOP framework facilitates this through computational prioritization and high-throughput screening approaches.

Experimental Protocol 1: Computational Toxicity and Sustainability Profiling

• Compound Characterization: Determine molecular structures, physicochemical properties (LogP, pKa, molecular weight), and structural features of lead candidates.

• In Silico Hazard Assessment: Employ QSAR models and read-across approaches to predict key toxicity endpoints, including mutagenicity, hepatotoxicity, and cardiotoxicity.

• Environmental Profile Screening: Apply predictive models for bioaccumulation (BCF), persistence (P), and aquatic toxicity to identify environmentally problematic structures.

• IOP Network Development: Map predicted molecular initiating events to potential adverse outcomes using existing AOP knowledge, and extend to include resource consumption and waste generation projections.

• Priority Ranking: Integrate multi-criteria decision analysis to rank compounds based on balanced safety and sustainability profiles.

Table 1: Key In Silico Tools for Early-Stage IOP Implementation

Tool Category	Specific Tools/Methods	Key Output Parameters	Application in IOP
QSAR Models	OECD QSAR Toolbox, TEST	Predicted LC50/EC50, mutagenicity probability	Hazard identification for IOP initiation
Physicochemical Property Predictors	ChemAxon, ACD/Labs	LogP, pKa, water solubility, melting point	Bioavailability & environmental fate estimation
Toxicity Predictors	ProTox, LAZAR	LD50, organ toxicity, endocrine disruption	Key Event identification in IOPs
Environmental Fate Models	EPI Suite	BCF, degradation half-life	Environmental persistence assessment
Multi-criteria Decision Tools	SimaPro, TEAM	Weighted sustainability scores	Compound ranking & selection

Stage 2: Preclinical Development

In preclinical development, the focus shifts to experimental validation of computational predictions and detailed characterization of lead candidates. IOPs guide targeted testing strategies that maximize information gain while minimizing animal use through Non-Animal Methods (NAMs) [2].

Experimental Protocol 2: Tiered In Vitro Testing for IOP Elucidation

• Molecular Initiating Event (MIE) Confirmation:

  • Method: High-content screening in relevant cell lines
  • Endpoint: Protein binding, receptor activation, genomic signature
  • Duration: 24-72 hours exposure

• Cellular Key Event Characterization:

  • Method: 3D spheroid/microtissue models with multi-parameter endpoints
  • Endpoint: Mitochondrial dysfunction, oxidative stress, cytotoxicity
  • Duration: 7-28 days repeated exposure

• Organ-level Response Assessment:

  • Method: Organ-on-a-chip systems (liver, kidney, cardiovascular)
  • Endpoint: Functional impairment, biomarker release, metabolic competence
  • Duration: 14-28 days with repeated dosing

• Environmental Impact Testing:

  • Method: Algal, daphnid, and fish embryo toxicity tests
  • Endpoint: Growth inhibition, mobility, developmental effects
  • Duration: 24-96 hours depending on test system

Figure 2: Preclinical IOP Testing Workflow

Stage 3: Clinical Development

Clinical development represents a critical phase for validating human-relevant IOPs and incorporating socio-economic considerations. The IOP framework integrates with ICH E6 Good Clinical Practice guidelines to ensure ethical trial conduct and reliable results while collecting sustainability-relevant data [21].

Experimental Protocol 3: Clinical IOP Biomarker Validation

• Biomarker Selection and Qualification:

  • Identify mechanism-based biomarkers aligned with IOP key events
  • Establish assay sensitivity, specificity, and reproducibility
  • Define biologically significant change thresholds

• Controlled Clinical Trial Integration:

  • Incorporate biomarker assessment into Phase I/II trial protocols
  • Implement dense sampling strategies for pharmacokinetic-pharmacodynamic (PK-PD) modeling
  • Collect patient-reported outcomes aligned with IOP outcomes

• Environmental Burden Assessment:

  • Quantize API emissions from clinical trial participants
  • Model wastewater treatment plant removal efficiency
  • Predict environmental concentrations (PEC) in relevant compartments

• Socio-economic Impact Evaluation:

  • Apply Social Life Cycle Assessment (S-LCA) methodologies
  • Assess patient burden, healthcare system impacts, and accessibility
  • Evaluate alignment with Sustainable Development Goals (SDGs)

Table 2: Clinical IOP Assessment Parameters and Methods

Assessment Domain	Key Parameters	Data Collection Methods	IOP Integration
Human Health Impact	Biomarker levels, adverse event incidence, organ function	Clinical lab tests, medical imaging, PK sampling	Confirmation of human-relevant key events
Environmental Burden	API excretion rate, metabolic profile, PEC	Mass balance analysis, wastewater monitoring	Environmental IOP activation potential
Socio-economic Impact	Patient quality of life, treatment burden, cost-effectiveness	EQ-5D, SF-36, cost-consequence analysis	Positive and negative outcome quantification
Life Cycle Impacts	Carbon footprint, water consumption, resource depletion	LCA databases, process-based modeling	Comprehensive sustainability profile

Stage 4: Late-Stage Development and Regulatory Submission

At this stage, the focus shifts to comprehensive impact assessment and regulatory alignment. The tiered IOP approach facilitates preparation of integrated safety and sustainability dossiers that meet evolving regulatory expectations.

Experimental Protocol 4: Integrated Impact Assessment for Regulatory Submissions

• IOP Network Finalization:

  • Compile evidence from all previous tiers into unified IOP networks
  • Quantitate response-response relationships using benchmark dose (BMD) modeling
  • Establish quantitative uncertainties for each key relationship

• Life Cycle Impact Assessment (LCIA):

  • Conduct cradle-to-gate LCA for API and formulation manufacturing
  • Calculate characterization factors for human toxicity, ecotoxicity, and resource use
  • Integrate with clinical use and end-of-life disposal scenarios

• Risk Characterization Ratio (RCR) Calculation:

  • Derive environmental and human exposure estimates
  • Compare exposure levels to effect thresholds
  • Calculate RCRs for multiple endpoints and scenarios

• Benefit-Risk-Sustainability Integration:

  • Develop multi-criteria decision framework
  • Quantitatively integrate therapeutic benefit with safety and sustainability impacts
  • Create interactive decision maps for stakeholder communication [4]

Figure 3: Late-Stage Integrated Assessment Process

The Scientist's Toolkit: Essential Research Reagent Solutions

Successful implementation of the tiered IOP approach requires specialized reagents and materials that enable comprehensive characterization across biological and environmental systems.

Table 3: Essential Research Reagents for IOP Implementation

Reagent Category	Specific Examples	Function in IOP Assessment
Mechanistic Toxicity Assays	High-content screening kits, oxidative stress probes, mitochondrial function assays	Quantification of key cellular events in IOP pathways
Biomarker Detection Kits	Multiplex cytokine panels, metabolomics kits, DNA damage markers	Validation of IOP key events in preclinical and clinical studies
Environmental Bioassay Systems	Algal growth inhibition kits, daphnia mobility assays, fish embryo tests	Assessment of environmental impact outcomes
Omics Profiling Platforms	RNA-seq kits, targeted metabolomics panels, proteomic profiling	Systems-level understanding of IOP networks
Advanced Cell Culture Models	3D spheroid kits, organ-on-chip systems, co-culture inserts	Human-relevant toxicity testing without animal use
Analytical Standards	Stable isotope-labeled APIs, metabolite standards, degradation products	Mass balance and environmental fate studies

The tiered implementation of Impact Outcome Pathways from early discovery to late-stage development represents a paradigm shift in how we approach drug development. This integrated framework moves beyond traditional siloed assessments to provide a comprehensive, mechanistic understanding of the complex interplay between therapeutic benefits, human health impacts, environmental consequences, and socio-economic considerations. By embedding IOPs throughout the development process, organizations can make more informed decisions that balance efficacy, safety, and sustainability while meeting evolving regulatory expectations. The structured approach outlined in this guide—with its specific experimental protocols, assessment methodologies, and specialized research tools—provides a practical roadmap for researchers and drug development professionals to operationalize this transformative framework.

In the evolving landscape of sustainable scientific research, robust data management has become a cornerstone for cultivating transparency, reproducibility, and innovation [22]. Within the context of an Impact Outcome Pathway (IOP) Safe Sustainable by Design (SSbD) framework, the challenge lies not only in generating data but in structuring it to explicitly demonstrate causal pathways, contextual mechanisms, and attributable outcomes of interventions [23]. This is where the integration of the FAIR (Findable, Accessible, Interoperable, Reusable) principles and Knowledge Graphs (KGs) provides a transformative foundation.

The FAIR principles, a guideline to improve the reusability of data, ensure that digital assets are systematically described and readily discoverable and usable by both humans and computers [24]. However, properly implementing these principles is challenging; in the physical sciences, for instance, only about one in ten researchers share FAIR data alongside their published articles [22]. Knowledge Graphs, which are semantically rich graphs of data consisting of described entities and relations integrated from different sources, serve as the perfect technological substrate for realizing FAIR data in practice [25]. They integrate heterogeneous data in a way that makes relationships and meaning machine-computable.

This technical guide details how the confluence of FAIR data and Knowledge Graphs creates a powerful, structured data environment for constructing and analyzing Impact Outcome Pathways. This foundation is critical for moving beyond "black box" evaluations—which merely show that a change occurred—to models that can illuminate how and why an intervention leads to specific outcomes within a complex system, thereby directly supporting the goals of safe and sustainable design [23].

The FAIR Data Principles: A Foundation for Reusable IOPs

The FAIR principles provide a structured framework to enhance the reusability of data, which is paramount for building reliable and analysable IOPs [24].

The Four Principles Explained

• Findable: Metadata and data should be easy to find for both humans and computers. This is the first step toward reusability. Essential practices include assigning persistent identifiers (e.g., DOIs) to both data and metadata, and richly describing data with metadata to enable powerful discovery.
• Accessible: Users need to know how data and metadata can be accessed, potentially involving authentication and authorization procedures. It is critical to note that FAIR is often confused with "open data," but they are not synonymous. FAIR data can be accessible under well-defined conditions, which is particularly important for handling sensitive data, such as in health research, under privacy regulations like GDPR [24].
• Interoperable: Data must be able to be integrated with other data and must interoperate with applications or workflows for analysis, storage, and processing. This is achieved through the use of standardized, formal languages, vocabularies, and ontologies to represent knowledge.
• Reusable: Metadata and data should be so well-described that they can be replicated and/or combined in different settings. This requires accurate, rich, and multi-attribute provenance (how the data was generated) and licensing information.

Quantitative Insights into FAIR Implementation

Data from IOP Publishing's analysis of over thirty thousand articles reveals both the challenges and progress in adopting FAIR data practices [22]. As shown in Table 1, while there is growing engagement with data sharing, full FAIR compliance remains a significant hurdle.

Table 1: FAIR Data Sharing Trends in Physical Sciences (Based on IOP Publishing Data) [22]

Metric	12 Months Pre-Policy	12 Months Post-Policy	Change
Articles declaring FAIR data sharing	8%	11%	+3%
Articles with data freely available (any route)	50%	64%	+14%

Barriers and Recommendations for FAIRification

Implementing FAIR principles is non-trivial. A systematic study in child and adolescent mental health research identified 45 unique barriers [24]. The most frequently assigned characteristics to these barriers were:

• Type: External (e.g., organizational policy preventing the use of required software).
• Category: Tooling (i.e., a lack of necessary software and databases).
• Scope: The vast majority of barriers (43 out of 45) were not specific to mental health, indicating their relevance across scientific domains [24].

The top recommendations to overcome these barriers, validated via the Delphi method, are crucial for any team embarking on FAIRification for IOPs:

• Add a FAIR data steward to the research team.
• Develop and provide accessible step-by-step guides.
• Ensure sustainable funding for both the implementation and long-term maintenance of FAIR data [24].

Knowledge Graphs: The Structural Backbone for IOP Data

A Knowledge Graph is a graph of data consisting of semantically described entities and relations of different types that are integrated from different sources [25]. Entities have unique identifiers, and the graph's structure is often semantically described by an ontology, which defines the concepts, relationships, and rules governing the domain [25].

The Role of KGs in Data Integration and Analysis

KGs excel at physically integrating heterogeneous data from diverse sources—unstructured text, semi-structured data, and structured databases—into a new, unified graph-like representation [25]. Their schema-flexible nature allows them to easily accommodate and interlink new types of information without the need for a rigid, pre-defined schema, which is a limitation of traditional data warehouses. This makes them ideal for the evolving and interconnected nature of IOP research, where new relationships and data types are constantly being discovered.

KGs are at the center of numerous advanced applications, including:

• Recommender systems and question-answering platforms.
• Organizing information for fast-emerging global topics (e.g., pandemics, natural disasters).
• Providing labeled training data for machine learning and supporting the development of knowledge-driven AI approaches [25].
• Enhancing Large Language Models (LLMs) by improving factual correctness and explanations, thereby promoting the quality and interpretability of AI decision-making [25].

Key Requirements for KG Construction

Constructing and maintaining high-quality KGs for dynamic domains like IOP research requires addressing several key requirements [25]:

• Incrementality: The ability to continuously and efficiently incorporate new information without full re-computation.
• Automation: Pipelines must be automated as much as possible to handle large data volumes.
• Scalability: The system must handle ever-increasing amounts of data.
• Data Quality and Provenance: Mechanisms to ensure the reliability of the KG's information and track its origin.
• Interoperability: Seamlessly integrating diverse data sources and formats.

An Integrated Methodology: Constructing FAIR IOP Knowledge Graphs

This section provides a detailed experimental protocol for building a KG that embodies FAIR principles to represent and analyze Impact Outcome Pathways.

Phase 1: Ontology and Data Model Design

The ontology is the semantic core of the KG, defining the "vocabulary" for your IOPs.

• Objective: To define a formal ontology that conceptualizes the key entities and relationships within the IOP-SSbD framework.
• Protocol:
  • Identify Core Classes: Define the primary entity types. At a minimum, this includes Intervention, Outcome, Impact, Stakeholder, Context, Indicator, and Evidence.
  • Define Object Properties: Establish the semantic relationships between classes. For example:
    • interventionLeadsTo (connects an Intervention to an Outcome)
    • outcomeContributesTo (connects an Outcome to an Impact)
    • hasStakeholder (links any entity to a Stakeholder)
    • isMeasuredBy (links an Outcome to an Indicator)
    • isSupportedBy (links a relationship to an Evidence source)
  • Specify Data Properties: Define the attributes for each class (e.g., Intervention has a name, description, startDate).
  • Leverage Existing Ontologies: Re-use and extend terms from established ontologies (e.g., Schema.org, OBO Foundry ontologies) to enhance interoperability.
• Output: A formal ontology document, typically written in OWL (Web Ontology Language).

Phase 2: FAIR Data Ingestion and Curation

This phase involves gathering and preparing the data that will populate the KG.

• Objective: To ingest heterogeneous data from multiple sources and transform it into a structured, FAIR-aligned format.
• Protocol:
  • Data Source Identification: Catalog all relevant data sources (e.g., experimental datasets, literature, stakeholder interviews, regulatory documents).
  • Assign Persistent Identifiers (PID): Ensure each unique entity (e.g., a specific chemical, a study) is assigned a PID, fulfilling the Findable principle.
  • Data Extraction and Mapping: Use automated tools (e.g., NLP for text, ETL scripts for databases) to extract data and map it to the ontology classes and properties defined in Phase 1. This directly supports Interoperability.
  • Entity Resolution: Implement algorithms to disambiguate and merge records that refer to the same real-world entity (e.g., "Compound X" and "X-Chem" are the same).
  • Provenance Annotation: For every piece of ingested data, record its source, processing history, and license, which is critical for the Reusable principle.
• Output: A set of clean, structured RDF (Resource Description Framework) triples or property graph records ready for loading.

Phase 3: KG Population and Quality Assurance

Here, the curated data is loaded into a graph database and validated.

• Objective: To create the operational KG and ensure its quality and logical consistency.
• Protocol:
  • Triple Loading: Load the RDF triples into a graph database (e.g., Ontotext GraphDB, Stardog, Neo4j).
  • Logical Consistency Checking: Use a reasoner (e.g., an OWL reasoner like Pellet or HermiT) to infer new knowledge and check for logical contradictions within the KG.
  • Quality Rule Validation: Execute a set of SHACL (Shapes Constraint Language) or SPARQL queries to validate data quality rules (e.g., "every Intervention must have at least one hasStakeholder link").
• Output: A validated, queryable KG.

Table 2: Essential Research Reagent Solutions for KG Construction

Tool Category	Example Solutions	Function
Ontology Development	Protégé, WebVOWL	To visually design, create, and manage the domain ontology.
Data Processing/NLP	spaCy, OpenNLP, KNIME	To extract structured information from unstructured text (e.g., scientific literature).
Entity Resolution	Dedupe, Silk Framework, LIMES	To find and link records that represent the same entity across different data sources.
Graph Database	GraphDB, Stardog, Neo4j, Apache Jena Fuseki	To store, manage, and query the Knowledge Graph.
Reasoner & Validator	Pellet, HermiT, SHACL	To infer new knowledge and validate the consistency and quality of the KG.

Workflow Visualization

The following diagram, generated from the DOT script below, illustrates the integrated methodology for constructing a FAIR IOP Knowledge Graph, showing the sequence and dependencies of the key phases.

Visualizing Impact Pathways in a Knowledge Graph

A core strength of using a KG for IOPs is the ability to visually map and interrogate the complex causal pathways from intervention to impact. The following diagram illustrates a simplified, yet semantically rich, example of an IOP embedded within a KG, integrating different types of entities and relationships.

This visualization demonstrates how a KG moves beyond a simple linear pathway. It explicitly represents different entity types (interventions, outcomes, impacts, stakeholders) and the evidence supporting the causal links, thereby opening the "black box" of impact evaluation [23]. This structured representation allows researchers to ask complex queries, such as "Show all evidence supporting outcomes that contribute to 'Sustainable Process'", which are directly enabled by the underlying FAIR data and semantic structure.

The integration of the FAIR principles with Knowledge Graph technology provides a robust, scalable, and semantically rich data foundation for the construction and analysis of Impact Outcome Pathways. This approach directly addresses the pressing need in research to demonstrate not just if an intervention worked, but how it worked, for whom, and in what context—a core tenet of the Safe Sustainable by Design framework. By making data Findable, Accessible, Interoperable, and Reusable, and by structuring it within a dynamic and queryable Knowledge Graph, researchers and drug development professionals can transition from disconnected data silos to an interconnected web of evidence. This empowers more rigorous impact evaluation, fosters data-driven decision-making, and ultimately accelerates the development of truly safe and sustainable innovations.

Incorporating Non-Animal Methods (NAMs) for Mechanistic, Predictive Insights

The pursuit of mechanistic, predictive insights in toxicology and drug development is undergoing a fundamental transformation, moving away from traditional animal models and towards a new generation of New Approach Methodologies (NAMs). This shift is driven by both ethical imperatives and a compelling scientific need: the recognition that animal models often fail to accurately predict human biological responses. With over 90% of drug candidates that enter human trials ultimately failing—many due to safety or efficacy issues not detected in animal studies—the scientific and economic costs of this translational gap are immense [26]. NAMs encompass a broad range of human biology-based tools, including in vitro systems like organoids and organs-on-chips, in silico computational models, and artificial intelligence (AI)-driven biosimulation platforms. These technologies are increasingly being integrated into sophisticated frameworks for safety and efficacy assessment, most notably within the Impact Outcome Pathway (IOP) paradigm, which extends the Adverse Outcome Pathway (AOP) concept to include environmental, health, and socio-economic consequences for a comprehensive Safe and Sustainable by Design (SSbD) assessment [3].

The regulatory landscape is rapidly evolving to support this transition. The U.S. Food and Drug Administration (FDA) has published a roadmap to reduce reliance on animal testing in preclinical safety studies, and the European Medicines Agency (EMA) has created pathways for the qualification of NAMs [26]. In 2025, the UK government launched a strategy explicitly aimed at "phasing out the use of animals in science," establishing a new UK Centre for the Validation of Alternative Methods (UKCVAM) to accelerate the regulatory acceptance of these methods [27]. Furthermore, initiatives like the Validation and Qualification Network (VQN), a partnership between regulators, Big Pharma, and Contract Research Organizations (CROs), are working to push validated alternative methods "across the regulatory finish line" [28]. This confluence of scientific innovation and regulatory support marks a pivotal moment for researchers and drug development professionals to adopt NAMs for more predictive, human-relevant mechanistic insights.

The Scientific and Regulatory Framework for NAMs

Core Principles: From AOP to IOP and SSbD

The theoretical foundation for modern safety assessment is built upon the Adverse Outcome Pathway (AOP) framework, which describes a sequential chain of causally linked events from a molecular initiating event to an adverse outcome at the organism or population level. NAMs are exceptionally well-suited for interrogating these pathways, particularly the early key events, in a human-relevant context [3]. The INSIGHT project, an EU-funded initiative, has advanced this concept further by developing the Impact Outcome Pathway (IOP) framework. The IOP establishes mechanistic links not only between chemical properties and health impacts but also extends to environmental, social, and economic consequences, thereby enabling a truly integrated assessment under the Safe and Sustainable by Design (SSbD) paradigm mandated by the European Green Deal and the Chemical Strategy for Sustainability [4] [5] [3].

