Implementing the Safe and Sustainable-by-Design (SSbD) Framework: A Strategic Guide for Biomedical Research and Drug Development

Abstract

This article provides a comprehensive guide for researchers, scientists, and drug development professionals on the European Commission's Safe and Sustainable-by-Design (SSbD) framework. It explores the foundational principles of this voluntary approach designed to steer innovation towards safer, more sustainable chemicals and materials. The content covers methodological application, including iterative assessment steps from hazard evaluation to life-cycle analysis, addresses current operational challenges and optimization strategies, and compares key framework implementations. By synthesizing the latest research and stakeholder feedback, this guide aims to equip biomedical innovators with the knowledge to proactively integrate safety and sustainability throughout the R&D lifecycle, from early discovery to regulatory preparedness.

Understanding the SSbD Framework: Principles, Drivers, and Synergies with EU Legislation

The EU Chemicals Strategy for Sustainability (CSS) is a cornerstone of the European Union's ambitious European Green Deal, officially adopted by the European Commission on 14 October 2020 [1] [2]. This strategy emerged as a direct response to the escalating global production and use of chemicals, which is projected to double by 2030, increasing potential risks to human health and environmental integrity [1]. Despite the EU's sophisticated existing chemicals legislation, this growth trajectory necessitated a more transformative approach to chemical management.

The CSS forms an integral part of the EU's zero pollution ambition, representing one of the key commitments within the broader European Green Deal framework [1]. The Green Deal itself serves as the EU's comprehensive growth strategy, designed to transform Europe into a sustainable, resource-efficient, and competitive economy while addressing climate challenges and the economic consequences of global crises [2] [3]. Within this context, the Chemicals Strategy for Sustainability establishes a foundational action plan to fundamentally rethink chemical safety, innovation, and sustainability throughout product life cycles.

Strategic Goals and Core Objectives

The EU Chemicals Strategy for Sustainability is built upon several interconnected strategic goals that collectively aim to transform the chemical sector while enhancing protection for citizens and the environment.

Table: Strategic Goals of the EU Chemicals Strategy for Sustainability

Goal Category	Specific Objectives	Expected Outcomes
Health & Environmental Protection	Ban most harmful chemicals in consumer products [2]Address the "cocktail effect" of chemical mixtures [2]Phase out PFAS unless use is essential [2]	Reduced exposure to hazardous substancesBetter understanding of combined chemical risksElimination of persistent pollutants
Innovation & Competitiveness	Boost production of safe/sustainable chemicals [2]Promote chemicals safe/sustainable by design [4]	Transition to cleaner industrial processesEuropean leadership in green chemistry
Regulatory Efficiency	Establish "one substance, one assessment" [2]Simplify hazard assessment process [5]	Faster decision-makingMore predictable regulatory environment
Global Leadership	Champion high safety standards globally [2]Stop exporting chemicals banned in EU [2]	Level playing field for industryReduced global chemical pollution

A particularly significant advancement under the strategy is the formal adoption of the "one substance, one assessment" (OSOA) legislative package in November 2025 [5]. This package creates a common data platform managed by the European Chemicals Agency (ECHA) that will serve as a one-stop shop for chemical information across more than 70 pieces of EU legislation [5]. This reform fundamentally streamlines the EU's approach to chemical assessment and shortens the gap between risk identification and regulatory action [5].

The strategic recognition of chemical mixtures represents another paradigm shift in chemical safety assessment. The strategy mandates attention to the "cocktail effect" of chemicals when assessing chemical risks [2], moving beyond traditional single-substance risk assessment approaches. Scientific discussions have highlighted the complexity of mixture toxicology, with proposals for Mixture Assessment Factors (MAF) ranging from 2 to 500, though factors around 5-10 are considered potentially suitable for implementation [6].

The Safe and Sustainable by Design (SSbD) Framework

Conceptual Foundation and Definition

The Safe and Sustainable by Design (SSbD) framework represents a voluntary, pre-market approach established through a European Commission Recommendation in December 2022 [4]. This framework aims to guide the innovation process for chemicals and materials toward clean and sustainable industrial transitions while substituting or minimizing the production and use of substances of concern beyond regulatory obligations [4]. The SSbD framework operates as a proactive assessment methodology that integrates safety and sustainability considerations throughout the entire innovation process, from conceptual design to market entry [7].

The framework employs an iterative process composed of two primary phases: the (re-)design phase and the assessment phase [4]. The redesign phase involves applying guiding principles to steer development processes, while the assessment phase comprises a structured evaluation across multiple criteria. This iterative approach allows for continuous refinement as data becomes available throughout the innovation cycle, supporting dynamic risk management and design improvements [4].

Assessment Framework and Methodological Approach

The SSbD assessment follows a structured, five-step process based on life cycle thinking principles [7]. The methodology progresses from intrinsic hazard assessment to broader sustainability evaluations:

• Step 1: Hazard Assessment - Evaluates the intrinsic properties of the chemical or material based on hazard categories from the CLP Regulation, classifying substances into three groups (A, B, C) according to their level of concern [7].

• Step 2: Human Health & Safety in Production - Assesses worker exposure and environmental releases during manufacturing and processing stages, identifying potential risks and exposure pathways [7].

• Step 3: Application Safety - Focuses on safety during the use phase of the chemical or material, particularly relevant for consumer products and industrial applications [7].

• Step 4: Environmental Sustainability - Evaluates broader environmental impacts across the life cycle, including resource use, emissions, and ecological effects [7].

• Step 5: Socio-Economic Sustainability - Assesses social and economic dimensions, though this area remains less developed in current methodological guidance [7].