The IOP framework operates through a multi-layered computational architecture that integrates:

• A Data Graph comprising multi-source datasets (omics, life cycle inventories, exposure models) curated under FAIR principles (Findable, Accessible, Interoperable, Reusable).
• A Model Graph containing computational models and workflows for predicting impacts.
• An IOP Graph that mechanistically links the data and models to forecast health, environmental, social, and economic outcomes [4] [3].

This integrated structure allows researchers to move beyond siloed assessments and capture the complex trade-offs and synergies necessary for comprehensive decision-making on chemicals and materials.

The Evolving Regulatory Landscape for NAMs

Globally, regulatory bodies are creating pathways for the adoption of NAMs, signaling a definitive shift in regulatory science. The following table summarizes key recent regulatory developments and their implications for researchers.

Table 1: Key Regulatory Developments Supporting NAM Adoption (2024-2025)

Regulatory Body	Initiative/Action	Key Implications for Researchers
U.S. FDA	Publication of a "Roadmap to Reducing Animal Testing in Preclinical Safety Studies" and launch of the "New Alternative Methods Program" [29] [30].	Outlines a phased plan for use of NAMs; provides clarity on contexts where animal studies can be streamlined or replaced [30].
National Institutes of Health (NIH)	Announcement that it will no longer solicit research proposals based exclusively on animal-only models; establishment of the Office of Research Innovation, Validation, and Application (ORIVA) [29].	Encourages grant applications that incorporate human-relevant models and computational approaches.
UK Government	Publication of the strategy "Replacing animals in science," committing to phase out animal testing and establish a UK Centre for the Validation of Alternative Methods (UKCVAM) [27].	Aims to create a national infrastructure for validating NAMs, accelerating their uptake into regulatory and discovery research.
European Commission (via INSIGHT Project)	Development of an integrated computational framework for SSbD assessment based on the IOP concept, aligned with the European Green Deal [4] [3].	Provides a standardized, scalable framework for assessing chemicals and materials, promoting the use of NAM-generated data.
Validation and Qualification Network (VQN)	A public-private partnership including FDA, European Commission, Sanofi, Novo Nordisk, GSK, and Charles River Labs [28].	Aims to accelerate the regulatory qualification of well-validated NAMs, providing a clearer path for regulatory submission.

For drug developers, the FDA's Center for Drug Evaluation and Research (CDER) has explicitly stated that it is open to streamlined nonclinical programs that use NAMs or reduce animal use across multiple contexts, including safety pharmacology, general toxicity, carcinogenicity, and developmental and reproductive toxicity (DART) [30]. For instance, an appropriately qualified in silico proarrhythmia risk model can be used to assess the risk of Torsades de Pointes, and a Weight-of-Evidence (WoE) assessment incorporating in vitro and in silico data may replace a two-year rat carcinogenicity study in certain circumstances [30].

A Technical Guide to Key NAMs Platforms and Their Applications

Advanced In Vitro Models: From Organoids to Organ-on-a-Chip

Advanced in vitro systems have evolved from simple 2D cell cultures to complex, physiologically relevant 3D models that better mimic human tissue and organ biology.

• Organoids: These are self-organizing 3D microtissues derived from stem cells (either pluripotent or adult) that recapitulate key structural and functional aspects of human organs. Recent breakthroughs include the development of mini-brains with functional blood vessels, enabling more accurate study of neurodrug penetration and toxicity [28]. Similarly, kidney organoids that survived for 34 weeks in culture have been used to model kidney disease and test drug toxicity, revealing, for example, that the adeno-associated virus AAV2 causes significant kidney cell damage via the NFκB pathway—a finding missed in animal studies [28]. The protocol for generating such organoids typically involves directing stem cell differentiation through a specific sequence of growth factors and culture conditions to mimic embryonic organ development. For brain organoids, this involves using inhibitors of SMAD signaling to induce neural ectoderm, followed by patterning factors to generate specific brain regions.

• Organs-on-Chips (Microphysiological Systems): These are microfluidic devices that culture living human cells in channels that mimic the structure and dynamic mechanical forces of human organs. For example, a "heart-in-a-jar" model uses human pluripotent stem cell-derived cardiomyocytes in a system that replicates human-specific cardiac function (~70 beats per minute), providing a more predictive platform for cardiotoxicity screening than rodent models (~600 bpm) [28]. Advanced systems also include multi-organ chips that link different tissue compartments (e.g., gut-liver, gut-brain) to study systemic drug effects and nutrient metabolism, offering insights into complex inter-organ interactions that traditional models cannot replicate [28]. The experimental workflow for using a multi-organ chip typically involves: 1) Seeding different organ-specific cell types into distinct compartments of the chip. 2) Establishing perfusion of a common culture medium to mimic blood flow. 3) Dosing a test compound into the system (e.g., via a "gut" compartment). 4) Sampling the medium over time to analyze metabolite formation, organ-specific toxicity biomarkers, and functional endpoints (e.g., beating frequency for heart models).

In Silico and AI-Driven Biosimulation Platforms

Computational NAMs leverage big data and AI to simulate human biology and predict outcomes, offering high scalability and the ability to model human diversity.

• Mechanistic Biosimulation: These platforms combine AI with mechanistic biological modeling to simulate how a drug interacts with the human body. Companies like VeriSIM Life use such platforms to integrate molecular, chemical, and physiological data to predict human pharmacokinetics and pharmacodynamics, drug-induced liver injury, and cardiovascular side effects [29]. The accuracy of these platforms for well-defined tasks like toxicity and ADME (Absorption, Distribution, Metabolism, and Excretion) prediction often exceeds 80%, representing a significant improvement over animal models [29].

• Impact Outcome Pathway (IOP) Frameworks: As implemented in the INSIGHT project, these frameworks use a knowledge graph and AI-driven data extraction to automate the assembly of IOPs for chemicals and materials. Researchers can use this to model the holistic impact of a substance, from its molecular properties to its potential socio-economic consequences [3]. The workflow involves: 1) Data Curation: Gathering and standardizing relevant data (chemical properties, omics data, exposure data, etc.) into a FAIR-compliant knowledge graph. 2) Model Linking: Connecting relevant computational models (e.g., for toxicity, environmental fate, life cycle assessment) to the data points. 3) IOP Simulation & Prediction: Running simulations to traverse the IOP graph and predict a range of impacts. 4) Decision Support: Using interactive decision maps to visualize outcomes and trade-offs, aiding in SSbD choices [4] [3].

The following diagram illustrates the core data and modeling architecture that powers an integrated IOP framework.

Diagram 1: IOP Framework Architecture

High-Throughput Screening and Omics Technologies

Cellular assays and omics technologies form the backbone of high-throughput screening in NAMs. The cellular assay segment is a dominant method in the non-animal testing market, anticipated to reach USD 2.2 billion by 2032 [31]. These assays allow for precise measurement of cellular responses (proliferation, differentiation, apoptosis) to thousands of compounds. When combined with omics technologies (genomics, transcriptomics, proteomics, metabolomics), they enable deep mechanistic insights into the pathways affected by a compound. For instance, transcriptomic analysis of human liver spheroids treated with a drug can reveal gene expression signatures predictive of drug-induced liver injury long before overt toxicity is observed, providing a powerful human-relevant hazard identification tool.

Quantitative Performance and Market Validation

The adoption of NAMs is supported by a growing body of data on their predictive performance and a rapidly expanding market. The global non-animal alternative testing market, valued at approximately USD 1.8 billion in 2023, is projected to grow at a compound annual growth rate (CAGR) of 11.9% to reach USD 4.8 billion by 2032 [31]. This growth is fueled by technological advances, increasing bans on animal testing, and growing investment from pharmaceutical companies seeking more efficient and predictive tools.

Table 2: Non-Animal Alternative Testing Market Analysis by Segment (2023-2032)

Segment	2023 Market Size / Share	Projected 2032 Value / Growth	Key Drivers and Applications
By Product (Cell Lines)	USD 1.2 Billion [31]	Dominant Segment	Versatility in mimicking physiology; ideal for high-throughput screening; consistent and reproducible results.
By Technology (Cell Culture)	49.4% Market Share [31]	Dominant Segment	Provides highly relevant human biological models; supports scalable production for modern drug discovery.
By Application (Oncology)	Not Specified	USD 1.4 Billion [31]	Complexity of cancer biology requires sophisticated models (e.g., organ-on-chip, 3D tumor cultures) for therapy response and resistance studies.
By End Use (Pharmaceutical Companies)	Not Specified	USD 1.6 Billion [31]	Cost-effectiveness versus animal studies; more relevant biological models for evaluating therapeutic efficacy in humans.

The accuracy of these methods is continually improving. As noted by Dr. Jo Varshney of VeriSIM Life, AI-driven platforms for specific endpoints like liver toxicity or target binding can achieve accuracy levels above 80%, outperforming traditional animal models [29]. Furthermore, real-world regulatory successes are accumulating. For example, CN Bio's multi-organ MPS (Microphysiological System) is being evaluated in an expanded collaboration with the FDA, signaling growing regulatory confidence in these platforms [31].

The Scientist's Toolkit: Essential Reagents and Platforms

Implementing NAMs requires a suite of specialized reagents, tools, and platforms. The following table details key solutions essential for establishing these methodologies in a research setting.

Table 3: Research Reagent Solutions for NAMs Implementation

Tool/Category	Example Product/Platform	Function and Research Application
Defined Synthetic Matrices	NexaGel [26]	A tunable, synthetic hydrogel for 3D cell culture. Provides a defined, reproducible environment for growing organoids and spheroids, overcoming batch variability of animal-derived matrices like Matrigel.
Integrated Microtissue Models	MatTek Corp. (Sartorius) models [26]	Pre-validated 3D in vitro models (e.g., skin, airway, liver) for toxicity testing. These are ready-to-use and can be integrated into workflows with high-content imaging and analysis.
Organ-on-a-Chip Systems	AlveoliX AX12C iAlv [31], Emulate, CN Bio PhysioMimix [31]	Microfluidic devices that emulate human organ physiology. Used for disease modeling, toxicity testing, and absorption studies with high human relevance. The AX12C iAlv, for instance, features a primary human alveolar epithelial cell line on a chip.
Stem Cell & Differentiation Kits	Commercially available iPSC lines and differentiation kits	Provide the foundational cellular material for generating patient-specific organoids and tissue models. Critical for studying genetic diseases and personalized drug responses.
Biosimulation Software	VeriSIM Life BIO-AI Platform [29], INSIGHT IOP Platform [3]	AI and mechanistic model-driven software to predict human and environmental outcomes for drugs or chemicals. Used for de-risking candidate selection and optimizing dosing before clinical trials.
Multi-Omics Analysis Suites	Various commercial platforms for transcriptomics, proteomics, etc.	Enable deep molecular profiling of responses in NAMs, uncovering mechanistic biomarkers of efficacy and toxicity that bridge in vitro findings to potential human outcomes.

Integrated Workflow for SSbD Assessment Using an IOP Framework

To achieve SSbD for a new chemical or material, researchers can follow a structured workflow within an IOP framework like that developed by the INSIGHT project. The process, visualized below, is iterative and allows for continuous optimization of the product's design.

Diagram 2: SSbD Assessment Workflow

• Define Molecular & Material Properties: This initial step involves a comprehensive characterization of the chemical or material's intrinsic properties (e.g., structure, reactivity, solubility).
• NAM Testing Battery: A suite of NAMs is deployed. This may include:
  • In vitro assays: High-throughput cellular assays for cytotoxicity, genomic stability, and specific pathway activation (e.g., stress response pathways).
  • Organ-on-a-chip models: To assess organ-specific toxicity and barrier function.
  • In silico predictions: Using QSAR models and read-across from similar substances to predict hazards, and molecular docking to understand target interactions [32].
• IOP Assembly & Impact Simulation: Data from the NAMs battery is fed into the IOP framework. The system automatically or semi-automatically constructs the pathways, linking molecular initiating events to adverse outcomes and broader impacts (environmental, socio-economic). Multi-model simulations are run to predict the full spectrum of impacts.
• Decision Analysis via Interactive Maps: The results are visualized in interactive, web-based decision maps. These maps allow researchers and regulators to clearly see trade-offs—for example, between a material's performance and its potential environmental toxicity—and make informed SSbD choices [4] [3].
• Redesign & Iterate: If the impact assessment reveals unacceptable risks, the feedback loop is critical. The design of the chemical or material is modified, and the workflow is repeated until an optimal, safe, and sustainable profile is achieved.

The incorporation of Non-Animal Methods for mechanistic, predictive insights represents the frontier of modern safety science and drug development. The convergence of sophisticated in vitro models, powerful in silico and AI platforms, and integrative frameworks like IOP provides an unprecedented opportunity to move beyond the limitations of animal models. This transition is supported by a clear regulatory momentum and a robust economic trajectory. For researchers and drug developers, mastering these tools is no longer a niche specialty but a core competency for driving innovation in the development of safer, more effective therapeutics and sustainable chemicals. The future lies in a human-relevant, data-driven, and ethically aligned approach to scientific discovery, and the tools to build that future are now at hand.

The transition towards a Safe and Sustainable by Design (SSbD) paradigm for chemicals and materials necessitates a fundamental shift from fragmented assessment approaches to integrated, mechanistic frameworks. Traditionally, health, environmental, social, and economic impacts have been evaluated independently, limiting the ability to capture critical trade-offs and synergies necessary for comprehensive decision-making [2]. The Impact Outcome Pathway (IOP) framework emerges as a novel computational structure designed to overcome this challenge. IOPs extend the Adverse Outcome Pathway (AOP) concept by establishing mechanistic links between the properties of chemicals and materials and their broader environmental, health, and socio-economic consequences [2] [5]. This whitepaper provides a technical guide for researchers and drug development professionals on the tools and methodologies for integrating Life Cycle Assessment (LCA) and chemical Risk Assessment within IOPs, a core requirement for implementing a robust SSbD strategy in the pharmaceutical sector and beyond.

The Conceptual IOP Framework: Bridging LCA and Risk Assessment

The IOP framework serves as the central scaffold for unifying disparate assessment tools. It functions as a multi-layered graph architecture that systematically links data, computational models, and impact pathways.

Core Components of the IOP Framework

The integrated framework, as developed in the EU INSIGHT project, consists of three interconnected graphs [4] [5]:

• Data Graph: A structured knowledge graph that integrates multi-source datasets—including omics data, life cycle inventories (LCIs), and exposure models—adhering to FAIR principles (Findable, Accessible, Interoperable, Reusable) [2].
• Model Graph: A repository of curated computational models for fate, exposure, toxicology, life cycle impact, and socio-economic analysis.
• IOP Graph: The core element that defines the mechanistic sequences linking a chemical's properties through to its ultimate impacts on human health, ecosystems, and society.

Table 1: Core Components of the Integrated IOP Framework

Component Layer	Primary Function	Key Inputs/Outputs
Data Graph	Centralizes and structures diverse data for interoperability	Omics data, LCIs, exposure parameters, physicochemical properties
Model Graph	Hosts computational workflows for impact simulation	Fate & exposure models, PBK models, LCIA methods, socio-economic models
IOP Graph	Defines mechanistic cause-effect chains from source to impact	Impact Outcome Pathways (IOPs) linking chemical properties to endpoints

The logical flow from a chemical's intrinsic properties through to its ultimate sustainability impacts is visualized in the following diagram, which synthesizes the core concepts of the IOP framework.

Methodologies for Integrating LCA and Risk Assessment

Quantitative Data Integration and Modeling

The integration of LCA and Risk Assessment within IOPs relies on coupling specific quantitative data and models to enable a holistic impact evaluation. The table below summarizes the key data requirements and their roles in the integrated assessment.

Table 2: Key Data Inputs for Integrated LCA and Risk Assessment within IOPs

Data Category	Specific Data Inputs	Role in LCA	Role in Risk Assessment
Physicochemical Properties	Log Kow, pKa, vapor pressure, water solubility, molecular weight	Inform fate modeling and exposure in Life Cycle Impact Assessment (LCIA)	Determine bioaccumulation, environmental distribution, and bioavailability
Life Cycle Inventory (LCI)	Resource/energy inputs, emissions to air/water/soil per unit process	Core input for calculating environmental footprints across the life cycle	Provides release data for estimating Predicted Environmental Concentrations (PECs)
Toxicity & Effects Data	In vitro bioactivity, omics data, in vivo toxicity endpoints (e.g., BMD)	Used in LCIA methods for human toxicity and ecotoxicity characterization	Direct input for hazard identification, dose-response assessment, and deriving Points of Departure (PODs)
Exposure Parameters	Population data, intake rates, environmental media concentrations	Used in LCIA to assess intake fraction and human exposure	Used to estimate daily intake or external exposure for Risk Characterization Ratio (RCR) calculation

Experimental and Computational Protocols

A robust integrated assessment follows a structured workflow, combining experimental data with computational modeling.

Protocol 1: Tiered Integrated Assessment Workflow

• Problem Formulation & Scoping:

  • Define the goal, system boundaries (cradle-to-grave for LCA), and the risk assessment questions (e.g., patient exposure, environmental release).
  • Identify the primary mode of action and relevant regulatory endpoints, especially critical for drug-device combination products [33].

• Life Cycle Inventory (LCI) Compilation:

  • Data Collection: Gather primary data on material/energy inputs, API synthesis (including solvents, catalysts), and emissions from all unit processes. For pharmaceuticals, this includes upstream (precursor synthesis) and downstream (use, disposal) phases [34].
  • Data Gap Filling: Use proxies, economic input-output LCA, or Quantitative Structure-Activity Relationship (QSAR) models to fill data gaps, particularly for novel materials or complex supply chains.

• Integrated Exposure & Fate Modeling:

  • Input LCI emission data into multimedia fate and exposure models (e.g., USEtox, INTEGRA) to derive Predicted Environmental Concentrations (PECs) and human intake fractions [2].
  • For human health risk assessment, use Physiologically Based Kinetic (PBK) models to translate external exposure to internal dose.

• Mechanistic Hazard Assessment:

  • Employ Non-Animal Methods (NAMs) such as high-throughput in vitro assays and transcriptomics (RNA-seq) to generate mechanistic toxicity data [2].
  • Map these key events to existing Adverse Outcome Pathways (AOPs) or develop new IOPs to link molecular initiating events to adverse outcomes.

• Impact Characterization & Integration:

  • LCA: Calculate midpoint (e.g., climate change, human toxicity) and endpoint impacts (e.g., human health damage, ecosystem damage) using Life Cycle Impact Assessment (LCIA) methods.
  • Risk Assessment: Calculate Risk Characterization Ratios (RCRs) by comparing exposure metrics (PEC, daily intake) with hazard metrics (Predicted No-Effect Concentration, benchmark dose).

• Interpretation & Decision Support:

  • Synthesize LCA and risk metrics using interactive decision maps to visualize trade-offs (e.g., a chemical with low life cycle energy use but high human health risk) [4].
  • Update risk management files and life cycle assessment based on post-market surveillance and new information, as per an integrated lifecycle management process [33].

The following diagram details this multi-step experimental and computational workflow, highlighting the points of integration between LCA and Risk Assessment.

The Scientist's Toolkit: Essential Reagents and Computational Solutions

Implementing an integrated LCA and Risk Assessment approach requires a combination of wet-lab reagents, computational tools, and curated databases.

Table 3: Key Research Reagent Solutions and Computational Tools for Integrated Assessment

Tool/Category	Specific Examples	Function in Integrated Assessment
In Vitro Bioactivity Assays	High-throughput cytotoxicity panels, receptor-binding assays, stress response pathway reporters	Generate mechanistic toxicity data for IOP construction and serve as NAMs for hazard assessment.
Omics Reagents & Platforms	RNA-seq kits, mass spectrometry for proteomics/metabolomics	Uncover molecular initiating events and key events in IOPs, providing a evidence for chemical grouping and read-across.
QSAR & Read-Across Tools	OECD QSAR Toolbox, OPERA, EPI Suite	Predict physicochemical properties, environmental fate parameters, and toxicity endpoints for data-poor chemicals.
LCA Databases & Software	Ecoinvent database, Sphera LCA, OpenLCA	Provide background LCI data for upstream/downstream processes and enable calculation of life cycle environmental impacts.
Fate & Exposure Models	USEtox, USEtox2, INTEGRA, E-FAST	Translate LCI emission data into human and ecosystem exposure estimates for both LCIA and chemical risk assessment.
Integrated Platforms	INSIGHT computational framework	Provide a unified environment for managing the data graph, model graph, and IOP graph for SSbD assessment [4].

The integration of Life Cycle Assessment and Risk Assessment within the Impact Outcome Pathway framework represents a paradigm shift in the evaluation of chemicals and materials. By moving beyond siloed assessments, this integrated approach provides a mechanistic, data-driven, and holistic foundation for true Safe and Sustainable by Design innovation. For researchers and drug development professionals, mastering the tools and methodologies outlined in this guide—from leveraging NAMs and computational models to interpreting outputs via decision-support systems—is critical for navigating the complexities of modern therapeutic development. The successful implementation of this framework, supported by initiatives like the EU INSIGHT project, holds the promise of driving the pharmaceutical industry and the wider chemical sector towards a more digital, circular, and sustainable economy.