Table: SSbD Framework Assessment Steps and Regulatory Synergies

Assessment Step	Key Assessment Criteria	Related EU Legislation	Data Requirements
Step 1: Hazard Assessment	CMR, PBT/vPvB, endocrine disruption, etc. [7]	CLP Regulation [7]REACH SVHC [8]	Hazard classification dataToxicological studies
Step 2: Production Safety	Worker exposure, industrial emissions [7]	Industrial Emissions Directive [3]Worker Protection Directives	Exposure scenariosRisk management measures
Step 3: Use Phase Safety	Consumer exposure, article emissions [7]	Product Safety Directive [3]REACH Restrictions [8]	Use pattern analysisExposure modeling
Step 4: Environmental Sustainability	Climate change, resource use, circularity [7]	Ecodesign Directive [3]EU Taxonomy	LCA datasetsEnvironmental footprint data
Step 5: Socio-Economic	Social impacts, economic viability [7]	Sustainable Finance Framework	Cost-benefit analysisSocial impact assessments

The relationship between the voluntary SSbD framework and existing EU legislation demonstrates significant synergies. Information generated during SSbD assessment can support regulatory compliance, while regulatory data and methodologies can inform SSbD assessments, creating a reciprocal flow of information between innovation and compliance efforts [7].

Advanced Assessment Methodologies and New Approach Methodologies (NAMs)

Evolution Beyond Traditional Toxicity Testing

The implementation of the CSS and SSbD framework coincides with a significant transformation in chemical safety assessment methodologies. New Approach Methodologies (NAMs) represent a paradigm shift from traditional animal testing toward innovative approaches based on the 3R principles (reduction, refinement, and replacement) [9]. These methodologies leverage advances in toxicogenomics (TGx), in vitro systems, and computational approaches to provide more mechanistic insights into chemical-biological interactions [9].

The Adverse Outcome Pathway (AOP) framework has emerged as a crucial conceptual model within NAMs, establishing coherent links between Molecular Initiating Events (MIEs) and Adverse Outcomes (AOs) at organismal or population levels [9]. This framework enhances the understanding and prediction of toxicity mechanisms, supporting more efficient safety assessment strategies. A key innovation in modern chemical safety assessment involves the integration of omics technologies (genomics, transcriptomics, proteomics, metabolomics) to characterize molecular responses to chemical exposures and identify early biomarkers of effect [9].

Proactive Safety Screening and Test System Characterization

A revolutionary approach in chemical safety assessment involves proactive safety screening that repositions the analytical workflow to prioritize hazard detection before compound identification [10]. This "effect-first" strategy represents a significant departure from conventional targeted analysis and addresses critical limitations in current consumer product safety evaluation [10].

The emerging methodology utilizes effect-based bioassays as the primary screening step, followed by identification and prioritization of compounds demonstrating biological activity [10]. This approach efficiently handles complex mixtures containing thousands of compounds of unknown identity and toxicity, effectively addressing the "unknown unknowns" in product safety assessment [10]. Implementation of planar bioassays in streamlined platforms like the 2LabsToGo-Eco system offers a practical solution for stakeholders to conduct proactive safety screening with minimal sample preparation [10].

Critical to the application of NAMs is the comprehensive molecular characterization of test systems using omics technologies [9]. Current test system selection often prioritizes practical considerations like availability and established use, rather than how completely the system's molecular profile correlates with physiological and pathological phenotypes in vivo [9]. Omics-based characterization enables data-driven selection of AOP-relevant in vitro test systems by matching their molecular makeup with key events in adverse outcome pathways [9].

Experimental Protocols for SSbD Assessment

Implementation of the SSbD framework requires standardized methodological approaches for safety and sustainability assessment. The following experimental protocols provide guidance for researchers conducting SSbD evaluations:

Protocol 1: Tiered Hazard Assessment (SSbD Step 1)

• Computational Screening: Conduct in silico prediction of physicochemical properties and toxicological endpoints using QSAR models and read-across approaches from structurally similar substances with existing data.
• In Vitro Testing Battery: Implement standardized OECD-validated in vitro assays for core toxicological endpoints (genotoxicity, endocrine disruption, cytotoxicity).
• Toxicogenomics Analysis: Apply transcriptomic profiling (RNA sequencing) to exposed in vitro systems to identify mechanistic pathways and potential novel toxicity signatures.
• Hazard Classification: Integrate results from all tiers to assign hazard classification according to CLP criteria and categorize into SSbD groups A-C.

Protocol 2: Omics-Driven Test System Characterization

• Baseline Molecular Profiling: Conduct comprehensive transcriptomic, proteomic, and metabolomic analysis of test systems under control conditions.
• Database Integration: Compare molecular profiles with reference databases (e.g., Human Protein Atlas, ENCODE, GTEx) to establish physiological relevance.
• AOP Network Mapping: Map expressed genes and proteins to Key Events in relevant Adverse Outcome Pathways using structured ontologies.
• Applicability Domain Definition: Establish test system applicability domain based on molecular competency for specific toxicological mechanisms.

Protocol 3: Proactive Safety Screening of Complex Mixtures

• Sample Preparation: Minimal processing using standardized extraction protocols appropriate for the product matrix (e.g., QuEChERS for consumer products).
• Effect-Based Bioassay Screening: Implement planar bioassays (e.g., 2LabsToGo-Eco system) for high-throughput hazard detection of endocrine disruption, mutagenicity, and cytotoxicity.
• Bioassay-Directed Fractionation: Separate complex mixtures based on bioassay results to isolate active components.
• High-Resolution Mass Spectrometry: Identify unknown hazardous compounds using non-targeted HRMS analysis with database matching.