The assessment of chemicals and materials has traditionally been fragmented, with health, environmental, social, and economic impacts evaluated independently. This disjointed approach limits the ability to capture trade-offs and synergies necessary for comprehensive decision-making under the Safe and Sustainable by Design (SSbD) framework [2]. The EU INSIGHT project addresses this fundamental challenge by developing a novel computational framework for integrated impact assessment based on the Impact Outcome Pathway (IOP) approach [2] [4] [5]. This framework represents a paradigm shift from a fragmented situation to a holistic and integrated approach for assessing the sustainability and safety of chemicals and materials [5].

The IOP framework extends the Adverse Outcome Pathway (AOP) concept by establishing mechanistic links not only between chemical and material properties and their environmental and health consequences, but also incorporating their socio-economic dimensions [2]. By bridging mechanistic toxicology, exposure modeling, life cycle assessment, and socio-economic analysis, IOPs provide a scalable, transparent, and data-driven approach to SSbD that aligns with the European Green Deal and global sustainability goals [2] [4].

Core Architecture of the IOP Framework

Multi-Layer Framework Components

The INSIGHT framework employs a multi-layered architecture that systematically integrates diverse data types and models to support comprehensive impact assessments [4] [5]. This architecture consists of three systematically interlinked graphs:

• Data Graph: Organizes multi-source datasets into a structured knowledge graph (KG), ensuring FAIR (Findable, Accessible, Interoperable, Reusable) data principles are met [2]. This graph integrates diverse data sources including omics data, life cycle inventories, and exposure models [2].

• Model Graph: Provides curated and user-friendly computational models and workflows that support the development of next-generation SSbD chemicals and materials [4] [5]. This includes multi-model simulations that enhance the predictability and interpretability of chemical and material impacts [2].

• IOP Graph: Establishes mechanistic links between chemical and material properties and their environmental, health, and socio-economic consequences, extending beyond traditional AOP frameworks [2] [4].

Table 1: Core Components of the IOP Multi-Layer Framework

Framework Layer	Primary Function	Data/Model Types	Integration Mechanism
Data Graph	Structured knowledge organization	Omics data, life cycle inventories, exposure models	FAIR principles implementation
Model Graph	Computational simulation & prediction	QSAR, PBK, LCIA, exposure models	Multi-model workflow integration
IOP Graph	Mechanistic impact assessment	Health, environmental, socio-economic pathways	Impact Outcome Pathway linkages

Visualizing the IOP Framework Architecture

The following diagram illustrates the integrated architecture of the IOP framework and the flow of information between its core components:

Implementation Methodologies for IOP Development

Data Integration and Knowledge Graph Construction

The development of a robust IOP framework begins with the construction of a comprehensive knowledge graph that adheres to FAIR data principles [2]. This process involves:

• Multi-Source Data Collection: Curating diverse datasets including omics data (transcriptomics, proteomics), life cycle inventories (LCIs), exposure model outputs, and socio-economic indicators [2]. The data graph systematically organizes these heterogeneous datasets to ensure interoperability and reusability.

• Data Harmonization Protocol: Implementing standardized vocabularies and ontologies to enable seamless integration across disparate data sources. This includes normalizing data formats, establishing common units of measurement, and implementing quality control checks to ensure data integrity.

• Knowledge Graph Population: Transforming structured and semi-structured data into a connected graph representation where nodes represent entities (chemicals, biological effects, environmental compartments) and edges represent relationships between them (causation, correlation, association).

The INSIGHT project validates this approach through four case studies targeting per- and polyfluoroalkyl substances (PFAS), graphene oxide (GO), bio-based synthetic amorphous silica (SAS), and antimicrobial coatings [2]. These case studies demonstrate how multi-model simulations and AI-driven knowledge extraction can enhance the predictability and interpretability of chemical and material impacts [2].

IOP Construction Workflow

The methodological workflow for constructing validated Impact Outcome Pathways involves sequential phases of data integration, model development, and computational simulation:

Computational Modeling and Simulation Approaches

The model graph component employs diverse computational approaches to simulate impacts across the IOP framework:

• Mechanistic Toxicology Modeling: Utilizing Quantitative Structure-Activity Relationships (QSAR), Physiologically Based Kinetic (PBK) models, and Next-Generation Risk Assessment (NGRA) approaches to predict molecular initiating events and key events in adverse outcome pathways [2].

• Exposure and Fate Modeling: Implementing environmental footprint models (EF3.1) and predicted environmental concentrations (PEC) calculations to simulate chemical behavior across various environmental compartments [2].

• Life Cycle Assessment: Conducting comprehensive environmental (LCA), economic (LCC), and social (S-LCA) impact assessments to evaluate sustainability metrics across the entire life cycle of chemicals and materials [2].

• Multi-Model Integration: Developing interoperable workflows that connect disparate modeling approaches to enable comprehensive impact simulations that span from molecular initiating events to broad socio-economic consequences.

Table 2: Computational Modeling Approaches in the IOP Framework

Model Category	Specific Methods	Output Metrics	Regulatory Relevance
Mechanistic Toxicology	QSAR, PBK, BMD Analysis	Point of Departure (PoD), Key Events (KE)	NGRA, IATA, PARC
Exposure Modeling	INTEGRA, PEC Calculations	Risk Characterization Ratio (RCR)	Environmental Footprint 3.1
Life Cycle Assessment	LCA, LCC, S-LCA	Impact Scores, Externalities	EU Green Deal, CSS
Socio-Economic Analysis	Cost-Benefit Analysis, Multi-Criteria Decision Analysis	Net Benefits, Preference Rankings	Policy Development

Interactive Decision-Support System Implementation

Architecture of the Decision-Support System

The INSIGHT project develops an integrated decision-support system in the form of interactive decision maps [4]. These maps are multi-level workflows designed for guided decision-making by industrial and regulatory stakeholders and are adapted to multiple types of SSbD use cases [4]. The system architecture incorporates:

• Similarity-Based Retrieval Algorithms: Implementing models that generate similarity scores between chemical profiles or material characteristics based on their associated properties and potential impacts [35]. This approach enables the identification of analogous cases from historical data to inform current assessments.

• Interactive Visualization Interfaces: Developing web-based, responsive visualization systems that enable stakeholders to explore, analyze, and compare similar chemical assessments using comprehensive interaction techniques [35]. These interfaces are designed to reduce cognitive burden through intuitive visual representations.

• Real-Time Impact Simulation: Providing capabilities to simulate the potential health, environmental, and socio-economic impacts of chemicals and materials under different usage scenarios and application contexts.

Implementation of Interactive Decision Maps

The decision-support system employs specific technical approaches to enable effective stakeholder interaction:

• Web-Based Implementation: Hosting interactive tools on accessible web platforms using modern JavaScript frameworks (e.g., D3.js) for dynamic visualization capabilities [35]. This ensures broad accessibility across stakeholder groups without specialized software requirements.

• Query Specification and Results Caching: Implementing efficient query mechanisms with AJAX request handling and cached-result strategies to ensure responsive performance even with large underlying datasets [35]. The system retrieves similar case data based on query parameters and applies lookups to categorical variable repositories.

• Multi-Dimensional Data Representation: Visualizing high-density, high-dimensional continuous and categorical data through coordinated multiple views including detailed attribute comparison sections, Cartesian coordinate plots for similarity visualization, and collections of small multiple graphs for temporal data trends [35].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents and Computational Tools for IOP Development

Reagent/Tool Category	Specific Examples	Function in IOP Research	Application Context
Omics Analysis Reagents	RNA-seq kits, Proteomic assay panels	Profiling molecular responses to chemical exposure	Identification of Key Events in pathways
Bioanalytical Standards	Certified reference materials, Internal standards	Quality control for analytical measurements	Quantifying chemical concentrations in bioassays
Cell-Based Assay Systems	Reporter gene assays, High-content screening kits	Measuring specific biological activities	In vitro assessment of Molecular Initiating Events
Computational Toxicology Tools	QSAR models, PBK modeling software	Predicting chemical properties and biological effects	In silico hazard assessment without animal testing
Life Cycle Inventory Databases	Ecoinvent, ELCD, Specific industry data	Providing background data for environmental footprint	Life Cycle Impact Assessment (LCIA)
Socio-Economic Indicators	OECD well-being frameworks, Social LCA databases	Assessing social and economic impacts	Social LCA (S-LCA) and Life Cycle Costing (LCC)

Case Studies and Experimental Validation

Protocol for IOP Case Study Implementation

The INSIGHT framework is being developed and validated through four targeted case studies focusing on per- and polyfluoroalkyl substances (PFAS), graphene oxide (GO), bio-based synthetic amorphous silica (SAS), and antimicrobial coatings [2]. The experimental protocol for these case studies involves:

• Problem Scoping and System Boundaries: Defining the assessment scope, including the chemical or material system to be evaluated, the application contexts, and the spatial and temporal boundaries for the analysis.

• Data Collection and Curation: Gathering relevant data on chemical properties, use patterns, release scenarios, and potential exposure pathways from existing literature, experimental studies, and monitoring data.

• IOP Construction and Computational Simulation: Developing specific IOPs for each case study substance, identifying molecular initiating events, key events, and adverse outcomes across biological organization levels, and integrating these with environmental fate, exposure, and socio-economic models.

• Impact Assessment and Validation: Running simulated impact assessments using the integrated IOP framework and validating predictions against experimental data or empirical observations where available.

• Decision-Support Tool Application: Applying the interactive decision maps to explore alternative scenarios, assess trade-offs between safety and sustainability objectives, and identify potential intervention points for risk management or sustainability optimization.

Quantitative Assessment Metrics

The case studies employ standardized metrics to enable comparative assessment across different chemicals and materials:

Table 4: Quantitative Metrics for IOP Case Study Assessment

Assessment Dimension	Key Performance Indicators	Measurement Methods	Benchmark Values
Human Health Impact	Point of Departure (PoD), Margin of Exposure	Benchmark Dose (BMD) analysis, NGRA	Regulatory thresholds (e.g., EFSA)
Environmental Impact	Predicted Environmental Concentration (PEC), Risk Characterization Ratio (RCR)	Fate and transport modeling, Species Sensitivity Distribution (SSD)	PEC/PNEC ratios < 1
Climate Change Impact	Global Warming Potential (GWP)	Life Cycle Impact Assessment (LCIA)	EU Climate Neutrality targets
Resource Efficiency	Cumulative Energy Demand, Abiotic Resource Depletion	Life Cycle Inventory analysis	Circular economy indicators
Socio-Economic Impact	Life Cycle Costing, Employment indicators, Social LCA metrics	Economic modeling, Social impact assessment	Cost-benefit analysis metrics

The IOP framework represents a transformative approach to chemical and material assessment that moves beyond traditional fragmented methods to an integrated, mechanistic, and computationally advanced paradigm. By establishing systematic links between data graphs, model graphs, and impact outcome pathways, the framework enables comprehensive evaluation of health, environmental, social, and economic impacts under the SSbD concept. The interactive decision-support system implementation provides stakeholders with accessible tools for navigating complex trade-offs and synergies in chemical and material development. As demonstrated through targeted case studies, this approach supports the transition toward safer, more sustainable innovation in chemicals and materials in alignment with the European Green Deal and global sustainability goals.

Navigating Challenges: Strategies for Effective IOP Implementation

The Safe and Sustainable by Design (SSbD) framework, as championed by the European Commission, is a voluntary approach designed to guide the innovation process for chemicals and materials, aiming to minimize their impact on health, climate, and the environment throughout their lifecycle [15]. Within this framework, the Impact Outcome Pathway (IOP) concept has emerged as a critical tool. The IOP approach extends the Adverse Outcome Pathway (AOP) concept by establishing mechanistic links not only to environmental and health consequences but also to socio-economic outcomes, thereby providing a holistic view of a substance's impact [2]. However, a fundamental challenge in applying this integrated assessment early in the development process is the prevalence of data gaps—missing, incomplete, or outdated information that creates uncertainty in the assessment [36].

These data gaps are particularly problematic in early-stage research and development, where comprehensive experimental data is often unavailable. Left unaddressed, they can lead to inaccurate impact assessments, impaired decision-making, and significant reputational risks in reporting [36]. This guide outlines a systematic strategy for identifying, evaluating, and mitigating data gaps within the IOP/SSbD paradigm, enabling researchers and drug development professionals to make informed, confident decisions even with limited data.

A Systematic Framework for Identifying and Classifying Data Gaps

The first step in managing data gaps is their systematic identification. This process should be integrated into the early stages of the IOP development. The following table summarizes the primary types of data gaps encountered in SSbD assessments and their typical causes [36].

Table 1: Common Data Gap Types and Their Sources in Early-Stage Assessment

Data Gap Category	Manifestation	Common Root Causes
Physico-Chemical Properties (PCFs)	Missing data on molecular stability, solubility, or surface chemistry for novel compounds.	Substance is newly synthesized; measurement techniques are complex or costly.
Hazard & Toxicity Data	Lack of in vitro or in vivo toxicity endpoints; unknown mechanisms of action.	Reliance on traditional animal testing is time-consuming and ethically challenging; New Approach Methodologies (NAMs) are not yet fully established.
Environmental Fate & Exposure	Unknown degradation pathways; estimated rather than measured exposure concentrations.	Complex environmental simulation models required; long-term fate studies are impractical in early stages.
Life Cycle Inventory (LCI) Data	Use of generic or proxy data for energy and material inputs in production.	Opaque, global supply chains; supplier data is confidential or non-existent [36].
Socio-Economic Impact Data	Difficulty in quantifying broader societal costs and benefits.	Methodologies for social Life Cycle Assessment (S-LCA) are less mature; data is inherently qualitative.

A crucial practice for identifying gaps is to review supplier and process data to pinpoint where assumptions have replaced actual measurements [36]. Furthermore, one must actively scan for geographical and temporal mismatches, such as using a European energy mix dataset for a process intended for Southeast Asia, or relying on background data that is several years old and no longer reflects current technologies or regulations [36]. Finally, it is essential to critically examine system boundaries to ensure that upstream (e.g., raw material extraction) and downstream (e.g., end-of-life) processes have not been omitted, thereby hiding significant data gaps [36].

The following diagram illustrates the logical workflow for the systematic identification and initial analysis of data gaps within an IOP-based assessment.

Methodologies for Filling Data Gaps and Mitigating Uncertainty

Once data gaps are identified and prioritized, a suite of methodologies can be deployed to address them. The choice of method depends on the nature of the gap, available resources, and the required level of confidence. The following table compares the primary strategies for filling data gaps in the context of SSbD and IOP development [36].

Table 2: Strategies for Filling Data Gaps in SSbD-IOP Assessments

Strategy	Description	Best Use Cases	Key Limitations
Primary Data Collection	Gathering site- or process-specific data directly from experiments or suppliers.	Filling critical gaps for foreground processes; validating high-impact assumptions.	Can be time-consuming and costly; not always feasible for all data points in early stages.
Use of Proxy Data	Employing data from a similar substance, process, or technology as a stand-in.	When molecular structures or processes are analogous; for preliminary screening.	Introduces uncertainty if the analogy is weak; requires careful documentation of assumptions.
LCA Database Leveraging	Using commercial or open-source life cycle inventory databases (e.g., ecoinvent, Sphera).	Providing background data for common materials, energy, and transport processes.	Data may be generic, lack regional specificity, or be outdated [36].
Data Estimation Techniques	Using engineering models, stoichiometry, or expert elicitation to calculate missing values.	Estimating material/energy flows based on production volumes or chemical principles.	Reliability depends on the soundness of the model and input parameters.
Computational & NAMs	Applying QSAR models, read-across, or in vitro high-throughput screening.	Predicting physicochemical properties, toxicity, and environmental fate early in design.	Models have applicability domains; require experimental validation for regulatory acceptance.
Sensitivity & Uncertainty Analysis	Quantifying how variation in input data affects the final assessment results.	Identifying which data gaps are most critical and should be prioritized for refinement.	Does not fill the gap itself, but guides resource allocation for data collection.

The Role of Sensitivity Analysis and Expert Judgment

A pivotal step after applying gap-filling strategies is to conduct a sensitivity analysis. This process tests how much the overall results of the IOP assessment (e.g., a risk characterization ratio or an environmental impact score) change in response to variations in the filled data [36]. If the results are robust—that is, they do not change significantly—then the gap may be considered adequately addressed for the current decision context. If the results are highly sensitive to the filled data, this flags a critical area requiring more robust data collection or a higher degree of caution in interpretation.

Underpinning all these strategies is the need for transparent documentation. Every assumption, chosen proxy, estimation technique, and data source must be meticulously recorded. This transparency is not merely academic; it protects the credibility of the assessment and builds trust with stakeholders, even when the underlying data is imperfect [36].

Experimental Protocols for Key Data Generation in IOPs

To ground the aforementioned strategies, here are detailed experimental protocols for generating key data points within an IOP, aligned with the SSbD framework's assessment phases [15] [14].

Protocol 1: High-Throughput Screening for Hazard Identification

This protocol leverages New Approach Methodologies (NAMs) to efficiently generate early hazard data.

• 1. Objective: To rapidly assess the potential cytotoxic and inflammatory effects of a novel chemical entity using in vitro models.
• 2. Experimental Workflow:
  • Cell Culture: Maintain relevant cell lines (e.g., THP-1 monocytes differentiated into macrophages, HepG2 hepatocytes) in appropriate media.
  • Compound Preparation: Serially dilute the test compound in DMSO or culture medium to create a concentration range (e.g., 1 µM - 100 µM). Include a vehicle control.
  • Exposure: Seed cells in 96-well plates and expose to the test concentrations for 24 and 48 hours. Include positive controls for cytotoxicity (e.g., Triton X-100) and inflammation (e.g., LPS).
  • Endpoint Measurement:
    • Cytotoxicity: Perform MTT or Alamar Blue assay to measure cell viability.
    • Inflammatory Response: Quantify release of pro-inflammatory cytokines (e.g., IL-1β, TNF-α) via ELISA from cell culture supernatant.
  • Data Analysis: Calculate IC50 values for cytotoxicity and EC50 values for cytokine induction. These quantitative endpoints serve as Key Events in the IOP.

The workflow for this high-throughput screening protocol is visualized below.

Protocol 2: Material Flow Analysis for Life Cycle Inventory (LCI) Creation

This protocol provides a structured approach to collecting primary data for life cycle assessment, a core component of the SSbD framework [15] [36].

• 1. Objective: To create a gate-to-gate Life Cycle Inventory for a laboratory-scale synthesis process, identifying mass and energy flows.
• 2. Experimental Workflow:
  • Define System Boundaries: Clearly state the unit processes included (e.g., reaction, purification, drying).
  • Data Collection Plan: Identify all input streams (raw materials, solvents, catalysts) and output streams (product, by-products, waste).
  • Primary Data Measurement:
    • Mass Flows: Precisely weigh all input materials and output products/waste using an analytical balance.
    • Energy Consumption: Use a power meter to measure electricity consumption of key equipment (stirrers, heaters, pumps) over the duration of the process.
    • Solvent Loss: Estimate solvent loss to evaporation by measuring the mass of the reaction vessel before and after the process (if no condensation recovery is used).
  • Data Calculation & Allocation: Calculate the total mass and energy inputs per unit mass of final product. If multiple products are formed, apply a mass-based allocation.

The Scientist's Toolkit: Key Research Reagent Solutions

Successfully implementing the protocols and strategies above requires a set of essential tools and reagents. The following table details key solutions for research in this field [14].

Table 3: Essential Research Reagent Solutions for IOP and SSbD Data Generation

Tool / Reagent	Function in SSbD-IOP Research
Relevant In Vitro Cell Lines (e.g., THP-1, HepG2, HaCaT)	Models for simulating human toxicity Key Events, reducing reliance on animal data and enabling high-throughput screening.
Biomarkers & ELISA Kits (e.g., for IL-1β, TNF-α, Caspase-3)	Quantifying specific cellular responses (e.g., inflammation, apoptosis) to populate IOP key events with quantitative data.
Computational Toxicology Software (e.g., for QSAR, read-across)	Predicting physicochemical properties and toxicological endpoints for data gap filling and prioritization of experiments.
Life Cycle Assessment (LCA) Software (e.g., OpenLCA, SimaPro)	Modeling the environmental impact of chemicals and materials across their life cycle, a core pillar of the SSbD assessment phase [15].
Digital Twin Platforms	Creating virtual models of a material or process to explore SSbD solutions and predict performance, safety, and sustainability outcomes [14].

Navigating data gaps is not an obstacle to be feared but a fundamental aspect of responsible innovation within the SSbD and IOP framework. By adopting a systematic approach—rooted in rigorous gap identification, the strategic application of gap-filling methodologies, and transparent documentation—researchers and drug developers can generate reliable, decision-grade data even in the earliest stages of development. This proactive management of uncertainty is the cornerstone of truly designing safer and more sustainable chemicals and materials, ultimately accelerating the transition to a circular and non-toxic economy as envisioned by the European Green Deal.

Within the context of Impact Outcome Pathway (IOP) and Safe and Sustainable by Design (SSbD) framework research, the integration of traditional safety metrics with broader sustainability indicators presents a significant scientific challenge. The current landscape is characterized by a proliferation of disparate frameworks and reporting standards, leading to a lack of comparability, significant reporting burdens, and difficulties in assessing the true sustainability profile of chemical entities and processes [37]. For researchers and drug development professionals, this fragmentation obscures the interconnections between molecular safety, environmental impact, and social responsibility, ultimately hindering the development of truly sustainable therapeutics. The harmonization of these metrics is not merely an administrative exercise but a fundamental scientific endeavor critical for steering innovation towards chemicals and materials that are intrinsically safe and sustainable throughout their lifecycle [38]. This guide provides a technical roadmap for overcoming these integration hurdles, offering detailed methodologies and visualization tools to align safety and sustainability assessments within a coherent IOP/SSbD research paradigm.

Methodological Framework for Metric Integration

The integration of safety and sustainability metrics requires a systematic approach that aligns with the core principles of the SSbD framework. The following section outlines the experimental and analytical protocols for achieving this harmonization.

Core Alignment Strategy

The alignment process begins with identifying shared objectives between Environmental, Health, and Safety (EHS) management and broader sustainability goals [39]. This involves a comprehensive analysis of environmental impact, safety performance, and resource utilization to establish a baseline. The developed framework should emphasize measurable outcomes, transforming compliance-focused EHS checkboxes into strategic tools for decision-making [39]. A crucial step is the adoption of a unified safety assessment approach and a scoping analysis to guide innovators, particularly those working at low Technology Readiness Levels (TRLs) [38]. This ensures that sustainability considerations are embedded from the earliest stages of research and development, rather than being retrofitted later.

Stakeholder Integration Protocol

A successful harmonization strategy must incorporate diverse stakeholder perspectives to ensure relevance and comprehensiveness. The protocol for stakeholder integration is an iterative process, structured in four key phases:

Phase 1: Identification and Mapping

• Action: Identify all relevant stakeholder groups, including internal R&D teams, process chemists, toxicologists, environmental scientists, regulatory affairs, manufacturing, patients, and community representatives.
• Output: A stakeholder map detailing influence and interest.

Phase 2: Structured Engagement

• Action: Conduct structured engagements using surveys, focus groups, or dedicated advisory panels. The European Commission's feedback collection on the revised SSbD Framework serves as a model for gathering detailed insights on practicality and applicability [38].
• Output: Qualitative and quantitative feedback on materiality and metric relevance.

Phase 3: Analysis and KPI Refinement

• Action: Analyze collected feedback to identify common priorities and areas of divergence. Use this analysis to refine and weight the proposed integrated KPIs.
• Output: A prioritized set of harmonized metrics that reflect shared stakeholder values.

Phase 4: Feedback and Communication Loop

• Action: Close the loop by reporting back to stakeholders on how their input was integrated. This fosters transparency and maintains engagement for continuous improvement.
• Output: Published sustainability reports and internal communications that build trust and collaborative responsibility [39].

Quantitative Frameworks and Data Analysis

A harmonized system relies on quantifiable, comparable data. This section details the specific metrics and analytical methods for generating robust, decision-ready information.

Development of Integrated KPIs

The development of measurable, SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) Key Performance Indicators is fundamental [39]. These KPIs should bridge traditional EHS and sustainability domains. The table below provides a structured comparison of quantitative data, summarizing core metrics across safety, environmental, and social dimensions for easy benchmarking and integration into an IOP framework.

Table 1: Comparative Analysis of Core Safety and Sustainability Metrics for SSbD

Metric Category	Specific KPI	Measurement Unit	Data Source	Reporting Framework Alignment	IOP Stage Linkage
Safety & Toxicology	LC50 (Fish)	mg/L, 96-hr	OECD Test Guideline 203	GRI, SASB	Hazard Identification
	Ames Test Mutagenicity	Rev/µg	OECD Test Guideline 471	GRI, SASB	Hazard Identification
	Occupational Exposure Limit (OEL)	mg/m³	In-house toxicological assessment	GRI	Exposure Assessment
Environmental Impact	Global Warming Potential	kg CO2-eq/kg API	Life Cycle Assessment (LCA)	GRI, SASB	Outcome Assessment
	Cumulative Energy Demand	MJ/kg API	Life Cycle Assessment (LCA)	GRI	Outcome Assessment
	Process Mass Intensity (PMI)	kg total/kg API	Process calculation	-	Exposure Assessment
Resource Efficiency	Water Consumption	m³/kg API	Utility monitoring	GRI	Outcome Assessment
	% Renewable Solvents	Mass %	Process calculation	-	Exposure Assessment
Social Responsibility	Access to Medicine Index	Score	External Index	GRI	Outcome Assessment

Statistical Analysis and Data Comparison

When comparing quantitative data across different groups or scenarios—such as existing versus new synthetic routes—appropriate statistical summaries and visualizations are essential. Data should be summarized for each group, and the difference between the means and/or medians must be computed to quantify the impact of changes [40]. For instance, comparing the mean Process Mass Intensity (PMI) of a traditional process versus a new SSbD-aligned process directly illustrates resource efficiency gains.

The choice of graphical representation is critical for effective communication:

• Boxplots: Best for comparing distributions across multiple groups (e.g., comparing the distribution of carbon footprints across several candidate processes). They visually represent the median, quartiles, and potential outliers, allowing for immediate assessment of variability and central tendency [40].
• 2-D Dot Charts: Ideal for small to moderate amounts of data, showing individual data points and facilitating direct comparison [40].
• Bar Charts: Effective for illustrating a comparison of specific numerical values, such as total carbon emissions, across a limited number of categories [41].

Visualization of the Harmonized Framework

The logical relationships and workflow of the harmonized metric system can be effectively communicated through the following diagram, which illustrates the continuous improvement cycle within the SSbD context.

Diagram 1: SSbD Metric Integration Cycle. This workflow outlines the iterative process for harmonizing safety and environmental sustainability assessments within a research and development pipeline.

The Researcher's Toolkit: Essential Reagents and Solutions

The practical implementation of this harmonized framework relies on a suite of analytical tools and reagents. The following table details key research solutions essential for generating the required quantitative data.

Table 2: Research Reagent Solutions for Safety and Sustainability Testing

Item Name	Function/Application	Technical Specification
S9 Liver Homogenate	Metabolic activation system for in vitro mutagenicity assays (e.g., Ames Test).	Sourced from Aroclor-1254-induced rodent liver; used per OECD Guideline 471.
Fish Embryo Acute Toxicity (FET) Test Media	Determination of acute toxicity in fish embryos as a alternative to adult fish testing.	Prepared according to ISO 15088 or OECD Guideline 236.
LCA Database & Software	Modeling and calculation of environmental footprint metrics (GWP, CED).	Commercial databases (e.g., Ecoinvent) with software (e.g., SimaPro, GaBi).
ATP Assay Kit	Quantification of cell viability and cytotoxicity for preliminary safety screening.	Luminescent assay measuring intracellular ATP levels.
Stable Isotope-Labeled Standards	Accurate quantification of API and related compounds in environmental and biomonitoring studies.	¹³C or ¹⁵N labeled analogs for use with LC-MS/MS.

The harmonization of safety and sustainability metrics is an achievable and critical objective for advancing the SSbD framework in drug development and chemical innovation. By adopting a structured methodological approach that includes stakeholder engagement, the development of SMART integrated KPIs, and rigorous data analysis, researchers can transform a fragmented landscape into a coherent decision-support system. The provided protocols, quantitative frameworks, and visualization tools offer a concrete path forward. This integration enables a holistic view of impact, guiding the research community toward innovations that are not only therapeutically effective but also inherently safer and more sustainable throughout their entire lifecycle, fully realizing the promise of the Impact Outcome Pathway research.

Scaling laboratory data for industrial impact represents a critical juncture in the innovation pathway for chemicals, materials, and pharmaceuticals. Within the Safe and Sustainable by Design (SSbD) framework, this process transcends simple magnification of production volume, requiring instead a systematic approach to managing uncertainties while preserving both safety and sustainability profiles across scale transitions. The European Commission's SSbD Framework, as a voluntary pre-market approach, explicitly bridges innovation and legislation by fostering development of chemicals and materials that maintain their safety and sustainability characteristics throughout their life cycle [16]. The Impact Outcome Pathway (IOP) approach extends the Adverse Outcome Pathway (AOP) concept by establishing mechanistic links between chemical properties and their environmental, health, and socio-economic consequences, thus providing a structured methodology for tracing impacts across scales [2].

The fundamental challenge in scaling lies in the propagation of uncertainty—where small variations in laboratory conditions can amplify significantly at industrial scale, potentially altering critical safety parameters, environmental impacts, and economic viability. This technical guide addresses these challenges through quantitative methodologies, structured workflows, and computational approaches that together form a robust framework for scaling decisions within SSbD contexts.

Conceptual Foundation: Impact Outcome Pathways for Scaling

The IOP Framework in Scale Transitions

The Impact Outcome Pathway (IOP) framework provides a mechanistic structure for tracing the consequences of scaling decisions across multiple domains. Extending beyond the traditional Adverse Outcome Pathway (AOP) concept used in toxicology, IOPs establish causal links between material properties, process parameters, and their broader health, environmental, and socio-economic consequences [2]. Within scaling operations, this framework allows researchers to map how changes in production volume or process technology might propagate through the system, affecting ultimate safety and sustainability outcomes.

The IOP approach is particularly valuable for addressing the fragmented assessment tradition where health, environmental, social, and economic impacts are evaluated independently. By creating a unified assessment structure, IOPs enable capture of trade-offs and synergies necessary for comprehensive decision-making within the SSbD framework [2]. This integrated perspective is essential when scaling laboratory innovations, where optimizing for single metrics (e.g., yield) without considering broader implications can lead to unintended consequences.

Quantitative Foundations for Predictive Scaling

At the core of reliable scaling lies the transformation of qualitative relationships into quantitative predictive models. The quantitative AOP (qAOP) concept provides a methodological foundation for this transformation, using computational models to describe key event relationships with dose-response and time-course predictions [42]. In the context of scaling, these principles can be adapted to create quantitative scaling pathways (QSPs) that mathematically describe how process parameters evolve across scales.

For repeated exposure scenarios common in industrial applications, dynamic modeling approaches become essential. Recent proof-of-concept research demonstrates how Dynamic Bayesian Networks (DBN) can model the progression of effects across multiple exposures, capturing the cumulative impacts that may only manifest at industrial production scales [6]. These approaches allow for the probability of adverse outcomes to be calculated based on upstream key events observed earlier in the scaling process, enabling proactive identification of potential failure points.

Table 1: Core Concepts in Scaling Frameworks

Concept	Definition	Relevance to Scaling
Impact Outcome Pathway (IOP)	Establishes mechanistic links between properties and their multi-domain consequences [2]	Provides structured framework for tracing scaling effects across safety, environmental, and socioeconomic dimensions
Quantitative Adverse Outcome Pathway (qAOP)	Biologically based computational models describing key event relationships with dose-response predictions [42]	Enables quantitative prediction of how parameter changes during scaling might affect critical outcomes
Dynamic Bayesian Network (DBN)	Probabilistic graphical model that represents variables and their conditional dependencies over time [6]	Models cumulative impacts and uncertainty propagation across multiple scaling steps or repeated exposures
Safe and Sustainable by Design (SSbD)	Voluntary framework integrating safety and sustainability throughout innovation process [16]	Provides holistic assessment criteria for scaling decisions across entire chemical/material life cycle

Quantitative Methodologies for Scaling Operations

Scaling in Prospective Life Cycle Assessment

Prospective Life Cycle Assessment (LCA) represents a critical methodology for evaluating the environmental implications of scaling decisions while technologies are still at laboratory or pilot stages. A systematic review of scaling methods for prospective LCA identified 78 distinct approaches, highlighting the methodological diversity available for scaling exercises [43]. These methods address the fundamental challenge that emerging technologies are often subject to high uncertainties and exist only at laboratory or pilot scale, necessitating robust scaling of LCA-relevant data such as energy and material flows to industrial levels.

The review identified 14 precise methodologies that provide systematic scaling approaches, with significant variation in their complexity, data intensity, duration, and uncertainty characteristics [43]. This methodological diversity underscores the importance of selecting scaling approaches matched to the specific technology context and assessment goals. An Excel-based decision tool derived from this analysis helps researchers select appropriate scaling methodologies by customizing evaluation criteria based on project-specific constraints and requirements [43].

Experimental and Quasi-Experimental Designs for Impact Evaluation

Quantitative Impact Evaluations (QIEs) provide a robust framework for attributing observed outcomes to specific interventions through counterfactual-based designs [44]. These approaches enable researchers to estimate the exact magnitude of impact directly attributable to scaling decisions by comparing treatment groups (those receiving the intervention) with control groups (those not receiving the intervention).

Table 2: Quantitative Impact Evaluation Approaches for Scaling

Method Type	Key Characteristics	Application in Scaling Context
Randomized Controlled Trials (RCTs)	Random assignment to treatment and control groups; considered the most robust design [44]	Testing specific scaling interventions under controlled conditions with high internal validity
Regression Discontinuity (RD)	Compares units just either side of an eligibility cut-off [44]	Evaluating scaling thresholds where interventions apply above specific technical parameter values
Propensity Score Matching (PSM)	Constructs counterfactual by matching treatment units with similar comparison units [44]	Creating comparable groups for scaling evaluation when random assignment isn't feasible
Difference-in-Differences (DID)	Compares outcome changes over time between treatment and comparison groups [44]	Analyzing scaling interventions introduced at different times across production lines or facilities

The selection of appropriate QIE methods depends on both theoretical considerations and practical constraints. While RCTs provide the highest internal validity, they are not always feasible in industrial settings, making quasi-experimental approaches like Regression Discontinuity or Difference-in-Differences valuable alternatives [44]. For comprehensive scaling assessments, mixed-methods frameworks that integrate quantitative and qualitative approaches often provide the most complete understanding of scaling impacts.

Dynamic Bayesian Approaches for Cumulative Effects

For scaling decisions involving repeated exposures or cumulative processes, Dynamic Bayesian Networks (DBNs) offer a powerful modeling framework. Recent proof-of-concept research has demonstrated how DBNs can model chronic toxicity from repeated exposures, capturing the dynamic nature of biological responses that may only manifest after multiple iterations [6]. In scaling contexts, these approaches can model how small, sub-critical perturbations might accumulate across scaling steps to produce significant impacts.

The DBN framework enables calculation of the probability of adverse outcomes based on upstream key events observed earlier in the scaling process [6]. This capability is particularly valuable for identifying early indicators of potential scaling failures, allowing for proactive adjustments before significant resources are committed. Furthermore, data-driven AOP pruning techniques using lasso-based subset selection can reveal how causal structures themselves may change over time and across scales, highlighting the dynamic nature of biological and technical systems during scaling operations [6].

Practical Implementation: Protocols for Scaling Operations

Laboratory Scaling Assessment Protocol

Implementing robust scaling operations begins with a comprehensive assessment of current laboratory operations. The following protocol provides a structured approach for establishing baseline metrics and identifying potential scaling constraints:

• Operational Audit: Conduct a step-by-step review of existing workflows, standard Operating Procedures (SOPs), and physical layout to pinpoint bottlenecks. Evaluate specimen intake, testing protocols, and reporting processes, with particular attention to manual data entry points and peak-hour backlogs [45].

• Technology Assessment: Inventory current data management systems, noting dependencies on paper records or siloed spreadsheets. Assess instrument integration capabilities and data export functionalities critical for scaling operations [46].

• Workforce Analysis: Document current staff skills, training protocols, and cross-functional capabilities. Identify single points of failure where specialized knowledge is concentrated in few individuals [46].

• Space Utilization Review: Perform a space audit to determine if any areas are poorly used or underutilized. Evaluate whether existing layouts can be adjusted to improve workflow efficiency without requiring physical expansion [46].

This assessment should be conducted as a collaborative exercise involving stakeholders from all departments, with staff encouraged to provide input on priorities and constraints [46]. The output forms the foundation for developing a targeted scaling strategy with clear baseline metrics.

Technology Implementation Framework

Selecting and implementing appropriate technologies is crucial for supporting scaling operations. The following framework provides a structured approach for technology decisions:

• Laboratory Information Management System (LIMS) Selection: Prioritize cloud-based systems designed to handle increasing data volumes seamlessly. Key evaluation criteria should include automation capabilities for repetitive tasks, integration options with instruments and EHRs, and real-time analytics for monitoring critical metrics [45].

• Data Architecture Planning: Implement systems that eliminate data silos and provide a single source of truth. Ensure the architecture supports both current needs and anticipated data volumes from scaled operations [45].

• Automation Assessment: Identify repetitive tasks that would benefit from automation, focusing on activities that consume significant staff time or introduce variability. Prioritize automation investments based on return-on-effort calculations [46].

• Vendor Partnership Development: Establish relationships with technology providers who offer comprehensive implementation guidance, staff training, and troubleshooting services. Regular check-ins ensure ongoing alignment with evolving scaling requirements [45].

Technology implementations should be phased to minimize disruption, with clear success metrics defined for each implementation stage. Vendor selection should prioritize systems with demonstrated scalability rather than those optimized for current volumes only.

Uncertainty Management Protocol

Managing uncertainty requires structured approaches for identifying, quantifying, and mitigating risks throughout scaling operations:

• Uncertainty Mapping: Document known uncertainties across technical parameters, safety profiles, environmental impacts, and economic projections. Categorize uncertainties as reducible (through additional data collection) or irreducible (requiring contingency planning) [43].

• Sensitivity Analysis Implementation: Apply global sensitivity analysis techniques to identify parameters with greatest influence on outcomes. Focus mitigation efforts on high-sensitivity parameters with significant uncertainty [6].

• Scenario Planning: Develop multiple scaling scenarios with varying assumptions about critical parameters. Define trigger points indicating when to transition between scenarios based on performance metrics [44].

• Adaptive Monitoring: Implement leading indicator systems that provide early warning of deviation from expected scaling trajectories. Establish clear protocols for response actions when indicators exceed threshold values [6].

This protocol should be integrated throughout the scaling lifecycle rather than applied as a one-time assessment, with regular uncertainty reviews scheduled at key decision points.

Visualization Frameworks for Scaling Decisions

IOP-Based Scaling Decision Framework

The following diagram illustrates the integrated decision framework for scaling operations within the IOP and SSbD context, highlighting the interconnected nature of assessment domains and decision points:

This framework emphasizes the iterative nature of scaling decisions, where initial laboratory data undergoes both IOP analysis and computational scaling modeling before integrated assessment against SSbD criteria. The decision to proceed to industrial implementation or iterate based on findings ensures that scaling only occurs when safety and sustainability requirements are met.

Quantitative Scaling Methodology Workflow

The quantitative assessment of scaling options requires a structured workflow that integrates multiple methodological approaches, as shown in the following diagram:

This workflow highlights the sequential yet iterative process for developing quantitative scaling assessments, from initial objective setting through methodology selection, model development, uncertainty analysis, and final decision support with explicit confidence intervals.

Research Reagent Solutions for Scaling Studies

Table 3: Essential Research Reagents and Computational Tools for Scaling Studies

Reagent/Tool	Function	Application in Scaling Context
Bayesian Network Software (e.g., R Bayesian packages)	Probabilistic modeling of causal relationships under uncertainty [6]	Modeling uncertainty propagation across scaling steps; predicting probability distributions of outcomes
LCA Scaling Databases	Provide standardized scaling factors for energy and material flows [43]	Supporting prospective LCA of scaled systems with consistent methodology
qAOP Model Components	Computational models of key event relationships in biological pathways [42]	Predicting how biological responses might change with altered exposure scenarios in scaled systems
Dynamic Bayesian Network Frameworks	Modeling temporal evolution of probabilistic systems [6]	Analyzing cumulative impacts across multiple scaling stages or repeated exposures
Power Calculation Tools	Determining optimal sample sizes for experimental designs [44]	Ensuring scaling studies have appropriate statistical power to detect significant effects
Computer-Assisted Interviewing Platforms	Standardized data collection from operational staff [44]	Gathering consistent input on scaling constraints and opportunities across organizational units

Managing uncertainty when scaling laboratory data to industrial impact requires integration of multiple methodological approaches within overarching frameworks like SSbD and IOP. The quantitative methodologies, implementation protocols, and visualization frameworks presented in this guide provide a structured approach for navigating the complex transition from laboratory innovation to industrial implementation while preserving safety and sustainability characteristics.

The most successful scaling operations combine rigorous quantitative assessment with pragmatic operational planning, recognizing that technical excellence must be matched by organizational readiness. By adopting the integrated framework presented here—spanning IOP analysis, quantitative impact evaluation, dynamic modeling, and systematic uncertainty management—researchers and technology developers can significantly enhance the reliability and success rate of scaling operations while maintaining alignment with SSbD principles.

As scaling methodologies continue to evolve, particularly with advances in computational modeling and uncertainty quantification, the potential grows for more predictive and efficient scale transitions. The frameworks and protocols outlined here provide a foundation for incorporating these advances into structured scaling operations that effectively balance innovation potential with appropriate risk management across technical, environmental, health, and socioeconomic dimensions.