Table: Research Reagent Solutions for SSbD Implementation

Research Tool Category	Specific Solutions	Application in SSbD	Regulatory Relevance
In Vitro Test Systems	OECD-validated cell lines (e.g., BALB/3T3, HepG2)Stem cell-derived models3D tissue models & organoids [9]	Hazard assessment (Step 1)Mechanistic studies	REACH requirements [8]CSS non-animal methods push
Omics Technologies	Transcriptomics platforms (RNA-seq)Proteomics solutions (LC-MS/MS)Metabolomics kits [9]	Test system characterizationMechanism of action studies	AOP development [9]Biomarker identification
Bioassay Systems	Planar bioassays (2LabsToGo-Eco) [10]High-content screening platformsReceptor-mediated assays	Proactive safety screening [10]Mixture toxicity assessment	CSS cocktail effect mandate [2]
Computational Tools	QSAR softwareAOP knowledge basesRead-across frameworks	Prioritization for testingHazard prediction	REACH alternative methods [8]OSOA data integration [5]
Analytical Chemistry	HRMS systemsChromatography platformsNon-targeted analysis software	Chemical identity confirmationExposure assessment	REACH registration data [8]Restriction support

The EU Chemicals Strategy for Sustainability represents a fundamental transformation in chemical governance, intrinsically linked to the broader objectives of the European Green Deal. The strategic implementation of this framework through initiatives like Safe and Sustainable by Design, "one substance, one assessment", and New Approach Methodologies establishes a comprehensive ecosystem for driving chemical innovation toward safer and more sustainable outcomes.

The integration of proactive safety screening methodologies that prioritize hazard detection before compound identification addresses critical gaps in current consumer protection approaches [10]. Similarly, the adoption of omics technologies for test system characterization and mechanism-based safety assessment enables a more biologically relevant understanding of chemical-biological interactions [9]. These advanced methodologies provide the scientific foundation for implementing the ambitious goals of the CSS while maintaining Europe's competitiveness in chemical innovation.

Future success in implementing this transformative agenda will depend on continued scientific advancement, regulatory alignment, and stakeholder engagement across the chemical value chain. The research community plays a particularly critical role in refining assessment methodologies, developing standardized protocols, and generating robust scientific evidence to support the transition toward safe and sustainable chemicals envisioned by the European Green Deal.

The Safe and Sustainable by Design (SSbD) framework, established as a voluntary approach through a European Commission Recommendation in December 2022, represents a transformative methodology for guiding innovation in chemicals and materials [4]. This framework systematically integrates safety and sustainability considerations throughout the entire research and development process, moving beyond traditional compliance-based approaches to proactively minimize impacts on health, climate, and the environment across sourcing, production, use, and end-of-life stages [4] [7]. At the heart of this methodology lies a dynamic iterative cycle composed of two fundamental, interconnected components: a (re-)design phase and an assessment phase [4]. This cyclical process enables continuous improvement and refinement of products from their earliest conceptual stages through to market-ready innovations, ensuring that safety and sustainability are not afterthoughts but foundational design principles. The framework's structure is particularly valuable for sectors like pharmaceuticals and advanced materials, where the European Commission is actively funding projects to refine and apply SSbD principles across various value chains [11] [12].

The Structural Foundation: (Re-)Design and Assessment Phases

The SSbD framework operates through two continuously interacting components that form a feedback loop for progressive refinement. The (re-)design phase serves as the strategic planning stage where innovators define the fundamental goals, scope, and system boundaries that will guide development [4]. In this phase, researchers establish the core objectives for their chemical or material, identify potential application contexts, and set the parameters for what constitutes a safer and more sustainable alternative. This foundational work is crucial for establishing clear assessment criteria and ensuring that subsequent evaluations are properly contextualized to the innovation's specific intended function and life cycle.

The assessment phase provides the evidence-based evaluation mechanism through four systematic steps, progressing from basic hazard characterization to comprehensive lifecycle evaluation [4] [7]. These steps include: (1) hazard assessment of the chemical or material's intrinsic properties; (2) human exposure assessment during industrial production; (3) exposure assessment during consumer use; and (4) full life-cycle assessment of environmental, health, and sustainability impacts [4]. This phased approach allows for appropriately scoped evaluations based on available data, with increasing comprehensiveness as innovations mature through development stages.

Table 1: Core Components of the SSbD Framework

Component	Primary Function	Key Activities	Outputs
(Re-)Design Phase	Strategic Planning	Define goals, scope, and system boundaries; Establish design parameters	Design specifications; Innovation context; Assessment boundaries
Assessment Phase	Evidence Generation	Step-wise evaluation of hazard, exposure, and lifecycle impacts	Safety profile; Sustainability metrics; Identification of improvement areas

The Iterative Process in Practice: From Data to Design Refinement

The true innovation of the SSbD framework lies in the dynamic relationship between its design and assessment components, which are applied repeatedly as data becomes available throughout the development process [4]. This iteration creates a continuous improvement cycle where assessment findings directly inform subsequent redesign efforts, progressively enhancing both safety and sustainability performance. In practice, early iterations might focus on basic hazard screening using predictive tools and computational models, while later iterations incorporate more comprehensive experimental data and detailed lifecycle assessments [13]. This approach is particularly valuable for addressing the challenges of low Technology Readiness Levels (TRL), where limited data availability traditionally impedes thorough safety and sustainability evaluation [14].

The diagram below illustrates this iterative relationship and the progressive flow of information between the core phases:

This iterative process is particularly well-suited to sectors with established stage-gate development processes. Research into pharmaceutical applications demonstrates that the stage-gate process in pharmaceutical R&D aligns effectively with the SSbD design-assessment cycle, creating natural checkpoints for iteration and refinement [13] [15]. As innovations progress toward higher Technology Readiness Levels, the assessment can incorporate increasingly robust data, moving from predictive modeling to experimental validation and eventually to full industrial-scale evaluation [14].