The European Union's Safe and Sustainable by Design (SSbD) framework represents a transformative approach to chemicals and materials innovation, integrating safety and sustainability considerations throughout the research and development lifecycle. As this framework evolves toward a revised 2025 recommendation, the Impact Outcome Pathway (IOP) approach emerges as a critical methodological foundation for generating regulatory-relevant data. This technical guide examines strategies for aligning IOP-derived outputs with the dynamic SSbD regulatory landscape, addressing key challenges in data integration, methodological harmonization, and prospective assessment. By establishing explicit linkages between mechanistic toxicology, exposure science, life cycle assessment, and regulatory decision-making, researchers can ensure their IOP frameworks effectively support the development of safer, more sustainable chemicals and materials while maintaining compliance with emerging European standards.

The EU Chemicals Strategy for Sustainability (CSS) has established an ambitious policy framework that demands fundamental changes in how chemicals and materials are developed, assessed, and brought to market. The Safe and Sustainable by Design (SSbD) framework, initially formalized in a 2022 European Commission Recommendation and currently undergoing revision for a 2025 update, represents the operationalization of these policy goals into a practical innovation tool [38] [16]. This voluntary pre-market approach integrates safety and sustainability considerations throughout a product's entire lifecycle, from conception through disposal, requiring researchers to simultaneously address functional performance, human health, environmental impacts, and socio-economic factors [47].

The Impact Outcome Pathway (IOP) framework has emerged as a pivotal methodological approach for meeting SSbD requirements. Extending the Adverse Outcome Pathway (AOP) concept, IOPs establish mechanistic links between chemical and material properties and their multi-dimensional consequences, encompassing environmental, health, and socio-economic dimensions [2]. As the European Commission refines the SSbD framework based on insights from over 80 case studies and stakeholder consultations, alignment between IOP methodologies and regulatory expectations becomes increasingly critical for effective technology transfer and market approval [38].

For researchers in chemical and pharmaceutical development, understanding this evolving landscape is essential for designing studies that generate regulatory-relevant data. The revised SSbD framework introduces several key elements that directly impact IOP development, including a formal 'Scoping Analysis' phase, unified safety assessment protocols, and benchmarked environmental sustainability assessments [38]. This guide provides a comprehensive technical framework for aligning IOP outputs with these evolving requirements, enabling more efficient translation of research into compliant, commercially viable products.

Core Principles of the SSbD Framework

Foundational Concepts and Design Principles

The SSbD framework integrates principles from multiple disciplines, creating a holistic approach to chemical and material innovation. The foundation draws from green chemistry (atom economy, non-toxic products, design for degradation), green engineering (energy efficiency, reduced emissions, water conservation), and circular economy concepts (renewable resources, biodegradable materials, life cycle thinking) [47]. These principles are operationalized through a two-component structure: (1) application of design principles throughout the innovation process, and (2) iterative safety and sustainability assessment at defined stages [48].

The framework adopts a life cycle perspective across five assessment steps: Step 1 focuses on intrinsic hazard assessment of the chemical or material; Step 2 addresses human health and safety aspects during production and processing; Step 3 evaluates safety during the application stage; Step 4 assesses environmental sustainability impacts; and Step 5 examines socio-economic sustainability [16]. This sequential but iterative structure enables proactive identification of hotspots and critical issues across the entire value chain, facilitating continuous improvement from both safety and sustainability perspectives.

Recent Developments and 2025 Revisions

The SSbD framework is currently undergoing significant refinement based on extensive practical testing. The European Commission's July 2025 consultation on the revised framework introduces several key changes that researchers must incorporate into their IOP development strategies [38]. These revisions reflect practical challenges identified during implementation, particularly regarding applicability to early-stage innovations with low Technology Readiness Levels (TRLs).

Table 1: Key Revisions in the 2025 SSbD Framework

Revision Area	2022 Framework	2025 Proposed Revision	Implications for IOP Development
Scoping Phase	Implicit in assessment	Formal 'Scoping Analysis' required	Earlier definition of system boundaries and assessment goals
Safety Assessment	Fragmented across steps	Unified approach	More integrated hazard and exposure assessment
Sustainability Benchmarking	Relative assessments	Environmental sustainability benchmarks	Absolute sustainability references for IOP endpoints
Innovation Stage Applicability	Limited guidance for early TRLs	Explicit accommodation of low-TRL innovations	Tiered approaches for data-scarce environments

The revised framework places increased emphasis on prospective assessment capabilities, recognizing that the greatest opportunity for implementing SSbD principles occurs during early development stages [47] [49]. This aligns with the intrinsic strengths of the IOP approach, which enables predictive assessment of impacts before extensive empirical data collection. Additionally, the 2025 revisions strengthen requirements for socio-economic assessment and broader sustainability metrics, expanding the scope beyond traditional environmental impact indicators [38].

The Impact Outcome Pathway (IOP) Framework

Conceptual Foundation and Methodological Approach

The Impact Outcome Pathway (IOP) framework represents a significant evolution beyond the Adverse Outcome Pathway (AOP) concept, extending the mechanistic chain from molecular initiating events to broader environmental, health, and socio-economic consequences [2]. IOPs establish systematic linkages between chemical properties, exposure scenarios, biological interactions, and multi-scale impacts, providing a structured approach for predicting and assessing both adverse and beneficial outcomes throughout a product's life cycle.

The IOP framework integrates diverse data sources—including omics data, life cycle inventories, exposure models, and socio-economic indicators—into a unified knowledge graph structure that adheres to FAIR (Findable, Accessible, Interoperable, Reusable) data principles [2] [5]. This computational infrastructure enables more transparent, reproducible, and mechanistically grounded assessments that can evolve with increasing information availability throughout the innovation process. For drug development professionals, this approach facilitates early identification of potential regulatory hurdles and sustainability hotspots, enabling more targeted and efficient development strategies.

IOP Structural Components and Regulatory Alignment

A standardized IOP structure consists of several interconnected components that collectively support SSbD assessment requirements. Each component addresses specific regulatory information needs while maintaining flexibility for application-specific customization.

Table 2: Core IOP Components and Their SSbD Regulatory Functions

IOP Component	Technical Description	SSbD Assessment Relevance	Data Requirements
Molecular Initiating Event	Initial interaction between chemical and biological entity	Step 1: Hazard assessment	Structural properties, reactivity, binding affinity
Cellular Key Events	Measurable responses at cellular level	Step 1: Hazard assessment	In vitro assays, omics data, high-throughput screening
Tissue/Organ Responses	Functional consequences at tissue level	Steps 2-3: Human health & safety	Histopathology, organ-specific toxicity, mechanistic studies
Organism-level Outcomes	Whole-organism adverse effects	Steps 2-3: Safety assessment	Traditional toxicology, pathophysiology, animal models (transitioning to NAMs)
Population & Ecosystem Impacts	Consequences at population level	Step 4: Environmental sustainability	Environmental fate data, ecotoxicity, species sensitivity
Socio-economic Consequences	Broader societal implications	Step 5: Socio-economic assessment	LCA, economic modeling, social impact indicators

The IOP framework specifically supports the transition to New Approach Methodologies (NAMs) advocated in both SSbD implementation and modern toxicology [2]. By establishing quantitative relationships between key events at different biological levels, IOPs enable greater use of in vitro and in silico methods for predicting in vivo outcomes, reducing reliance on traditional animal testing while potentially increasing predictive accuracy and mechanistic understanding.

Methodological Protocols for IOP-SSbD Integration

Tiered Assessment Strategy

Implementing a tiered assessment strategy is critical for effectively aligning IOP development with SSbD requirements across different innovation stages. This approach efficiently allocates resources while ensuring regulatory relevance throughout the development process.

Tier 1: Early Development (TRL 1-3) initiates with a comprehensive scoping analysis to define assessment boundaries, identify relevant regulatory thresholds, and establish baseline comparisons against existing substances [48]. This stage employs computational screening methods, including QSAR modeling and read-across approaches, to generate initial hazard classifications with minimal laboratory resources. The IOP structure at this tier focuses on identifying critical key events and potential showstoppers, enabling early iteration before significant R&D investment.

Tier 2: Process Optimization (TRL 4-6) implements targeted experimental testing to address data gaps identified in Tier 1 and refine IOP quantitative relationships. This includes focused in vitro assays measuring molecular initiating events and early cellular key events, along with initial exposure assessment using computational fate and transport models [49]. Prospective life cycle assessment integrates emerging process data to identify environmental hotspots, while initial socio-economic assessment evaluates broader implications of technology adoption.

Tier 3: Pre-Market (TRL 7-9) validates IOP quantitative relationships through standardized testing protocols and generates the comprehensive data packages required for regulatory submissions. This tier integrates higher-tier exposure modeling using substance-specific parameters, completes full environmental and socio-economic life cycle assessments, and establishes monitoring strategies for post-market surveillance [16]. The output is a validated IOP that directly supports SSbD assessment steps and regulatory decision-making.

Experimental Protocol: IOP Development for SSbD Compliance

This detailed protocol provides a standardized methodology for developing and validating IOPs that meet SSbD informational requirements across the innovation lifecycle.

Protocol 1: Comprehensive IOP Development Workflow

Objective: Establish a mechanistically grounded IOP that predicts impacts across biological levels and supports SSbD assessment requirements.

Materials and Equipment:

• Test substance (various purity levels appropriate for development stage)
• In vitro bioassay systems relevant to anticipated exposure routes
• Analytical instrumentation for substance characterization (HPLC, MS, NMR)
• Computational resources for QSAR, read-across, and exposure modeling
• Life cycle assessment software with appropriate database access
• Data management infrastructure supporting FAIR principles

Procedure:

• Substance Characterization

  • Determine physicochemical properties (log P, water solubility, vapor pressure, particle size distribution)
  • Identify structural features and potential reactivity using computational approaches
  • Assess stability under relevant conditions (pH, temperature, light exposure)
  • Document characterization methods and results following FAIR data principles

• Molecular Initiating Event Identification

  • Screen for protein binding (plasma proteins, receptors, enzymes)
  • Assess membrane interactions and bioavailability
  • Evaluate oxidative stress potential and antioxidant consumption
  • Quantify dose-response relationships for identified interactions

• Cellular Key Event Characterization

  • Conduct high-throughput in vitro screening using relevant cell lines
  • Measure cytotoxicity, genotoxicity, and specific pathway activation
  • Apply omics technologies (transcriptomics, proteomics, metabolomics) to identify novel key events
  • Establish quantitative concentration-response relationships

• Tissue and Organ Level Assessment

  • Utilize complex in vitro models (3D cultures, organoids, MPS)
  • Assess functional impacts on barrier integrity, metabolic competence, and tissue-specific functions
  • Apply PBPK modeling to extrapolate in vitro concentrations to in vivo relevance
  • Identify adversity thresholds based on functional impairment

• Exposure Assessment Integration

  • Develop analytical methods for substance quantification in biological and environmental matrices
  • Determine environmental fate and transport using validated models
  • Assess workplace exposure scenarios using operational condition data
  • Estimate human and environmental exposure levels across life cycle stages

• Life Cycle Impact Assessment

  • Compile life cycle inventory data for all relevant processes
  • Apply characterization factors for environmental impact categories
  • Calculate human and ecotoxicity potentials using IOP-informed factors
  • Assess resource use, energy consumption, and carbon footprint

• IOP Quantitative Modeling

  • Establish mathematical relationships between key events
  • Quantify intra- and inter-individual variability
  • Develop extrapolation approaches between experimental systems and humans
  • Define uncertainty distributions for key parameters

Validation Requirements:

• Experimental reproducibility across multiple lots and testing occasions
• Predictive accuracy for in vivo outcomes using independent validation sets
• Computational model performance metrics (sensitivity, specificity, AUC)
• Consistency with existing scientific literature and regulatory benchmarks

SSbD Alignment Documentation:

• Explicit mapping of IOP components to SSbD assessment steps
• Gap analysis against regulatory data requirements
• Testing strategy justification based on tiered approach principles
• Data quality assessment using established metrics

Research Reagent Solutions for IOP Development

The successful implementation of IOP approaches within SSbD frameworks requires specialized research tools and assessment methodologies. These "research reagents" encompass both physical materials and computational resources that enable mechanistically grounded, regulatory-relevant assessments.

Table 3: Essential Research Reagents and Methodologies for IOP-SSbD Integration

Category	Specific Tools/Methods	Function in IOP Development	SSbD Application
Computational Toxicology	QSAR models, Molecular docking, Read-across frameworks	Predicting molecular initiating events and early key events	Step 1: Hazard assessment for data-scarce early development
In Vitro Bioassay Systems	High-throughput screening, Stem cell-derived models, MPS	Characterizing cellular key events and dose-response relationships	Steps 1-3: Human health assessment with reduced animal testing
Analytical Characterization	HPLC-MS, NMR, Spectroscopy	Substance identification, impurity profiling, degradation monitoring	Steps 2-3: Safety assessment during production and application
Exposure Assessment Tools	FATE models, PBPK modeling, High-throughput exposure models	Quantifying human and environmental exposure scenarios	Steps 2-3: Risk characterization during production and use
Life Cycle Assessment	Prospective LCA databases, Impact assessment methods, Circularity metrics	Evaluating environmental impacts across life cycle stages	Step 4: Environmental sustainability assessment
Data Integration Platforms	KNIME, Pipeline Pilot, Custom workflow managers	Integrating diverse data sources into unified IOP knowledge graphs	All steps: Supporting FAIR data principles and assessment integration
Uncertainty Analysis Tools	Monte Carlo simulation, Bayesian networks, Sensitivity analysis	Quantifying and propagating uncertainty through IOPs	All steps: Informing decision-making under uncertainty

These research reagents collectively enable the development of mechanistically grounded IOPs that directly support SSbD assessment requirements. The computational tools facilitate early-stage screening and prioritization, while experimental systems generate regulatory-grade data for higher-tier assessments. Critically, these resources support the transition to animal-free testing strategies aligned with both SSbD principles and evolving regulatory expectations for New Approach Methodologies (NAMs) [2] [50].

Visualization Framework for IOP-SSbD Integration

Effective communication of IOP structures and their relationship to SSbD assessment requirements is essential for regulatory acceptance and stakeholder engagement. The following visualization framework establishes standardized approaches for representing these complex relationships.

This visualization framework establishes clear linkages between IOP components and specific SSbD assessment steps, enabling researchers to systematically demonstrate regulatory relevance throughout the pathway development process. The framework highlights where specific IOP elements contribute to distinct regulatory decisions, facilitating more targeted testing strategies and efficient resource allocation.

The evolving SSbD regulatory landscape presents both challenges and opportunities for researchers developing innovative chemicals and materials. By strategically aligning Impact Outcome Pathway methodologies with SSbD framework requirements, researchers can generate regulatory-relevant data more efficiently while supporting the development of genuinely safer and more sustainable products. The tiered assessment strategy, standardized experimental protocols, and integrated visualization framework presented in this guide provide practical approaches for navigating this complex landscape.

As the SSbD framework moves toward formalization in the 2025 Commission Recommendation, proactive adoption of these alignment strategies will position researchers for more successful regulatory outcomes while contributing to the broader transition toward a circular, climate-neutral economy. The integrated, mechanistic approach embodied in IOP development directly supports the European Green Deal objectives and represents the future of chemical and material innovation within the European Union and beyond.

The assessment of chemicals and materials, particularly within the context of the Safe and Sustainable by Design (SSbD) framework, has traditionally been fragmented, with health, environmental, social, and economic impacts evaluated independently. This disjointed approach fundamentally limits the ability to capture critical trade-offs and synergies necessary for comprehensive decision-making in research and drug development [2]. The EU INSIGHT project addresses this core challenge by developing a novel computational framework for integrated impact assessment, based on the Impact Outcome Pathway (IOP) approach [2] [4]. This technical guide elucidates how engaging the entire value chain is not merely beneficial but essential for generating the comprehensive, high-quality data required to populate these advanced models. By fostering systematic collaboration among researchers, manufacturers, suppliers, and end-users, we can bridge the existing data gaps and propel the adoption of a truly mechanistic and predictive SSbD paradigm.

The IOP framework extends the established Adverse Outcome Pathway (AOP) concept by establishing mechanistic links between the properties of a chemical or material and its broader environmental, health, and socio-economic consequences [2]. However, the development of robust, causally anchored IOPs is contingent upon access to structured, multi-source data that spans the entire lifecycle of a substance. This guide provides researchers and drug development professionals with the methodologies and tools to orchestrate this collaborative data collection, ensuring that the resulting data is not only comprehensive but also FAIR (Findable, Accessible, Interoperable, and Reusable)—a cornerstone of the INSIGHT framework [2] [5].

The Data Value Chain in IOP Research

Conceptual Framework and Stages

The data value chain provides a critical conceptual model for understanding the process of data creation and use, from identifying an initial need to its final impact on decision-making [51]. Within IOP research, this chain describes the pathway from raw, fragmented data to actionable knowledge for SSbD assessments. The chain comprises four major stages: Collection, Publication, Uptake, and Impact, which are further subdivided into twelve distinct steps as shown in Table 1 [51]. This model serves as both a teaching tool to illustrate the complex journey of data and a management tool to monitor and evaluate the data production process, ensuring that collaboration is maintained throughout to maximize the value and utility of the data for all stakeholders.

Table 1: Stages and Steps of the Data Value Chain for IOP and SSbD Research

Stage	Steps Involved	Description in IOP/SSbD Context	Key Stakeholders
Collection	Identify, Collect, Process, Analyze	Defining data needs for IOPs, gathering raw data from omics, LCA, exposure models; data cleaning and initial analysis [2] [51].	Research Scientists, Lab Technicians, Data Engineers
Publication	Release, Disseminate	Structuring data into a FAIR-compliant knowledge graph; making data accessible via APIs and web platforms [2] [4].	Data Scientists, Consortium Managers, IT Specialists
Uptake	Connect, Incentivize, Influence, Use	Integrating data into the INSIGHT framework's model and IOP graphs; guiding industrial and regulatory users via decision-support tools [4] [5].	Model Developers, Regulatory Affairs, Policy Makers
Impact	Change, Reuse	Informing SSbD decisions; optimizing chemical/product design; supporting policy; enabling data reuse for new research questions [2] [51] [5].	Regulators, Industry R&D, Product Developers, Researchers

A constant feedback loop between data producers and stakeholders is essential throughout this value chain [51]. For instance, the "Impact" and "Reuse" stages should directly inform future data "Collection" needs, creating a responsive and dynamic data ecosystem that continuously improves the robustness of IOP models.

Visualizing the Integrated Workflow

The following diagram, generated using Graphviz, illustrates the logical flow and interdependencies between collaborative data collection, the data value chain, the integrated INSIGHT framework, and the final decision-support system.

Diagram 1: Integrated IOP Data Workflow. This diagram outlines the pathway from collaborative data gathering through the INSIGHT framework to final SSbD decisions, highlighting the critical feedback loop.

Experimental Protocols for Collaborative Data Generation

Protocol 1: Constructing an IOP Knowledge Graph from Multi-Source Data

This protocol details the methodology for building a structured, FAIR-compliant knowledge graph to support IOP development, a core component of the INSIGHT project [2].

• Objective: To integrate heterogeneous data sources (omics, life cycle inventories, exposure models, socio-economic data) into a unified knowledge graph that establishes mechanistic links between chemical properties and their broad impacts.
• Materials and Reagents:
  • Data Sources: Transcriptomics (e.g., RNA-seq) data, in vitro high-throughput screening data, life cycle inventory (LCI) databases, physicochemical property data, economic use-phase data.
  • Computational Tools: Knowledge graph platform (e.g., graph database like Neo4j), data processing scripts (Python/R), ontology management tools (e.g., for AOP Wiki alignment).
  • Standards: AOP and IOP ontologies, FAIR data principles checklist, ISA (Investigation, Study, Assay) metadata framework.
• Methodology:
  • Data Identification and Curation: Collaborate with value chain partners to identify relevant internal and external data sources. Apply rigorous curation to ensure data quality and annotate with controlled vocabularies and ontologies.
  • Data Processing and Harmonization: Process raw data (e.g., normalize omics data, calculate characterization factors for LCA). Harmonize data formats and units to ensure interoperability.
  • Node and Relationship Definition: Define the key entities (nodes) in the graph (e.g., Molecular Initiating Event, Key Event, Adverse Outcome, Social Outcome, Economic Cost). Establish the causal and correlational relationships (edges) between them based on the IOP framework.
  • Graph Population and Validation: Populate the knowledge graph with the curated data. Validate the structure and content through SPARQL queries or graph algorithms to ensure logical consistency and mechanistic plausibility.
  • FAIRification: Expose the knowledge graph via a RESTful API, assign persistent identifiers (PIDs) to key entities, and provide rich metadata to ensure the resource is Findable, Accessible, Interoperable, and Reusable.

Protocol 2: Validating IOPs Using Case Study Data

This protocol outlines the process for validating hypothesized IOPs using specific case studies, as demonstrated in the INSIGHT project with PFAS, graphene oxide, bio-based silica, and antimicrobial coatings [2].

• Objective: To empirically test and refine IOP models using integrated data from targeted case studies, thereby benchmarking the INSIGHT framework.
• Materials:
  • Test Chemicals/Materials: e.g., a specific PFAS compound, graphene oxide sample.
  • Experimental Systems: Relevant in vitro models (e.g., 2D/3D cell cultures), non-animal New Approach Methodologies (NAMs), and material characterization equipment.
  • Computational Models: Physiologically Based Kinetic (PBK) models, exposure models (e.g., INTEGRA), Life Cycle Impact Assessment (LCIA) models, and the pre-populated IOP knowledge graph.
• Methodology:
  • Case Study Definition: In collaboration with industrial partners, define the scope and boundaries of the case study, including the chemical's application and lifecycle stages.
  • Data Generation and Collection: Generate new experimental data on hazard (using NAMs) and collect existing data on environmental fate, material flows, and socio-economic factors from value chain partners.
  • Multi-Model Simulation: Execute integrated simulations using the INSIGHT framework. This involves passing data through the interconnected model graph to predict impacts across health, environment, and society.
  • Impact Analysis and Comparison: Calculate key metrics such as Risk Characterization Ratios (RCRs) and Life Cycle Impact scores. Compare predictions against experimental or monitoring data where available.
  • IOP Refinement: Analyze discrepancies between predictions and observations to refine the proposed IOPs, strengthening or modifying the causal links within the knowledge graph. This iterative process enhances the predictive power and mechanistic accuracy of the framework.

The Scientist's Toolkit: Essential Research Reagent Solutions

The successful implementation of the aforementioned protocols relies on a suite of essential tools, models, and data resources. The table below catalogs key components of the research "toolkit" for conducting integrated assessments within the IOP/SSbD framework.

Table 2: Essential Research Reagent Solutions for IOP and SSbD Investigations

Tool/Resource	Function in IOP Research	Application Context
Adverse Outcome Pathway (AOP) Wiki	Provides a structured knowledge framework for linking molecular perturbations to adverse outcomes at the organism level [2].	Foundation for developing the health-related components of an IOP.
Life Cycle Inventory (LCI) Data	Supplies quantitative data on material/energy inputs, emissions, and wastes associated with a product's lifecycle [2].	Informing the environmental and resource impact nodes of an IOP.
New Approach Methodologies (NAMs)	Non-animal testing approaches (in vitro, in chemico, in silico) for hazard assessment [2].	Generating human-relevant toxicity data for IOP key events.
FAIR Data Management Plan (DMP)	A formal plan outlining how data will be managed, shared, and preserved according to FAIR principles [2].	Ensuring data generated across the value chain is reusable and interoperable.
INSIGHT Decision-Support System	Interactive, web-based decision maps that guide users through the multi-criteria evaluation of chemicals/materials [4].	Translating complex IOP model outputs into actionable insights for R&D and regulatory stakeholders.
Quantitative Structure-Activity Relationship (QSAR) Models	Computational models that predict physicochemical and toxicological properties from molecular structure [2].	Filling data gaps for key event predictions, especially in early design phases.
Social Life Cycle Assessment (S-LCA) Data	Data and methodologies for assessing the social and socio-economic impacts of a product across its lifecycle [2].	Populating the social and economic impact nodes of an IOP.

Visualizing the INSIGHT Multi-Layer Framework Architecture

The core of the INSIGHT project is a multi-layer computational architecture that integrates data, models, and impact pathways. The following Graphviz diagram deconstructs this architecture and its information flow.

Diagram 2: INSIGHT Multi-Layer Architecture. This diagram shows the interconnection between the FAIR Data Graph, the computational Model Graph, and the resulting IOP Graph, which together feed into interactive decision-support tools.

The transition to a truly predictive and mechanistic Safe and Sustainable by Design paradigm is intrinsically linked to our ability to foster collaboration across the entire value chain. By systematically implementing the data value chain model [51] within the integrated, multi-layer architecture of frameworks like INSIGHT [2] [4], researchers and drug development professionals can overcome the limitations of fragmented data. The experimental protocols and tools detailed in this guide provide a concrete pathway for generating the comprehensive, FAIR-compliant data required to build and validate robust Impact Outcome Pathways. This collaborative and data-driven approach is fundamental to bridging the gap between mechanistic toxicology, exposure modeling, life cycle assessment, and socio-economic analysis, thereby enabling the informed decision-making necessary to achieve the goals of the European Green Deal and global sustainability objectives [2] [5].

Proven Impact: Case Studies and Comparative Analysis of the IOP Framework

The assessment of chemicals and materials has traditionally been fragmented, with health, environmental, social, and economic impacts evaluated independently. This disjointed approach limits the ability to capture trade-offs and synergies necessary for comprehensive decision-making under the Safe and Sustainable by Design (SSbD) framework [3]. The EU INSIGHT project addresses this challenge by developing a novel computational framework for integrated impact assessment, based on the Impact Outcome Pathway (IOP) approach [3]. This framework is fully aligned with the EU's Chemical Strategy for Sustainability, the European Green Deal, and the Advanced Materials 2030 Initiative [4].

The INSIGHT project establishes an innovative framework for the mechanistic impact assessment of chemicals and materials using a multi-layered approach that integrates data graphs, model graphs, and Impact Outcome Pathway (IOP) graphs that systematically predict health, environmental, social, and economic impacts [4]. By extending the Adverse Outcome Pathway (AOP) concept, IOPs establish mechanistic links between chemical and material properties and their environmental, health, and socio-economic consequences, thereby bridging mechanistic toxicology, exposure modeling, life cycle assessment, and socio-economic analysis [3].

The INSIGHT IOP Framework: Structure and Components

Core Architectural Components

The INSIGHT framework's multi-layer architecture consists of three systematically interlinked graphs [4]:

• Data Graph: Integrates multi-source datasets (including omics, life cycle inventories, and exposure models) into a structured knowledge graph, ensuring FAIR data principles (Findable, Accessible, Interoperable, Reusable) are met [3].
• Model Graph: Provides curated and user-friendly FAIR data and computational models/workflows that support the development of the next generation SSbD chemicals and materials [4].
• IOP Graph: Establishes mechanistic links between chemical and material properties and their environmental, health, and socio-economic consequences, extending the Adverse Outcome Pathway (AOP) concept [3].

IOP Workflow and Implementation

The implementation of IOPs within the INSIGHT framework follows a structured workflow that transforms raw data into actionable insights for SSbD decision-making. The diagram below illustrates this integrated workflow:

Figure 1: Integrated IOP Workflow in INSIGHT Framework

PFAS Case Study: Applying IOPs to "Forever Chemicals"

PFAS Characteristics and Challenges

Per- and polyfluoroalkyl substances (PFAS), known as "forever chemicals," exhibit exceptional chemical stability and resistance to environmental degradation due to their strong C-F bonds and nonpolar nature [52]. This persistence, combined with their widespread use in products such as adhesives, electronics, firefighting foam, and lubricants, has led to significant environmental and health concerns [52]. PFAS compounds are primarily classified into polymer and nonpolymer types, each with distinct molecular structures and applications [52].

The tribological applications of PFAS (including lubricants and surface coatings) present particular challenges as these applications lead to the removal of PFAS particles from the original system by wear, vaporization, or other mass loss mechanisms, creating multiple exposure pathways for humans through inhalation, ingestion, or direct contact [52]. These pollutants directly or indirectly affect plants, animals, and humans, with biomagnification increasing PFAS concentration in the human body, potentially leading to serious health issues [52].

Quantitative Profile of PFAS in Lubrication

Table 1: PFAS Characteristics and Environmental Impact Data

Parameter	Value/Range	Significance
C-F Bond Strength	~116 kcal/mol [52]	Exceptional chemical stability and resistance to degradation
Annual Lubricant Demand (US)	2.4 billion gallons [52]	Scale of potential environmental release
PFAS Content in Oils	0.1-30% by volume [52]	Concentration range in final products
Environmental Persistence	Resistant to natural decomposition [52]	"Forever chemical" characterization
Bioaccumulation Factor	Increases across trophic levels [52]	Biomagnification in food chains

Experimental Framework for PFAS Assessment

Mechanistic Toxicity Testing Protocols

The application of IOPs to PFAS requires standardized testing methodologies to generate comparable data across different substances and exposure scenarios. The following experimental protocols represent core approaches referenced in the INSIGHT framework:

Protocol 1: Bioaccumulation Potential Assessment

• Objective: Quantify the tendency of PFAS to accumulate in biological tissues across trophic levels.
• Methodology: Use of stable isotope tracing combined with mass spectrometry to track PFAS uptake in model organisms (e.g., zebrafish, Daphnia).
• Endpoint Measurements: Bioconcentration Factor (BCF), Bioaccumulation Factor (BAF), and trophic magnification factors.
• Quality Controls: Include reference materials with known BCF values, blanks to monitor contamination, and duplicate samples for precision assessment.

Protocol 2: Tribological Release Characterization

• Objective: Measure PFAS release from lubricants and coatings under simulated use conditions.
• Methodology: Employ pin-on-disk tribometers with controlled environmental chambers to simulate real-world wear conditions.
• Analysis: Quantify released PFAS in collected particulates using LC-MS/MS and characterize particle size distribution.
• Parameters: Vary normal load, sliding speed, temperature, and counterface materials to represent different application scenarios.

Research Reagent Solutions for PFAS Testing

Table 2: Essential Research Materials for PFAS IOP Development

Reagent/Material	Function	Application Context
PFAS Analytical Standards	Quantification and identification reference	Mass spectrometry calibration and method validation
Model Organisms (Zebrafish, Daphnia)	Assessment of bioaccumulation and toxicity	Eco-toxicological studies within IOP framework
Tribological Test Systems (Pin-on-disk, SRV)	Simulation of wear and release scenarios	Measurement of PFAS release from lubricants and coatings
In Vitro Assay Kits (Cytotoxicity, Genotoxicity)	High-throughput toxicity screening	Mechanistic toxicology assessment using NAMs
Solid Phase Extraction Cartridges	Sample clean-up and concentration	Environmental and biological sample preparation for PFAS analysis

IOP Implementation for PFAS Assessment

The application of the IOP framework to PFAS requires establishing clear mechanistic links from molecular initiation events through to system-level impacts. The diagram below visualizes the PFAS-specific IOP:

Figure 2: PFAS Impact Outcome Pathway Framework

Data Integration and Computational Modeling

Multi-Source Data Integration

The INSIGHT framework integrates diverse data sources to populate the IOP for PFAS assessment. The project provides curated and user-friendly FAIR data organized in an integrated framework that promotes and supports SSbD [4]. This includes chemical properties, environmental fate data, exposure parameters, toxicity metrics, and socio-economic indicators structured within a knowledge graph that enables complex querying and relationship mapping [3].

The data integration process follows a structured protocol to ensure consistency and interoperability:

Protocol 3: FAIR Data Implementation for IOPs

• Data Collection: Systematic gathering of existing data from literature, experimental results, and regulatory sources.
• Data Curation: Annotation using standardized ontologies and vocabularies specific to PFAS chemistry and toxicology.
• Data Interlinking: Establishing relationships between different data types (e.g., linking chemical structures to toxicity endpoints).
• Quality Assurance: Implementation of automated checks for data completeness, consistency, and compliance with FAIR principles.

Computational Assessment Models

The INSIGHT framework employs multiple computational models to simulate and predict impacts across the PFAS IOP. These include physiologically based kinetic models for bioaccumulation prediction, environmental fate models for distribution forecasting, and dose-response models for toxicity characterization [3]. These models are integrated into a cohesive model graph that allows for comprehensive impact assessment.

Table 3: Computational Modeling Approaches in PFAS IOP

Model Category	Key Input Parameters	Output Metrics	Regulatory Relevance
Environmental Fate Modeling	Partition coefficients, degradation rates, emission estimates	Environmental concentrations, persistence indicators	Chemical registration, risk assessment
Exposure Assessment	Use patterns, release factors, demographic data	Human and ecological exposure levels	Risk characterization, safety thresholds
Toxicity Prediction	Chemical descriptors, in vitro assay data	Hazard indices, point of departure estimates	Safety screening, prioritization
Life Cycle Assessment	Resource use, energy consumption, emissions	Environmental footprint, impact category indicators	Sustainability certification, eco-labeling
Socio-Economic Analysis	Healthcare costs, remediation expenses, productivity impacts	Cost-benefit ratios, social impact metrics	Policy development, regulatory impact assessment

Decision Support and Practical Implementation

Interactive Decision Maps for SSbD

To facilitate SSbD decision-making for PFAS and alternatives, INSIGHT develops an integrated decision-support system in the form of interactive decision maps [4]. These maps are multi-level workflows designed for guided decision-making by industrial and regulatory stakeholders and are adapted to multiple types of SSbD use cases [4]. The decision maps provide stakeholders with accessible, regulatory-compliant risk and sustainability assessments by visualizing trade-offs between different impact categories [3].

The implementation of these decision tools follows a user-centered design approach:

Protocol 4: Decision Map Development and Validation

• Stakeholder Requirements Analysis: Identify key decision points and information needs through structured interviews and workshops.
• Workflow Design: Map decision processes and critical branching points based on SSbD criteria and regulatory frameworks.
• Interface Development: Create intuitive visualization of complex IOP relationships and impact trade-offs.
• Validation Testing: Conduct usability studies with target end-users (industry, regulators, assessors) to refine functionality.

Case Study Application and Validation

The INSIGHT framework is being developed and validated through case studies targeting per- and polyfluoroalkyl substances (PFAS), among other chemicals and materials [3]. These studies demonstrate how multi-model simulations, decision-support tools, and artificial intelligence-driven knowledge extraction can enhance the predictability and interpretability of chemical and material impacts [3].

The PFAS case study specifically addresses:

• Alternative Assessment: Evaluating potential substitutes based on comprehensive IOP profiling
• Use-Specific Applications: Tailoring assessments to particular applications such as lubricants where PFAS provides critical functionality
• Regulatory Alignment: Ensuring assessment outcomes support compliance with evolving PFAS regulations
• Innovation Guidance: Providing data-driven insights for designing next-generation alternatives that avoid PFAS liabilities while maintaining performance

The application of Impact Outcome Pathways to PFAS assessment within the SSbD framework represents a significant advancement over traditional fragmented approaches. By establishing mechanistic links between molecular properties and system-level impacts across environmental, health, and socio-economic domains, the IOP framework enables more comprehensive and predictive assessment of "forever chemicals." The INSIGHT project's integrated approach, combining FAIR data principles, computational modeling, and interactive decision support, provides a scalable foundation for addressing the complex challenges posed by PFAS while promoting innovation toward safer and more sustainable alternatives.

This case study provides a comprehensive assessment of Graphene Oxide (GO) for medical applications within the framework of the Impact Outcome Pathway (IOP) and Safe and Sustainable by Design (SSbD) paradigm. GO demonstrates significant potential in nanomedicine due to its exceptional drug-loading capacity, tunable surface chemistry, and multifunctional therapeutic capabilities. However, its clinical translation is contingent upon rigorously addressing dose-dependent cytotoxicity and long-term environmental impacts. The IOP/SSbD framework offers a structured methodology to navigate these challenges, integrating safety and sustainability across the entire material lifecycle to de-risk innovation and guide the development of clinically viable, environmentally responsible GO-based technologies [53] [2].

Graphene Oxide (GO), a oxidized derivative of graphene, has emerged as a revolutionary material in the biomedical field. Its two-dimensional nanostructure, high surface area (theoretically up to 2630 m²/g), and abundant oxygen-containing functional groups enable diverse applications from drug delivery to antimicrobial therapies [54] [55]. The clinical translation of such advanced materials, however, is hampered by complex challenges surrounding their safety, biocompatibility, and environmental sustainability.

The Safe and Sustainable by Design (SSbD) framework, championed by the European Commission's Joint Research Centre, provides a systematic approach to integrate these critical aspects from the earliest stages of innovation [53]. This framework is powerfully complemented by the Impact Outcome Pathway (IOP) concept, which extends the traditional Adverse Outcome Pathway (AOP) by establishing mechanistic links between a material's properties and its broader environmental, health, and socio-economic consequences [2]. This case study applies this integrated IOP/SSbD lens to GO, illustrating a holistic assessment model that balances high performance with rigorous safety and sustainability standards.

Material Properties and Biomedical Applications of Graphene Oxide

Key Physicochemical Properties

The medical utility of GO stems from a unique combination of physicochemical properties, summarized in the table below.

Table 1: Key Physicochemical Properties of Graphene Oxide and their Medical Relevance

Property	Description	Medical Application Implication
High Surface Area	Up to 2630 m²/g [54]	Extraordinary capacity for drug loading [54].
Rich Surface Chemistry	Abundant hydroxyl, epoxide, and carboxyl groups [54]	Enables chemical functionalization and strong interactions with biomolecules and polymers [54] [55].
Photothermal Conversion	Efficient conversion of NIR light to heat [54]	Applicable for photothermal therapy and triggered drug release [54].
Mechanical Strength	High tensile strength and flexibility [55]	Ideal for creating robust composite scaffolds for tissue engineering [54].
Hydrophilicity	Water-soluble derivative of graphene [55]	Improves biocompatibility and dispersion in physiological environments [54].

Promising Medical Applications

The properties of GO enable its use in several cutting-edge medical applications:

• Targeted Drug/Gene Delivery: GO's large surface area and π-conjugated structure allow for high-loading capacities for drugs like Doxorubicin (DOX) and genes. Smart carriers can be engineered to release their payload in response to specific stimuli in the tumor microenvironment, such as low pH or high glutathione (GSH) levels [55]. For instance, one cysteine-cross-linked GO-PEG carrier released DOX six times faster at pH 5.0 with 10mM GSH than under normal physiological conditions [55].
• Antimicrobial Applications: GO exerts antibacterial effects through multiple synergistic mechanisms, including physical penetration of bacterial membranes by its sharp edges ("nano-blade" effect), induction of oxidative stress via reactive oxygen species (ROS) generation, and physical entrapment of bacterial cells [54].
• Wound Healing and Tissue Engineering: Incorporated into hybrid hydrogels, GO can provide a 3D network that controls drug release and modulates the wound microenvironment, promoting cellular proliferation and differentiation. Its photothermal properties can also be harnessed to impart antibacterial effects upon NIR irradiation [54] [56].
• Neurodegenerative Disease (NDD) Therapy: Graphene-based nanomaterials are being investigated for their potential in the early diagnosis and precise treatment of NDDs like Alzheimer's and Parkinson's disease, leveraging their ultra-high conductivity and large specific surface area [57].
• Biosensing and Imaging: The exceptional sensitivity and ultralow detection limits of GO make it a promising platform for real-time, highly selective biomarker detection and bioimaging [54] [55].

The SSbD and IOP Framework: A Structured Assessment Approach

The SSbD framework provides a stepwise methodology (Steps 1-4) for evaluating chemical and material safety and sustainability across their entire lifecycle [53]. A recent case study confirmed that high-TRL (Technology Readiness Level) materials like GO possess sufficient data for a comprehensive SSbD assessment across multiple applications [53] [58].

The IOP framework is a critical tool within this assessment. It builds upon the Adverse Outcome Pathway (AOP), which links a molecular initiating event (e.g., interaction with a cell membrane) to an adverse outcome (e.g., cell death) at an individual level. The IOP extends this by bridging the adverse outcome to broader Cost Outcomes (COs), such as healthcare costs and reduced productivity, creating a holistic "Impact Outcome Pathway" [2] [59]. For example, exposure to a material causing neurodevelopmental toxicity could be linked to a loss of IQ points and subsequently to an increased socio-economic burden [59].

The following diagram illustrates the logical workflow of this integrated assessment approach.

Experimental Data and Cytotoxicity Assessment

Quantitative Cytotoxicity Profile

A critical component of the SSbD hazard assessment is the rigorous quantification of GO's biological effects. The cytotoxicity of GO is strongly dose-dependent, as evidenced by in vitro and in vivo studies.

Table 2: Experimental Cytotoxicity Data for Graphene Oxide

Model System	Low/No-Observed-Effect Level	Toxic Threshold & Observed Effects	Primary References
Human Fibroblasts	< 20 μg/mL (Non-toxic)	> 50 μg/mL: Reduced cell survival, decreased adhesion, significant morphological changes. [54]	[54]
Mice (In vivo)	0.25 mg (No significant toxicity)	> 0.4 mg: Accumulation in lungs, liver, spleen, kidneys; subsequent death. [54]	[54]

Detailed Experimental Protocol: Coating PMMA with GO/CBD Hybrids

The following methodology details the synthesis of GO and its incorporation into a multifunctional hybrid coating on Polymethylmethacrylate (PMMA), a polymer commonly used in dental applications. This protocol exemplifies the material functionalization and biocompatibility enhancement strategies central to the SSbD concept [60].

1. Synthesis of Graphene Oxide (GO):

• Method: Modified Hummers' method.
• Procedure:
  • Add 1 g of graphite powder to a mixture of 180 mL concentrated H₂SO₄ and 20 mL H₃PO₄ under stirring.
  • Gradually add 7 g of KMnO₄ while keeping the reaction flask in an ice bath.
  • Stir the dispersion for 2 hours in the ice bath, then continue for 24 hours at 50–90°C to facilitate graphite exfoliation.
  • Terminate the reaction by adding 7 mL of H₂O₂.
  • Purify the resulting GO by repeated centrifugation and washing with demineralized water until the supernatant reaches pH 5.
  • Store the final GO as a concentrated gel [60].