Methodological Protocols: Implementing the Assessment Phase

Hazard Assessment (Step 1)

The initial hazard assessment focuses on characterizing the intrinsic properties of chemicals and materials, utilizing criteria aligned with the Regulation on Classification, Labelling and Packaging (CLP) [7]. The protocol involves:

• Data Collection: Gather existing experimental data from sources like REACH registration dossiers, or generate new data through standardized testing or New Approach Methodologies (NAMs) such as QSAR models and in vitro methods [7].
• Hazard Classification: Categorize substances into three groups based on CLP criteria:
  • Group A (Criterion H1): Substances of Concern, including CMRs, endocrine disruptors, and PBT/vPvB substances.
  • Group B (Criterion H2): Substances with less critical hazards but potential safety concerns.
  • Group C (Criterion H3): Substances with no significant hazards or data gaps preventing classification [7].
• Data Gap Analysis: Identify missing information critical for safety determination and establish testing strategies to address these gaps.

Exposure Assessment During Production (Step 2) and Use (Step 3)

These steps evaluate potential exposure to workers during industrial manufacturing and to consumers during product application. The methodological approach includes:

• Exposure Scenario Development: Define specific conditions of exposure throughout the product life cycle, considering process types, operational conditions, and protective measures [7].
• Exposure Estimation: Utilize standardized exposure models or monitoring data to quantify potential exposure levels. For nanomaterials, specific tools like the SAbyNA platform provide adapted exposure assessment workflows [16].
• Risk Characterization: Compare exposure estimates with derived no-effect levels or other hazard benchmarks to identify potential risks requiring mitigation through design changes.

Life Cycle Assessment (Step 4)

The environmental sustainability assessment employs life cycle thinking to quantify impacts across multiple categories:

• Goal and Scope Definition: Establish assessment boundaries aligned with the system boundaries defined in the design phase, including functional units and impact categories [4].
• Life Cycle Inventory: Compile energy and material inputs and environmental releases across the entire life cycle.
• Impact Assessment: Evaluate contributions to impact categories such as climate change, resource use, and ecotoxicity.
• Interpretation: Identify environmental hotspots and improvement opportunities to inform redesign decisions.

Table 2: Methodological Requirements for SSbD Assessment Steps

Assessment Step	Data Requirements	Methodological Tools	Key Output Metrics
Step 1: Hazard Assessment	Experimental toxicity data, Read-across from similar substances, QSAR predictions	CLP regulation criteria, SSbD hazard groups (A, B, C), Tiered testing strategies	Hazard classification, Identification of substances of concern
Step 2: Production Exposure Assessment	Process descriptions, Physical-chemical properties, Operational conditions	Exposure models (ECETOC TRA, MEASE), Monitoring data	Exposure estimates for workers, Risk characterization ratios
Step 3: Use Phase Exposure Assessment	Product application scenarios, Release potential, Consumer use patterns	Consumer exposure models, Environmental release categories	Consumer exposure levels, Environmental emission estimates
Step 4: Life Cycle Assessment	Resource/energy inputs, Emission data, Transportation distances	LCA software (SimaPro, GaBi), Impact assessment methods (EF, ReCiPe)	Carbon footprint, Resource efficiency indicators, Environmental impact scores

Research and Implementation Tools

The practical implementation of SSbD principles is supported by dedicated tools and platforms that streamline the assessment process. The SAbyNA guidance platform represents a significant advancement, particularly for nanomaterials and nano-enabled products, by integrating assessment modules for safety, environmental sustainability, and costs [16]. This digital tool provides:

• Optimized workflows to support development of safe-by-design nanomaterials and processes
• Informative modules guiding method selection for exposure and hazard assessment
• Assessment modules for screening-level evaluation of environmental sustainability
• Safe-by-design intervention strategies to mitigate identified risks while maintaining functionality [16]

For the pharmaceutical sector, research indicates that SSbD implementation requires developing methods to predict environmental safety and sustainability based on limited R&D data, establishing pragmatic procedures for integrating SSbD into drug innovation, and creating weighing systems that balance environmental parameters with medical efficacy and patient safety [13] [15]. The emerging concept of Safe and Sustainable by Comparison (SSbC) extends these principles to marketed pharmaceuticals, enabling healthcare actors to make more informed choices about existing treatments [13].

Table 3: Essential Research Tools for SSbD Implementation

Tool/Platform	Primary Function	Applicable Sectors	Key Features
SAbyNA Platform	Integrated safety and sustainability assessment	Nanomaterials, Nano-enabled products	GUIDEnano risk assessment integration, SbD intervention strategies, Cost assessment modules
SSbD Knowledge Sharing Portal	Methodological guidance and case studies	Chemicals, Materials	Framework implementation examples, Stakeholder community resources
GUIDEnano Tool	Nanomaterial-specific risk assessment	Nanomaterials	Fate and exposure modeling, Risk mitigation guidance
REACH Assessment Frameworks	Regulatory hazard and risk assessment	Chemicals	Standardized testing strategies, Chemical safety assessment methodologies

Current Challenges and Future Development

While the SSbD framework provides a robust foundation, several methodological and practical challenges remain active areas of research and development. Current limitations include the need for more efficient metrics that allow for aggregation and comparison of results across assessment steps, streamlined methods for sustainability assessment, and stronger integration of biophysical benchmarks related to chemical pollution [11]. The European Commission is actively addressing these challenges through a revision process, with a survey open until September 2025 to collect stakeholder feedback on practical improvements [14].

The revised framework, expected in 2025, introduces several enhancements including a 'Scoping Analysis' to better guide innovators, a unified safety assessment approach, and an Environmental Sustainability Assessment benchmark [14]. Future developments will likely focus on expanding SSbD application to broader sets of economic sectors, materials, and processes, as well as improving interoperability between the voluntary SSbD framework and regulatory requirements [11] [7]. For researchers and drug development professionals, these advancements promise increasingly practical and effective tools for designing innovations that simultaneously optimize therapeutic benefit, safety, and environmental sustainability.

The Safe and Sustainable by Design (SSbD) framework, established as a voluntary approach by the European Commission, is a proactive methodology designed to guide the innovation process for chemicals and advanced materials [4]. Its core objectives are threefold: to steer innovation towards a green and sustainable industrial transition; to substitute or minimize the production and use of substances of concern, aligning with and exceeding regulatory obligations; and to minimize impacts on health, climate, and the environment throughout the entire life cycle of a chemical, material, or product [4] [17]. This framework is inherently iterative, combining a (re-)design phase with an assessment phase that is refined as data becomes available [4] [18].

Central to this approach is the evolving concept of a "substance of concern" (SoC). Historically, chemical management in the EU was primarily risk-based. However, the EU Chemicals Strategy for Sustainability (CSS) marks a shift towards a more hazard-centric approach, broadening the definition of SoC [19]. Beyond substances with inherently hazardous properties, the concept now also includes those that negatively affect the reuse and recycling of materials, thereby integrating circular economy objectives into safety assessments [20] [19]. For researchers and drug development professionals, understanding this expanded definition is critical for navigating future regulatory landscapes and designing inherently safer and more sustainable products.

Defining and Categorizing Substances of Concern

The definition of a substance of concern is pivotal for applying the SSbD framework. According to the Ecodesign for Sustainable Products Regulation (ESPR), a substance can be classified as an SoC if it meets one or more of the following criteria [20]:

• Substances of Very High Concern (SVHC) already identified under the REACH regulation.
• Substances exhibiting any of the hazardous properties listed in Part 3 of Annex VI to the CLP Regulation.
• Substances covered by the Regulation on Persistent Organic Pollutants (POP Regulation).
• Substances that negatively affect the reuse and recycling of materials in the article in which they are contained.

This broadened definition signifies a fundamental change. The concept is "no longer only safety related, as consideration is now given to circularity objectives" [19]. This expansion has drawn some criticism from industry, with concerns that a generic application could classify a significant proportion of registered chemicals as substances of concern without product-specific risk assessment [20]. The following table summarizes the key regulatory criteria and their implications for research and development.

Table 1: Key Criteria for Defining Substances of Concern and Research Implications

Criterion	Regulatory Basis	Key Consideration for R&D
Hazardous Properties	CLP Regulation (Annex VI)	Focus on intrinsic molecular properties and classification (e.g., CMR, endocrine disruptor).
Very High Concern	REACH (Candidate List)	Prioritizes substances for authorization, driving substitution efforts.
Persistence	POP Regulation	Highlights compounds that resist environmental degradation.
Circularity Impact	ESPR	Extends beyond human/eco-toxicity to include material compatibility with recycling processes.

The SSbD Assessment Methodology for Comparing Alternatives

The SSbD framework provides a structured, iterative process for assessing the safety and sustainability of chemicals and materials. The assessment phase is typically composed of multiple steps that follow a life-cycle thinking approach [18] [7] [17]. This methodology serves as an excellent foundation for objectively comparing the performance of alternative substances or processes.

The SSbD Assessment Framework

The workflow for a comparative SSbD assessment can be visualized as a multi-stage, iterative process where the outcomes of each step inform the redesign and refinement of the innovation.

Detailed Experimental and Assessment Protocols

For researchers to generate comparable and reliable data, following detailed methodologies for each assessment step is paramount. Below are the core protocols as outlined in the SSbD framework and related research.

Protocol 1: Hazard Assessment (Step 1)

• Objective: To evaluate the intrinsic hazardous properties of the chemical/material.
• Methodology: This step involves a thorough assessment of the substance's physicochemical, toxicological, and ecotoxicological properties. Data can be gathered from existing literature, experimental studies, or through in silico models (e.g., QSAR, read-across) [18] [7]. The substance is then classified according to the CLP Regulation criteria. The outcome determines if the substance falls into Group A (fulfilling SSbD criteria), B (failing some criteria), or C (failing all criteria), which directly influences its status as a substance of concern [7].

Protocol 2: Human Health & Safety in Production (Step 2)

• Objective: To assess occupational exposure and risks during the production and processing phases.
• Methodology: This requires an exposure assessment for workers involved in manufacturing and handling. Methods include workplace air monitoring, dermal exposure modeling, and use of control banding tools. The risk is characterized by comparing estimated exposure levels with derived no-effect levels (DNELs) for relevant endpoints [18].

Protocol 3: Safety in Final Application (Step 3)

• Objective: To evaluate consumer and environmental exposure and potential impacts during the use phase of the final product.
• Methodology: This involves estimating exposure scenarios for consumers (e.g., dermal, inhalation) and releases into the environment (e.g., water, soil). Standardized emission models and exposure assessment tools (as provided by REACH guidance) are employed. The risk is characterized by comparing predicted exposure and environmental concentrations (PEC) with predicted no-effect concentrations (PNEC) [18].

Protocol 4: Environmental Sustainability Assessment (Step 4)

• Objective: To measure the broader environmental footprint across the chemical's life cycle.
• Methodology: Conduct a Life Cycle Assessment (LCA) following the Product Environmental Footprint (PEF) methodology where applicable [17]. This quantitative approach assesses impacts on climate change, resource use, water, and ecosystem quality, from raw material extraction to end-of-life (cradle-to-grave) [18] [17]. For early-stage innovations, prospective LCA (pLCA) and ex-ante assessments are recommended despite data challenges [21].