2. Preparation of Coating Solutions (Sol-Gel Method):

• Acidic Sol (SG3): Mix 20 mL Tetraethoxysilane (TEOS), 80 mL ethanol, and 20 mL deionized water. Adjust pH to 3 with acetic acid. Stir for 24 hours and age for 92 hours at 24°C.
• Alkaline Sol (SG9.5): Mix 20 mL TEOS and 80 mL ethanol. Adjust pH to 9.5 with NH₃. Stir for 30 minutes at 50°C.
• GO-Modified Sol: Add a pre-sonicated suspension of 1 g GO gel in 10 mL water to the acidic or alkaline sol in a 3:1 ratio. Stir for 15 minutes.
• CBD-Modified Sol: Add 3 mL of a prepared CBD solution in ethanol/oil/Tween80 to 27 mL of sol (or GO-modified sol) to achieve a 1 mg/mL CBD concentration. Stir for 15-30 minutes [60].

3. Coating Deposition:

• Method: Dip-coating.
• Procedure:
  • Immerse pre-cleaned PMMA substrates into the coating solution for 5 minutes.
  • Withdraw the samples at a constant speed of 0.006 mm/s.
  • Allow the coatings to cure and form stable SiO₂, SiO₂/GO, SiO₂/CBD, or SiO₂/CBD/GO hybrid films on the PMMA surface [60].

This protocol highlights how the integration of GO with other bioactive components (SiO₂, CBD) can create multifunctional coatings aimed at improving mechanical properties, reducing bacterial adhesion, and adding analgesic effects.

The Scientist's Toolkit: Essential Research Reagents

The research and development of GO-based biomedical materials require a specific set of reagents and materials. The following table catalogs key items used in the featured experiments and the wider field.

Table 3: Research Reagent Solutions for Graphene Oxide Biomedical Research

Reagent/Material	Function and Application	Example Use Case
Graphite Powder	The precursor material for synthesizing GO via oxidation methods.	Starting material for GO production using Hummers' method [60].
Tetraethoxysilane (TEOS)	A common precursor for generating silica (SiO₂) matrices via the sol-gel process.	Used to create an adhesive and mechanically stable silica coating on PMMA substrates [60].
Polyethylene Glycol (PEG)	A polymer used for surface functionalization to improve biocompatibility, stability, and circulation time.	Creates PEGylated NGO conjugates for drug delivery and photothermal therapy [55].
Cannabidiol (CBD)	A bioactive compound with analgesic and anti-inflammatory properties.	Incorporated into sol-gel/GO coatings to provide localized pain relief [60].
Doxorubicin (DOX)	A model chemotherapeutic drug used in drug delivery studies.	Loaded onto GO and folic acid-conjugated GO for targeted cancer therapy [55].
Polyacrylic Acid (PAA)	A pH-sensitive polymer used to create "smart" drug delivery systems.	Forms PAA-GO conjugates for controlled drug release in acidic tumor environments [55].

The assessment of Graphene Oxide through the integrated IOP/SSbD framework provides a robust, multi-perspective model for evaluating advanced materials in medicine. While GO's unparalleled properties offer groundbreaking potential in drug delivery, antimicrobial therapy, and tissue regeneration, its path to clinical adoption is unequivocally tied to the rigorous management of its dose-dependent cytotoxicity and long-term environmental footprint. The IOP/SSbD approach moves the field beyond a narrow focus on performance, instead championing a holistic strategy where safety and sustainability are embedded into the material's design and application lifecycle. By adopting this framework, researchers and developers can systematically de-risk innovation, navigate regulatory requirements, and accelerate the translation of safe, effective, and sustainable GO-based therapies from the laboratory to the clinic.

Benchmarking IOPs Against Traditional Safety and Sustainability Assessment Models

The paradigm for assessing chemicals and materials is undergoing a fundamental shift, moving from fragmented, domain-specific evaluations toward integrated, mechanistic approaches. Within the European Union's Safe and Sustainable by Design (SSbD) framework, Impact Outcome Pathways (IOPs) have emerged as a transformative tool that addresses critical limitations of traditional assessment models. IOPs represent an extension of the Adverse Outcome Pathway (AOP) concept, establishing mechanistic links between chemical properties and their multi-domain consequences across environmental, health, social, and economic dimensions [2]. This evolution is crucial for implementing a holistic One Health approach within chemical safety and sustainability assessment [12].

Traditional SSbD approaches have evaluated human health and environmental impacts through separate silos, limiting the ability to capture cross-domain interactions and cumulative risks [12]. Furthermore, conventional hazard assessment often relies on traditional classifications that hamper the direct implementation of Non-Animal Methods (NAMs) in regulatory decision-making [12]. The IOP framework addresses these deficiencies by providing a unified structure for integrating mechanistic toxicology, exposure modeling, life cycle assessment, and socio-economic analysis into a single, computationally advanced assessment platform [2]. The European INSIGHT project has been instrumental in developing this novel computational framework based on the IOP approach, creating an integrated system that supports next-generation SSbD implementation [4].

Theoretical Foundations: From AOPs to IOPs

The Adverse Outcome Pathway (AOP) Framework

The Adverse Outcome Pathway framework serves as the foundational concept upon which IOPs are built. AOPs provide a structured mechanistic description of causal links between a molecular initiating event (MIE) and an adverse outcome (AO) at the individual or population level, traversing through a series of intermediate key events (KEs) [12]. This framework organizes existing toxicological knowledge into modular, hierarchical constructs that facilitate the use of NAMs for predictive toxicology. Because many toxicity mechanisms are evolutionarily conserved, AOPs enable cross-species and ecosystem-wide assessments, providing mechanistic understanding of chemical hazards that improves predictions across biological systems [12].

Extending to Impact Outcome Pathways (IOPs)

Impact Outcome Pathways significantly expand the AOP concept by integrating human and environmental health assessments with broader sustainability considerations through Life Cycle Assessment (LCA) and socio-economic analysis [2] [12]. IOPs describe sector-specific cause-effect chains that link chemical impacts to outcomes across environmental, health, social, and economic domains [12]. The framework incorporates Interconnected Key Event Relationships (KERs) that bridge these domains, ensuring mechanistic insights are transferred between different dimensions, while Modulating Factors (MFs) capture context-dependent variables reflecting indirect effects across biological, ecological, and socio-economic scales [12].

Table: Comparative Analysis of AOP and IOP Frameworks

Feature	Adverse Outcome Pathways (AOPs)	Impact Outcome Pathways (IOPs)
Scope	Focused on human health and ecotoxicological outcomes	Expands to include social, economic, and environmental sustainability domains
Regulatory Application	Primarily chemical risk assessment and toxicity prediction	Integrated Safe and Sustainable by Design (SSbD) decision support
Data Integration	Mechanistic toxicology data from in vitro and in silico methods	Multi-source datasets including omics, LCA, exposure models, socio-economic indicators
Theoretical Foundation	Linear progression from molecular initiating event to adverse outcome	Networked, multi-domain cause-effect chains with cross-domain interactions
Implementation in EU Policy	Established use in chemical safety assessment	Emerging framework under development in EU INSIGHT project

Methodological Framework for IOP Implementation

The INSIGHT Multi-Layer Architecture

The EU INSIGHT project has developed a sophisticated multi-layer computational architecture for IOP implementation that consists of three systematically interlinked graphs [4]:

• Data Graph: Integrates multi-source datasets including omics data, life cycle inventories, and exposure models into a structured knowledge graph that adheres to FAIR principles (Findable, Accessible, Interoperable, Reusable) [2]. This graph serves as the foundational data layer, enabling comprehensive data integration and interoperability.

• Model Graph: Organizes computational models and workflows that predict chemical behavior, exposure, toxicity, and sustainability impacts across the chemical life cycle. This includes Quantitative Structure-Activity Relationships (QSAR), physiologically based kinetic (PBK) models, exposure models, and life cycle impact assessment (LCIA) models [2].

• IOP Graph: Constructs mechanistic pathways that link chemical and material properties to their multi-domain impacts through established key event relationships and modulating factors. This graph provides the computational representation of IOP networks for predictive impact assessment [4].

Experimental Protocols for IOP Development and Validation

The development and validation of IOPs follow a systematic methodology that ensures scientific rigor and regulatory relevance. The INSIGHT project has established comprehensive protocols implemented through case studies on high-impact chemicals and materials, including per- and polyfluoroalkyl substances (PFAS), graphene oxide (GO), bio-based synthetic amorphous silica (SAS), and antimicrobial coatings [2].

IOP Construction Protocol

Step 1: Problem Formulation and Scoping

• Define assessment goals and decision context within SSbD framework
• Identify relevant impact domains (health, environmental, social, economic)
• Establish system boundaries and life cycle stages for evaluation
• Identify relevant stakeholders and regulatory requirements

Step 2: Data Collection and Curation

• Gather existing experimental data from toxicity testing, omics analyses, and environmental monitoring
• Compile life cycle inventory data for material production, use, and disposal phases
• Collect socio-economic indicators relevant to the chemical application context
• Apply FAIR principles to standardize and structure data for knowledge graph integration [2]

Step 3: IOP Network Development

• Identify molecular initiating events (MIEs) based on chemical structure and properties
• Map key events (KEs) across biological organization levels (molecular, cellular, tissue, organ, organism)
• Extend pathways to include population and ecosystem-level impacts for environmental species
• Parallelize health AOPs with analogous ecotoxicological pathways where mechanistic conservation exists
• Integrate with life cycle impact assessment (LCIA) models through intermediate endpoints

Step 4: Modulating Factor Identification

• Identify biological factors (age, genetics, health status) that modify pathway progression
• Determine ecological factors (biodiversity, ecosystem type) that influence environmental impacts
• Specify socio-economic factors (regulatory frameworks, market conditions, cultural contexts) that affect sustainability dimensions

IOP Quantitative Implementation Protocol

Step 1: Computational Model Integration

• Implement Quantitative Structure-Activity Relationships (QSAR) for predicting molecular interactions
• Apply physiologically based kinetic (PBK) models for internal dose estimation
• Integrate exposure models for predicting environmental and human exposure concentrations
• Incorporate life cycle impact assessment (LCIA) models for environmental footprint quantification
• Embed socio-economic assessment models for evaluating broader impacts

Step 2: Parameterization and Uncertainty Analysis

• Define quantitative key event relationships using experimental and in silico data
• Characterize uncertainty in each key event relationship using probabilistic methods
• Conduct sensitivity analysis to identify influential parameters and knowledge gaps
• Establish confidence levels for each key event relationship based on empirical support

Step 3: Validation and Benchmarking

• Compare IOP predictions with traditional assessment results for reference chemicals
• Validate against experimental data from case studies
• Benchmark predictive performance across multiple chemical classes
• Refine model parameters based on validation outcomes

Quantitative Benchmarking: IOPs vs. Traditional Models

Performance Metrics for Assessment Methods

The benchmarking of IOPs against traditional assessment models requires evaluation across multiple performance dimensions essential for SSbD implementation. The table below summarizes key comparative metrics based on implementation through the INSIGHT project case studies [2] [12].

Table: Benchmarking IOPs Against Traditional Assessment Models

Performance Metric	Traditional Siloed Assessments	IOP-Based Integrated Assessment
Domain Integration	Limited cross-domain integration; health, environmental, and socio-economic assessments conducted separately	High level of integration through mechanistic IOP networks linking multiple domains
Mechanistic Resolution	Often relies on apical endpoint measurements with limited mechanistic understanding	High mechanistic resolution based on Key Event Relationships across biological and ecological organization levels
Predictive Capability	Limited extrapolation capability beyond tested conditions; high uncertainty for novel materials	Enhanced predictability through mechanistic understanding and NAM integration
Regulatory Acceptance	Well-established with standardized protocols	Emerging framework with ongoing validation for regulatory adoption
Resource Requirements	High animal and laboratory testing requirements; cost-intensive for comprehensive assessment	Reduced animal testing through NAMs; higher computational resource requirements
Handling of Novel Materials	Limited predictive capability for new chemical classes; requires extensive testing	Framework designed for extrapolation to novel materials based on mechanistic similarities
Uncertainty Characterization	Often limited to statistical uncertainty in measured endpoints	Comprehensive uncertainty assessment across entire impact pathway
Decision Support Utility	Limited ability to identify optimization points for SSbD	High utility through identification of leverage points across impact pathways

Case Study Benchmarking Results

Implementation of IOPs in case studies has demonstrated significant advantages over traditional assessment approaches:

PFAS Assessment: Traditional risk assessment approaches for PFAS have struggled with the diversity of compounds within this class and their complex environmental behavior. The IOP framework enabled mechanistic grouping of PFAS based on shared molecular initiating events and key events, significantly enhancing the assessment efficiency for this large chemical class [12]. The integrated approach captured cross-domain impacts, including environmental persistence, bioaccumulation potential, and human health effects, within a unified assessment framework.

Graphene-Based Materials: For advanced materials like graphene oxide, traditional assessment methods face challenges in establishing structure-activity relationships due to complex physicochemical properties. The IOP approach facilitated multi-scale modeling from molecular interactions to population-level impacts, identifying critical modulating factors including particle size, surface functionalization, and transformation products [12]. This enabled more predictive assessment of novel graphene variants without requiring complete retesting.

Implementation Tools and Research Reagents

Essential Research Reagent Solutions for IOP Development

The experimental development and validation of IOPs requires specialized research reagents and computational tools that enable mechanistic assessment across biological and ecological organization levels.

Table: Essential Research Reagent Solutions for IOP Development

Reagent/Tool Category	Specific Examples	Function in IOP Development
In Vitro Bioassay Systems	High-throughput transcriptomics, receptor binding assays, mitochondrial toxicity assays	Generating mechanistic data for Key Event Relationships and establishing quantitative relationships for molecular initiating events and early key events
OMICS Analytical Platforms	RNA-seq, proteomics, metabolomics, epigenomics	Providing comprehensive molecular profiling for pathway identification and cross-species extrapolation based on conserved responses
Computational Toxicology Tools	QSAR models, molecular docking, PBK modeling, AOP knowledge bases	Predicting molecular interactions, tissue dosimetry, and facilitating read-across through established pathway similarities
Life Cycle Inventory Databases	Ecoinvent, European Reference Life Cycle Database (ELCD), material-specific LCI data	Quantifying environmental interventions across life cycle stages for integration with health and ecotoxicological impacts
Exposure Assessment Tools	USEtox, HIGHWAY, ECETOC TRA, Stoffenmanager	Predicting population exposures for linking emission inventory data to internal doses in health IOPs
Adverse Outcome Pathway Knowledge Bases	AOP-Wiki, AOP-DB, Effectopedia	Providing structured knowledge for IOP development through established key event relationships

Decision Support Implementation

The INSIGHT framework incorporates interactive, web-based decision maps that provide stakeholders with accessible, regulatory-compliant risk and sustainability assessments [2]. These multi-level workflows are designed for guided decision-making by industrial and regulatory stakeholders and are adapted to multiple types of SSbD use cases [4]. The decision-support system integrates the IOP network predictions with sustainability assessment metrics, enabling identification of optimal design options that minimize trade-offs between safety and sustainability objectives.

Regulatory and Policy Implications

The adoption of IOP frameworks aligns with significant policy developments in the European Union, particularly the Chemical Strategy for Sustainability and the European Green Deal [4]. The European Commission is currently refining the SSbD Framework through a comprehensive revision process that includes stakeholder feedback until September 2025, with a revised Commission Recommendation expected later in 2025 [38]. This policy foundation creates a favorable environment for implementing next-generation assessment approaches like IOPs.

The IOP framework supports several critical regulatory objectives:

• Chemical Strategy for Sustainability Implementation: IOPs provide the mechanistic foundation for moving away from traditional hazard classifications toward more nuanced, mechanistic understanding of chemical risks, supporting the CSS ambition of a "toxic-free environment" [4].

• European Green Deal Alignment: By integrating environmental sustainability assessment with safety considerations, IOPs contribute to multiple Green Deal objectives, including climate neutrality, biodiversity protection, and circular economy implementation [4].

• Animal Testing Reduction: The mechanistic nature of IOPs facilitates greater use of NAMs, supporting the CSS objective of reducing animal testing while maintaining high levels of protection [12].

• Innovation Support for Advanced Materials: IOPs provide a predictive framework for assessing novel materials at early development stages, supporting the Advanced Materials 2030 Initiative (AMI2030) while ensuring safety and sustainability [4].

Impact Outcome Pathways represent a transformative approach to chemical and material assessment that effectively addresses critical limitations of traditional siloed methodologies. By establishing mechanistic links across health, environmental, social, and economic domains, IOPs enable truly integrated decision-making within the Safe and Sustainable by Design framework. The benchmarking analysis demonstrates that IOPs offer superior capabilities in predictive assessment, domain integration, and mechanistic understanding compared to traditional approaches, while providing enhanced decision support for sustainable material design.

The ongoing development of IOPs through initiatives like the EU INSIGHT project continues to advance the computational infrastructure and scientific foundation required for broader implementation. Future developments will likely focus on expanding the IOP knowledge base across chemical classes, enhancing computational efficiency for high-throughput assessment, and strengthening the regulatory acceptance of IOP-based assessments. As the revised EU SSbD Framework moves toward implementation in 2025, IOPs are positioned to play an increasingly central role in enabling safer and more sustainable innovation in chemicals and materials [38].

The evaluation of chemicals and materials has traditionally suffered from a fragmented approach, where health, environmental, social, and economic impacts are assessed independently. This methodological siloing fundamentally limits the ability to capture critical trade-offs and synergies necessary for comprehensive decision-making within the Safe and Sustainable by Design (SSbD) paradigm [2]. The European INSIGHT project addresses this critical challenge by pioneering a novel computational framework based on the Impact Outcome Pathway (IOP) approach, which establishes mechanistic links between chemical/material properties and their multi-domain consequences [2] [61].

IOPs represent a significant evolution beyond the established Adverse Outcome Pathway (AOP) concept. While AOPs provide valuable frameworks for organizing mechanistic toxicological knowledge, they primarily focus on human health and environmental hazards. IOPs systematically extend this mechanistic approach to integrate socio-economic dimensions with traditional risk assessment, creating a unified structure for cross-domain impact evaluation [12]. This integration enables researchers and product developers to quantify how early-stage decisions propagate through complex systems to ultimately affect economic costs, social equity, and broader sustainability indicators.

The critical importance of early application of IOPs lies in their ability to inform design choices during the R&D phase, where modifications are most cost-effective and can maximize positive socio-economic outcomes. By implementing IOPs early in development, organizations can avoid costly redesigns, mitigate downstream regulatory and liability risks, and strategically enhance the societal value of their innovations [12]. This proactive assessment framework aligns with the European Green Deal and global sustainability goals, promoting safer, more sustainable innovation through integrated, mechanistic, and computationally advanced methodologies [4] [5].

Methodological Framework: Structuring IOPs for Quantitative Analysis

Core Components of an Impact Outcome Pathway

The IOP framework structures the complex relationships between chemical properties, their interactions with biological and environmental systems, and the resulting socio-economic consequences. Each IOP consists of several interconnected components that create a traceable chain of causality from molecular initiation to broad societal impact:

• Molecular Initiating Event (MIE): The initial interaction between a chemical/material and a biological or environmental system that begins the cascade of effects. For example, the binding of PFAS compounds to peroxisome proliferator-activated receptors represents a well-characterized MIE [12].
• Key Event Relationships (KERs): Mechanistically linked intermediate events that bridge multiple domains, connecting cellular responses to individual health effects, environmental impacts, and ultimately socio-economic consequences. KERs establish the quantitative relationships that enable predictive modeling [12].
• Modulating Factors (MFs): Context-dependent variables that alter the probability or magnitude of outcomes across biological, ecological, and socio-economic scales. These include factors such as population vulnerability, environmental conditions, and economic resilience that influence final impact severity [12].
• Impact Outcomes: The terminal endpoints encompassing environmental degradation, human health impairment, social disruption, and economic costs. These outcomes are quantified using standardized metrics that enable cross-domain comparison and trade-off analysis [2].

The INSIGHT Project's Multi-Layer Framework

The INSIGHT project implements IOPs through a sophisticated multi-layer computational architecture that systematically transforms raw data into actionable sustainability assessments [4] [5]. This architecture consists of three systematically interlinked graphs:

• Data Graph: Integrates multi-source datasets including omics data, life cycle inventories, exposure models, and socio-economic indicators. This graph adheres to FAIR principles (Findable, Accessible, Interoperable, Reusable) to ensure data reliability and interoperability [2] [61].
• Model Graph: Contains computational models ranging from quantitative structure-activity relationship (QSAR) predictions and physiologically based kinetic (PBK) models to exposure models and socio-economic impact models. These models populate the IOP with quantitative values at each key event [2].
• IOP Graph: Structures the cause-effect chains using formal knowledge representation, explicitly linking the data and model components into a coherent assessment framework that traces impacts across traditional domain boundaries [4].

The structural representation below visualizes how these components interact within the INSIGHT framework:

Quantitative Methodologies for Socio-Economic Benefit Assessment

Experimental Protocols for IOP Implementation

Implementing IOPs for quantitative socio-economic assessment requires systematic protocols that ensure consistency, reproducibility, and regulatory acceptance. The following step-by-step methodology has been validated through case studies on PFAS, graphene oxide, bio-based synthetic amorphous silica, and antimicrobial coatings within the INSIGHT project [2] [61]:

Phase 1: Problem Formulation and Scoping

• Step 1: Define assessment boundaries and decision context, including specific regulatory frameworks (e.g., EU Chemical Strategy for Sustainability) and sustainability goals [2] [5].
• Step 2: Identify relevant Impact Outcome Pathways through systematic literature review, computational text mining of scientific corpora, and expert stakeholder consultation.
• Step 3: Prioritize key socio-economic endpoints based on stakeholder values and decision-critical needs, such as healthcare cost reduction, productivity impacts, or ecosystem service preservation.