Comparative Data Analysis of Alternatives

When comparing alternatives to a substance of concern, data from the SSbD assessment should be synthesized into a clear, structured format to guide decision-making. The following table exemplifies how such a comparison can be structured, focusing on key quantitative and qualitative outcomes from the assessment phases.

Table 2: Hypothetical Comparative SSbD Assessment of Two Alternative Solvents

Assessment Parameter	Substance A (Incumbent SoC)	Alternative B (Bio-based)	Alternative C (Green Synthetic)	Key Experimental Data & Methodology
Step 1: Hazard Profile	CMR 1B	Irritant	No classification	Test Method: In vitro and in silico assessment per CLP guidelines.
Step 2: Worker Exposure Risk	High (low boiling point)	Moderate	Low (high boiling point)	Test Method: Exposure modeling using Stoffenmanager; DNEL comparison.
Step 3: Consumer Risk (Use Phase)	High dermal absorption	Low dermal absorption	Negligible	Test Method: Derived Exposure Scenarios (REACH); consumer exposure modeling.
Step 4: Global Warming Potential (kg CO₂ eq)	5.2	2.1	1.8	Methodology: Cradle-to-gate LCA using PEFCR; data from Ecoinvent v3.
Step 4: Resource Use (kg Sb eq)	0.015	0.008	0.012	Methodology: Abiotic resource depletion assessment (CML baseline).
Circularity (Recycling Compatibility)	Hinders recycling	Compatible	Improves recycling	Assessment: Based on ESPR criteria; laboratory-scale recycling trials.
Overall SSbD Score	Non-SSbD	Potential SSbD	SSbD	Assessment: Weighted multi-criteria decision analysis (MCDA).

The Scientist's Toolkit: Key Research Reagent Solutions

Implementing the SSbD framework requires a suite of methodological tools and data resources. The following table details essential "research reagents" – in this context, key models, databases, and software – that are critical for conducting the assessments described in the experimental protocols.

Table 3: Essential Research Tools and Data Sources for SSbD Implementation

Tool/Resource Name	Function in SSbD Assessment	Relevance to Protocol
QSAR Toolbox	Filling data gaps for hazard assessment by predicting physicochemical and toxicological properties based on structural similarity.	Protocol 1: Hazard Assessment [18]
FAIR Data Principles	A set of guiding principles (Findable, Accessible, Interoperable, Reusable) to ensure robust data management, which is critical for SSbD assessments [18].	All Protocols, particularly data generation and sharing.
PEF/OEF Methods	Provides the standardized methodology for calculating the environmental footprint of products and organisations, supporting the LCA.	Protocol 4: Environmental Sustainability Assessment [17]
New Approach Methodologies (NAMs)	Encompasses non-animal testing methods (e.g., in vitro, omics) for early and efficient safety screening of novel chemicals [21].	Protocol 1: Hazard Assessment
Prospective LCA (pLCA) Models	Allows for the environmental assessment of emerging technologies at early development stages (low TRL) despite data uncertainties.	Protocol 4: Environmental Sustainability Assessment [21]
Exposure Assessment Tools (e.g., ECETOC TRA, RISKOFDERM)	Software and models used to estimate exposure levels for workers and consumers under specific use scenarios.	Protocol 2 & 3: Exposure and Risk Assessment [18]

Adopting the SSbD framework represents a paradigm shift from reactive risk management to proactive, preventative design. For researchers and drug development professionals, this means integrating safety and sustainability considerations at the earliest stages of the innovation process [17] [21]. The iterative relationship between design and assessment, as shown in the workflow diagram, is crucial for continuous improvement.

Successfully defining key outcomes for substituting substances of concern and minimizing life-cycle impact hinges on a multi-faceted approach: a deep understanding of the expanding regulatory definition of SoCs; the rigorous application of standardized assessment protocols; and the strategic use of comparative data analysis to guide decision-making. Furthermore, overcoming implementation challenges—such as data availability for low-TRL innovations and the need for cross-departmental collaboration within companies—is essential for accelerating the industrial transition towards safer and more sustainable chemicals and materials [18] [21]. By leveraging the structured methodologies and tools outlined in this guide, the scientific community can effectively contribute to this transition, ensuring that innovations are not only high-performing but also aligned with the principles of safety and sustainability from their conception.

The Safe and Sustainable by Design (SSbD) framework, established through a European Commission Recommendation in December 2022, represents a transformative voluntary approach to guiding the innovation process for chemicals and materials [4]. Developed by the European Commission's Joint Research Centre (JRC), this framework operates within the broader context of the European Green Deal and its Chemicals Strategy for Sustainability (CSS), which aims to drive the transition toward clean, sustainable industries and ultimately achieve a toxic-free environment [7]. The SSbD framework is fundamentally designed as a pre-market innovation tool that integrates safety and sustainability considerations throughout the research and development lifecycle, creating a critical bridge between innovative chemical/material development and regulatory compliance.

This framework functions on a dual-phase structure consisting of a (re-)design phase and an assessment phase, applied iteratively as data becomes available throughout the innovation process [4]. The design phase incorporates principles from established approaches including green chemistry, green engineering, and circular economy concepts, while the assessment phase encompasses a comprehensive evaluation spanning hazard assessment, human health and safety in production, use-phase impacts, and environmental sustainability [22]. This systematic approach allows innovators to identify potential safety and sustainability issues early in the development process, facilitating proactive design improvements before products enter the market.

The relationship between SSbD and existing regulatory frameworks is synergistic rather than duplicative. While EU chemical regulations primarily focus on ensuring the safety of marketed products through specific substance, product, or sectoral legislation, the SSbD framework takes a proactive, life-cycle perspective that covers the entire innovation journey from conception to commercialization [7]. This forward-looking approach positions SSbD as a complementary mechanism that prepares innovations for smoother regulatory approval while simultaneously driving continuous improvement in chemical and material design to exceed minimum compliance requirements.

Comparative Analysis: SSbD Framework Versus Traditional Regulatory Approach

Key Characteristics and Objectives

Aspect	SSbD Framework	Traditional Regulatory Approach
Nature	Voluntary, pre-market innovation guide [7]	Mandatory, market-entry requirements [7]
Primary Focus	Proactive design improvement throughout R&D [7]	Compliance verification for market approval [7]
Assessment Scope	Entire life cycle from sourcing to end-of-life [4] [7]	Specific hazards, risks, or substance uses [7]
Application Timing	Early innovation stages through development [18]	Pre-market notification and post-market monitoring [7]
Core Principles	Hazard minimization, sustainability, circularity [4] [22]	Risk management, classification, restriction [7]
Data Requirements	Tiered approach matching innovation maturity [18]	Standardized data requirements regardless of stage [7]

Methodological Comparison of Assessment Approaches

The SSbD framework incorporates a five-step assessment methodology that systematically evaluates both safety and sustainability dimensions throughout the innovation process [18] [7]. This begins with hazard assessment of the chemical/material (Step 1), proceeds to evaluate human health and safety in production and processing (Step 2), assesses human health and environmental aspects in the final application (Step 3), then conducts environmental sustainability assessment (Step 4), and optionally addresses socioeconomic sustainability (Step 5) [7]. This comprehensive structure mirrors the scope of multiple regulatory frameworks but integrates them into a cohesive assessment workflow that aligns with innovation milestones.

In contrast, traditional regulatory approaches typically focus on specific legislative mandates with defined boundaries. For example, the Classification, Labelling and Packaging (CLP) Regulation focuses primarily on hazard classification, while the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation employs detailed risk assessment methodologies for specific use scenarios [7]. The SSbD framework does not replace these regulatory requirements but rather creates a pathway for innovations to more readily meet them by addressing potential concerns during the design phase rather than after development is complete.

A significant distinction lies in the iterative application of SSbD assessments throughout the innovation process, which follows a stage-gate model where evaluations become progressively more detailed as the innovation matures [18]. This contrasts with regulatory assessments that typically occur at defined points in the product lifecycle, often when seeking market approval. The tiered approach of SSbD allows for early identification of potential regulatory challenges when design changes are most feasible and cost-effective to implement.

The SSbD Assessment Protocol: Methodologies and Data Requirements

Experimental Framework and Assessment Workflow

The implementation of SSbD follows a structured experimental framework that begins with a crucial scoping analysis to define the assessment boundaries, functional requirements, and innovation context [18]. This initial phase establishes the foundation for all subsequent assessments by identifying the chemical/material under consideration, its intended applications, the relevant life cycle stages, and the system boundaries for evaluation. The scoping analysis ensures that assessments remain focused on decision-relevant criteria and appropriate comparative benchmarks.

The core assessment phase employs a multi-step methodology that integrates both safety and sustainability dimensions:

• Step 1: Hazard Assessment - This step focuses on evaluating the intrinsic properties of the chemical/material using criteria aligned with the CLP Regulation, categorizing substances into groups A, B, and C based on hazard profiles [7]. The assessment includes comprehensive evaluation of human health hazards (acute toxicity, skin corrosion, carcinogenicity), physicochemical hazards (explosiveness, flammability), and environmental hazards (hazardous to aquatic environment, persistent and bioaccumulative) [18].

• Step 2: Human Health and Safety in Production - This operational safety assessment examines potential exposures during manufacturing and processing, incorporating occupational exposure limits, process safety considerations, and end-of-life implications [18]. Methodologies include exposure modeling, monitoring data from analogous processes, and safety assessments for industrial handling.

• Step 3: Use-Phase Safety Assessment - This step evaluates consumer and environmental exposure during the application phase, considering product use patterns, release scenarios, and potential transformation products [18]. Experimental approaches include exposure modeling, leaching studies, degradation testing, and monitoring data from similar products.

• Step 4: Environmental Sustainability Assessment - This comprehensive life cycle assessment quantifies environmental impacts across categories including climate change, resource use, ecosystem quality, and chemical pollution [18] [22]. The methodology follows standardized LCA principles but adapts them for early innovation stages through prospective modeling and scenario analysis.

• Step 5: Socioeconomic Assessment - This optional step evaluates broader societal impacts including economic viability, social acceptability, and community effects [7]. Methodologies include cost-benefit analysis, social life cycle assessment, and stakeholder engagement processes.

Research Reagent Solutions for SSbD Implementation

Research Tool Category	Specific Methods/Approaches	Function in SSbD Assessment
Hazard Assessment Tools	QSAR, read-across, NAMs, HTE [23]	Predict intrinsic hazards when experimental data is limited
Exposure Assessment Tools	Exposure modeling, biomonitoring, sensor technologies [18]	Quantify human and environmental exposure across life cycle
Life Cycle Assessment	Prospective LCA, anticipatory LCA, ML-assisted assessment [22]	Estimate environmental impacts for early-stage innovations
Data Management	FAIR data principles, digital logbooks, knowledge sharing platforms [18] [22]	Ensure data findability, accessibility, interoperability, reuse
Decision Support	Multi-criteria decision analysis, trade-off evaluation [22]	Balance competing objectives across safety and sustainability

Synergies Between SSbD and Regulatory Compliance

Information Flow Between Innovation and Regulatory Processes

The SSbD framework establishes a reciprocal information exchange with existing regulatory frameworks, creating value for both innovation development and regulatory compliance [7]. Information generated through SSbD assessments can subsequently support regulatory submissions by providing comprehensive safety and sustainability data that addresses multiple regulatory requirements simultaneously. Conversely, regulatory data and methodologies can inform SSbD assessments, particularly for established substances with extensive existing data packages.