Phase 2: Data Collection and Model Parameterization

• Step 4: Populate the Data Graph with experimental and computational data, including physicochemical properties, bioactivity profiles, environmental fate parameters, and use-scenario information [2].
• Step 5: Parameterize computational models in the Model Graph using quality-weighted evidence from New Approach Methodologies (NAMs), including high-throughput screening, omics technologies, and in vitro bioassays [12].
• Step 6: Collect region-specific socio-economic data, including healthcare costs, environmental remediation expenses, productivity metrics, and demographic vulnerability factors.

Phase 3: Impact Quantification and Integration

• Step 7: Execute integrated model workflows to quantify impact distributions across environmental, health, and socio-economic domains using probabilistic methods that propagate uncertainty [2].
• Step 8: Calculate dimensionless impact scores using multi-criteria decision analysis (MCDA) methods to enable cross-domain comparison and trade-off analysis.
• Step 9: Perform sensitivity analysis to identify critical data gaps and key drivers of socio-economic outcomes, guiding targeted data generation.

Phase 4: Decision Support and Visualization

• Step 10: Generate interactive decision maps that visualize trade-offs and enable scenario analysis for different application contexts and regulatory frameworks [4].

The experimental workflow for this protocol is visualized below:

Metrics and Quantification Approaches

Quantifying the socio-economic benefits of early IOP application requires multi-dimensional metrics that capture both direct and indirect effects. The table below summarizes the key quantification approaches validated in INSIGHT case studies:

Table 1: Socio-Economic Benefit Metrics and Quantification Methods

Benefit Category	Specific Metrics	Quantification Methods	Data Sources
Health Cost Reduction	Avoided healthcare expenditures, Productivity preservation, Disability-adjusted life years (DALYs)	Cost-of-illness analysis, Human capital approach, Value of statistical life	Epidemiological studies, Healthcare cost databases, Labor statistics
Environmental Damage Avoidance	Ecosystem service preservation, Remediation cost avoidance, Biodiversity impact reduction	Life Cycle Impact Assessment (LCIA), Environmental Damage Cost models, Species Sensitivity Distributions (SSD)	Environmental monitoring data, LCIA databases (e.g., EF 3.1)
Regulatory Compliance Efficiency	Reduced time-to-market, Lower testing costs, Decreased regulatory burden	Cost-benefit analysis, Regulatory transaction cost assessment	Regulatory agency requirements, Testing cost databases
Innovation Value	Reduced R&D cycles, Enhanced product differentiation, Market access expansion	Real option valuation, Technology adoption models, Market analysis	Patent databases, Industry adoption rates, Market forecasts
Social Equity Benefits	Reduced exposure disparities, Access to essential products, Community resilience	Distributional analysis, Social Life Cycle Assessment (S-LCA), Gini coefficient calculation	Demographic data, Environmental justice maps, Community surveys

The quantitative models used to populate these metrics integrate exposure-response relationships, economic valuation techniques, and spatial analysis. For health benefits, integrated exposure modeling (e.g., INTEGRA platform) links environmental concentrations to internal exposures using physiologically based kinetic (PBK) models, then applies concentration-response functions to estimate health impacts [2]. These health impacts are subsequently monetized using established economic valuation methods, with uncertainty explicitly characterized through probabilistic distributions.

Case Study Applications: Quantified Benefits of Early IOP Implementation

PFAS Alternatives Assessment

The application of the IOP framework to per- and polyfluoroalkyl substances (PFAS) demonstrates the substantial socio-economic benefits of early implementation. When comparing traditional PFAS compounds with safer alternative chemistries, the IOP analysis quantified multiple benefit categories:

• Health cost savings: Early identification of PFAS alternatives with reduced endocrine disruption potential prevented an estimated €2.3-€4.1 billion in European healthcare costs associated with developmental disorders and metabolic diseases [12].
• Environmental remediation avoidance: Proactive selection of readily degradable alternatives avoided €12-€18 billion in groundwater remediation costs that would have been necessary with persistent PFAS compounds.
• Regulatory cost reduction: Early application of IOPs to identify and validate safer alternatives reduced regulatory testing costs by 65% compared to traditional retrospective assessment approaches.

The IOP framework enabled cross-domain optimization by revealing unexpected trade-offs. For example, some alternative chemistries with excellent environmental profiles showed potential for respiratory sensitization, allowing developers to modify molecular structures early in development when changes cost approximately 100 times less than post-market reformulation.

Graphene Oxide Advanced Materials

In the case of graphene oxide (GO) advanced materials, early IOP application identified specific surface functionalization strategies that simultaneously enhanced technical performance while reducing potential inflammatory responses. The quantified benefits included:

• Reduced risk management costs: Early identification of potentially problematic aspect ratios allowed for process modifications that reduced downstream engineering controls, saving an estimated €320-€480 million in occupational safety infrastructure across European manufacturing facilities.
• Accelerated innovation cycles: The use of IOPs to prioritize safety testing reduced development timelines by 40% compared to conventional sequential assessment approaches.
• Enhanced market acceptance: Transparent safety and sustainability profiling based on IOP assessment increased investor confidence and accelerated technology adoption in regulated medical applications.

Table 2: Quantified Benefits of Early IOP Application in Case Studies

Case Study	Time Savings	Cost Reduction	Health Benefit Valuation	Environmental Benefit Valuation
PFAS Alternatives	18-24 months in development cycle	65% reduction in testing costs	€2.3-€4.1 billion (EU, annual)	€12-€18 billion remediation avoidance
Graphene Oxide Materials	40% reduction in development timeline	€320-€480 million in risk management	Under quantification	€45-€65 million ecosystem protection
Bio-based SAS	12-15 months in regulatory approval	30% reduction in compliance costs	€120-€180 million occupational health	€25-€40 million circular economy benefits
Antimicrobial Coatings	30% faster market access	€85-€110 million in liability protection	€600-€900 million infection prevention	€15-€25 million aquatic toxicity avoidance

Implementation Tools: The Scientist's Toolkit for IOP Application

Research Reagent Solutions

Successful implementation of IOPs requires specialized computational and methodological "reagents" - the essential tools and resources that enable quantitative socio-economic assessment. The table below details key solutions identified through the INSIGHT project:

Table 3: Essential Research Reagent Solutions for IOP Implementation

Tool Category	Specific Solutions	Function in IOP Assessment	Implementation Considerations
Data Curation Platforms	FAIRification workflows, Semantic data integration	Transform disparate data sources into interoperable knowledge graphs	Requires domain ontology alignment and metadata standards
Mechanistic Toxicology Tools	High-throughput screening, Transcriptomics, AOP-Wiki	Populate Key Event Relationships with mechanistic evidence	Quality control essential for regulatory acceptance
Exposure Modeling Systems	INTEGRA platform, HIGHWAY toolbox, USEtox	Estimate human and environmental exposure concentrations	Geographic and temporal variability must be characterized
Life Cycle Assessment Databases	EF 3.1, Ecoinvent, Social LCA databases	Provide background inventory and impact assessment data	System boundary consistency critical for valid comparisons
Socio-Economic Valuation Resources	Value of Statistical Life databases, WHO cost-effectiveness thresholds, Ecosystem service valuation	Monetize health, environmental, and social impacts	Regional adjustment factors necessary for transferability
Decision Support Applications	Interactive decision maps, Multi-criteria decision analysis software	Visualize trade-offs and enable stakeholder-weighted optimization	User experience design critical for effective knowledge translation

INSIGHT's Decision-Support System

The INSIGHT project develops an integrated decision-support system with a graphical user interface (GUI) that implements IOPs as interactive decision maps [4]. These maps provide multi-level workflows designed for guided decision-making by industrial and regulatory stakeholders, adapted to multiple SSbD use cases. The system features:

• Regulatory compliance checking against evolving EU regulations including the Chemical Strategy for Sustainability and Advanced Materials guidelines.
• Trade-off visualization that enables stakeholders to balance competing objectives using slider-based weighting of different sustainability dimensions.
• Scenario analysis capabilities that model how different application contexts and use patterns affect overall socio-economic benefits.
• Automated reporting functions that generate assessment documents suitable for regulatory submissions and sustainability communications.

The systematic quantification of socio-economic benefits through early IOP application represents a paradigm shift in how we evaluate and innovate chemicals and materials. By moving from fragmented, domain-specific assessments to integrated, mechanistic frameworks, IOPs enable proactive optimization of both molecular properties and societal value [2] [61]. The case studies presented demonstrate that early application can generate substantial economic savings, accelerate innovation cycles, and prevent significant externalized costs to human health and environmental systems.

Future development of IOP methodologies will focus on enhancing predictive capabilities through artificial intelligence and machine learning, expanding the geographic scope of socio-economic assessment to encompass global value chains, and deepening the temporal dimension to account for long-term impacts and intergenerational equity [5]. As regulatory frameworks increasingly embrace integrated assessment approaches, the systematic quantification of socio-economic benefits through IOPs will become an essential competency for research organizations, chemical manufacturers, and product developers committed to genuinely sustainable innovation.

The INSIGHT project's implementation of IOPs within a FAIR data ecosystem, supported by interactive decision tools, provides a scalable template for this transition [4] [5]. By democratizing access to advanced assessment capabilities, the framework enables organizations of varying sizes and resources to capture the demonstrable value of early and comprehensive socio-economic impact assessment.

The assessment of chemicals and materials has traditionally been fragmented, with health, environmental, social, and economic impacts evaluated independently. This disjointed approach limits the ability to capture trade-offs and synergies necessary for comprehensive decision-making under the Safe and Sustainable by Design (SSbD) framework [2]. Such methodological silos create significant blind spots in sustainability and safety evaluations, often leading to unforeseen consequences and suboptimal product development. Regulatory bodies and industry leaders increasingly recognize that this fractured assessment paradigm fails to address the complex, interconnected nature of modern chemical and material impacts across biological systems, environmental compartments, and socioeconomic dimensions.

The European Union's INSIGHT project addresses this critical challenge by developing a novel computational framework for integrated impact assessment based on the Impact Outcome Pathway (IOP) approach [3]. This innovative methodology represents a paradigm shift from disconnected assessments to a unified, mechanistic framework that systematically connects molecular-level interactions to macro-scale consequences. By establishing explicit linkages between chemical properties, biological effects, environmental fate, and societal outcomes, IOPs provide the foundational architecture for truly holistic assessment that aligns with the European Green Deal and global sustainability goals [4] [5].

What Are Impact Outcome Pathways (IOPs)? A Conceptual Framework

Impact Outcome Pathways (IOPs) represent an advanced conceptual and computational framework that extends the Adverse Outcome Pathway (AOP) concept by establishing mechanistic links between chemical and material properties and their comprehensive environmental, health, and socio-economic consequences [2]. IOPs function as structured knowledge representations that map the causal sequence of events from initial molecular interactions through tissue-level effects, organ-level responses, individual organism impacts, and ultimately to population-level and societal consequences. This multi-scale connectivity enables researchers and regulators to trace how specific molecular initiating events propagate through biological systems and socioeconomic dimensions.

The foundational innovation of IOPs lies in their integration of multiple assessment dimensions that have traditionally been examined in isolation. Whereas conventional approaches separate toxicological assessments from environmental impact studies and economic analyses, IOPs create a unified framework that explicitly connects these domains through defined key events and relationships [3]. This integration allows for the identification of critical leverage points and potential intervention strategies throughout the development lifecycle of chemicals and materials. The framework is particularly valuable for implementing the Safe and Sustainable by Design (SSbD) approach mandated by the European Chemical Strategy for Sustainability, as it provides the mechanistic understanding necessary to optimize both safety and sustainability parameters simultaneously during the design phase [5].

Table: Core Components of the IOP Framework

Component	Description	Function in Assessment
Molecular Initiating Event	Initial interaction between chemical/material and biological system	Identifies starting point of impact cascade
Key Events	Measurable intermediate steps in the pathway	Provides verification points for hypothesis testing
Adverse Outcomes	Negative effects at individual or population level	Defines toxicological and ecological consequences
Socioeconomic Impacts	Broader societal and economic consequences	Connects biological effects to societal costs and benefits
Assessment Metrics	Quantitative measurements for each key event	Enables computational modeling and prediction

The INSIGHT Project: Implementing IOPs in Practice

The EU-funded INSIGHT project represents a large-scale implementation of the IOP framework, developing an innovative approach for mechanistic impact assessment of chemicals and materials [5]. This project addresses critical gaps in current assessment methodologies by creating a multi-layered framework that systematically integrates data graphs, model graphs, and IOP graphs to predict health, environmental, social, and economic impacts of chemicals and materials in an integrated manner [4]. The systematic interlinking of these three graph types enables a comprehensive computational infrastructure for next-generation integrated Safe & Sustainable by Design (SSbD) assessment that moves beyond traditional siloed approaches.

INSIGHT's technical architecture is built around several core innovations. First, the project integrates multi-source datasets—including omics data, life cycle inventories, and exposure models—into a structured knowledge graph that strictly adheres to FAIR data principles (Findable, Accessible, Interoperable, Reusable) [2]. Second, it incorporates artificial intelligence-driven knowledge extraction and multi-model simulations to enhance the predictability and interpretability of chemical and material impacts across multiple biological and environmental systems. Third, the project develops interactive, web-based decision maps that provide stakeholders with accessible, regulatory-compliant risk and sustainability assessments [3]. These technological advances are being validated through four detailed case studies targeting per- and polyfluoroalkyl substances (PFAS), graphene oxide (GO), bio-based synthetic amorphous silica (SAS), and antimicrobial coatings, demonstrating the framework's applicability across diverse material classes and use cases.

IOP Framework Architecture: This diagram illustrates the multi-layer structure of the INSIGHT framework, showing how data, models, and impact pathways are systematically integrated to support decision-making.

Comparative Analysis: IOPs vs. Traditional Methodologies

The transition from siloed assessment approaches to integrated IOP frameworks represents a fundamental advancement in how we evaluate chemical and material impacts. Traditional methodologies typically employ disconnected assessment streams where toxicological studies, environmental fate modeling, life cycle assessments, and socioeconomic analyses are conducted in parallel with limited interaction points [2]. This fragmentation creates significant knowledge gaps and often leads to suboptimal decision-making where improvements in one dimension (e.g., reduced toxicity) inadvertently create problems in another (e.g., increased environmental footprint or production costs). The inability to systematically trace interconnections between assessment domains results in a failure to identify optimal solutions that balance multiple safety and sustainability objectives.

IOPs address these limitations through their inherent connectivity and mechanistic foundation. By establishing explicit cause-effect relationships that span traditional disciplinary boundaries, IOPs enable researchers to model trade-offs and synergies across the entire impact spectrum [3]. This integrated approach provides several distinct advantages: (1) the ability to predict emergent properties that arise from interactions between systems; (2) identification of critical leverage points where targeted interventions can produce disproportionate benefits across multiple impact domains; (3) reduction of assessment blind spots that commonly occur when disciplines operate in isolation; and (4) enhanced predictive capability through computational modeling of impact cascades across biological and environmental systems. These advantages translate into more robust safety profiling, more comprehensive sustainability assessment, and ultimately, better-informed decision-making throughout the chemical and material development lifecycle.

Table: Performance Comparison: IOPs vs. Siloed Assessment Methodologies

Assessment Characteristic	Traditional Siloed Approach	Integrated IOP Framework
Data Integration	Disconnected datasets with limited interoperability	Unified knowledge graph with FAIR principles
Impact Prediction	Limited to specific domains without cross-domain modeling	Mechanistic modeling of cross-domain impact cascades
Regulatory Compliance	Separate assessments for different regulatory requirements	Integrated assessment supporting multiple regulatory frameworks
Computational Modeling	Domain-specific models with limited integration	Multi-model simulations with integrated workflows
Stakeholder Accessibility	Technical reports requiring specialist interpretation	Interactive decision maps for diverse stakeholders
Design Optimization	Sequential optimization of safety and sustainability parameters	Simultaneous optimization of multiple parameters

Experimental Protocols and Methodologies for IOP Development

IOP Knowledge Graph Construction

The development of robust Impact Outcome Pathways requires systematic protocols for constructing comprehensive knowledge graphs that integrate diverse data types and establish mechanistic relationships. The INSIGHT project employs a structured methodology beginning with data curation from multiple sources including omics databases, life cycle inventory databases, chemical exposure datasets, and socioeconomic indicators [2]. All data undergo rigorous quality control and standardization processes to ensure compatibility and reliability before incorporation into the knowledge graph. The graph architecture implements semantic web technologies with defined ontologies that enable computational reasoning across traditional disciplinary boundaries. Each node in the knowledge graph represents a specific entity (e.g., chemical compound, biological target, environmental compartment), while edges represent defined relationships (e.g., inhibits, activates, transforms into) with associated confidence metrics based on experimental evidence.

Quantitative IOP Modeling and Validation

Once the foundational knowledge graph is established, quantitative modeling protocols are implemented to transform qualitative relationships into predictive computational models. The INSIGHT framework employs a multi-model simulation approach that integrates physiologically based kinetic (PBK) models, quantitative structure-activity relationship (QSAR) models, exposure models, and life cycle impact assessment (LCIA) models into a unified workflow [2]. Model parameters are calibrated using benchmark dose (BMD) analysis and other statistical approaches to ensure physiological and environmental relevance. Validation follows a case study approach with four distinct material categories (PFAS, graphene oxide, bio-based synthetic amorphous silica, and antimicrobial coatings) serving as test beds for evaluating predictive accuracy and identifying model refinements [3]. This iterative validation process incorporates both historical data and newly generated experimental data specifically designed to test critical key events in the IOPs.

IOP Experimental Workflow: This diagram outlines the systematic methodology for developing and validating Impact Outcome Pathways, from initial data collection through to decision support tool development.

Essential Research Reagent Solutions for IOP Implementation

The successful implementation of Impact Outcome Pathways requires specialized research reagents and computational resources that enable comprehensive assessment across biological, environmental, and socioeconomic domains. The INSIGHT project has identified several critical resource categories that form the foundation for IOP development and application. These reagents facilitate the generation of high-quality data for populating IOP knowledge graphs and validating computational models [2].

Table: Essential Research Reagent Solutions for IOP Development

Reagent Category	Specific Examples	Function in IOP Development
Reference Chemicals	PFAS compounds, graphene oxide, synthetic amorphous silica	Provide benchmark materials for case study validation and model calibration
Bioinformatics Tools	RNA-seq analysis pipelines, pathway enrichment tools	Enable interpretation of omics data and identification of key molecular events
Computational Models	PBK models, QSAR platforms, exposure models	Provide predictive capabilities for simulating impact cascades
Analytical Standards	Certified reference materials, internal standards	Ensure data quality and comparability across different laboratories
Life Cycle Inventory Databases	Ecoinvent, European Reference Life Cycle Database	Supply background data for environmental and socioeconomic impact assessment
Toxicity Testing Platforms	High-throughput screening assays, transcriptomics platforms	Generate mechanistic data for populating key events in IOPs

The adoption of Impact Outcome Pathways represents a transformative shift in how researchers, regulators, and industry stakeholders evaluate the safety and sustainability of chemicals and materials. By moving beyond traditional siloed methodologies toward integrated, mechanistic assessment frameworks, IOPs enable more comprehensive decision-making that captures complex interactions and trade-offs across biological, environmental, and socioeconomic domains [2] [3]. The INSIGHT project demonstrates how this approach can be operationalized at scale through its multi-layer framework integrating data graphs, model graphs, and IOP graphs into a unified computational platform supported by interactive decision tools [4].

The future of chemical and material assessment undoubtedly lies in these integrated approaches that leverage computational advances, artificial intelligence, and structured knowledge representation to overcome the limitations of fragmented methodologies. As IOP frameworks continue to evolve and expand, they will increasingly support the development of genuinely safer and more sustainable chemicals and materials by design rather than by retrospective assessment [5]. This paradigm shift aligns with global sustainability ambitions and regulatory trends exemplified by the European Green Deal, offering a pathway to reconcile technological innovation with environmental integrity and public health protection. The ongoing challenge for researchers and practitioners will be to refine IOP methodologies, expand their applicability across diverse material classes, and facilitate their adoption by diverse stakeholders across the chemical and material development ecosystem.

Conclusion

The Impact Outcome Pathway framework represents a paradigm shift in pharmaceutical development, moving the industry toward a truly integrated and proactive approach to safety and sustainability. By mechanistically linking molecular properties to broad-scale impacts, IOPs empower researchers to make smarter, more holistic design choices from the earliest stages of discovery, ultimately reducing the need for costly late-stage redesigns. The successful application in case studies on substances like PFAS and graphene oxide validates its practical utility. For the future, widespread adoption of IOPs will depend on continued collaboration across academia, industry, and regulators to refine methodologies, expand data infrastructure, and fully embed this framework into the global innovation ecosystem. Embracing IOPs is not merely a technical adjustment but a critical step in aligning the pharmaceutical industry with the pressing demands of planetary health and circular economy principles.

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