This synergistic relationship manifests through several concrete mechanisms:

• Regulatory Preparedness: SSbD implementation prepares innovations for smoother regulatory approval by proactively addressing potential concerns and generating robust data packages that demonstrate safe use and sustainability credentials [23]. This is particularly valuable for novel materials and technologies that may face uncertain regulatory pathways.

• Data Generation for Regulatory Submissions: The tiered assessment approach in SSbD generates data that directly supports regulatory requirements under frameworks such as REACH, CLP, and product-specific legislation [7]. For example, hazard data generated in Step 1 of SSbD assessment directly informs classification and labeling requirements, while exposure assessments from Steps 2 and 3 support chemical safety assessments under REACH.

• Methodological Alignment: The SSbD framework incorporates regulatory criteria and assessment methodologies, particularly in its hazard assessment step which directly references CLP classification criteria [7]. This alignment ensures that SSbD outcomes remain relevant for subsequent regulatory evaluation while maintaining the framework's broader safety and sustainability ambitions.

Complementary Roles in Chemical Governance

The relationship between SSbD and regulatory frameworks represents a division of labor in chemical governance rather than duplication of effort. Regulatory frameworks establish the minimum requirements for market access, while SSbD encourages performance beyond compliance through continuous improvement and innovation [7]. This complementary relationship enables each approach to focus on its respective strengths: regulation provides legal enforceability and a level playing field, while SSbD offers flexibility and adaptability to technological innovation.

This complementarity extends to their respective positions in the chemical/product lifecycle. Regulatory frameworks primarily operate at the market entry gate, establishing conditions under which products can be placed on the market and used [7]. In contrast, SSbD functions throughout the innovation process, guiding research and development decisions from early laboratory stages through to commercialization [18]. The sequential application of these approaches creates a comprehensive governance continuum that addresses both innovation dynamics and market oversight.

Implementation Challenges and Research Gaps

Operationalization Barriers in SSbD Application

Despite its theoretical promise, the practical implementation of SSbD faces significant challenges that impact its effectiveness as a bridge between innovation and regulation. Comprehensive mapping of these challenges has identified 35 distinct barriers across conceptual, methodological, and practical dimensions [18]. The highest priority challenges include:

• Integration into Innovation Processes: The most significant barrier involves effectively embedding SSbD considerations into established stage-gate innovation processes [18]. This requires cultural and organizational changes beyond mere methodological adoption, including incentive structures, decision-making criteria, and performance metrics that value safety and sustainability alongside traditional technical and commercial considerations.

• Data Availability and Quality: Substantial data gaps exist, particularly for novel materials and early-stage innovations where experimental data is limited [18]. This challenge is exacerbated by uncertainties in scaling laboratory results to industrial production and use scenarios. Potential solutions include enhanced application of FAIR data principles, optimized in silico methods for early innovation stages, and development of structured data sharing infrastructures [18].

• Integration of Safety and Sustainability Aspects: Methodological challenges persist in effectively combining hazard, risk, and life cycle assessment approaches within a coherent decision-making framework [18]. This requires harmonization of input data, assumptions, and scenario constructions across these traditionally separate assessment domains, as well as development of integrated interpretation frameworks.

Methodological and Conceptual Development Needs

Beyond immediate operational challenges, several conceptual and methodological gaps require attention to strengthen the bridge between SSbD and regulatory compliance:

• Hazard-Based versus Risk-Based Approaches: A fundamental tension exists between hazard-based cutoff criteria in the SSbD framework and the risk-based approaches predominant in chemical regulation [23]. The JRC SSbD framework prioritizes hazard minimization before considering exposure and use conditions, while regulatory frameworks typically evaluate safety through risk characterization that integrates both hazard and exposure [23]. This conceptual mismatch can create implementation challenges for innovations where exposure control enables safe use of substances with hazardous properties.

• Absolute versus Relative Sustainability: The SSbD framework aspires toward absolute sustainability benchmarks, while most current assessment methods generate relative comparisons to existing alternatives [11]. Developing operational absolute sustainability assessment methods represents a significant methodological challenge that requires establishing context-based environmental boundaries and allocation principles.

• Tool Development and Harmonization: Despite the existence of numerous assessment tools for specific SSbD aspects, integrated toolkits that support the complete SSbD assessment workflow remain underdeveloped [18] [22]. Priority development areas include integrated software platforms, standardized data formats, and harmonized assessment endpoints that facilitate comparison across assessment steps and decision-making.

The SSbD framework represents a significant evolution in how safety and sustainability considerations are integrated into chemical and material innovation. By establishing a structured bridge between innovation processes and regulatory requirements, SSbD creates a proactive pathway for developing substances and products that not only meet compliance standards but exceed them through designed-in safety and sustainability attributes. The framework's iterative, life cycle-oriented approach addresses fundamental limitations of traditional regulatory systems that necessarily focus on specific hazards, uses, or product categories.

The future effectiveness of SSbD as a bridge between innovation and regulation will depend on addressing current methodological challenges and strengthening implementation frameworks. Priority development areas include harmonized assessment methodologies that balance hazard- and risk-based considerations, integrated data management infrastructures supporting FAIR principles, and decision-support tools that effectively navigate trade-offs across multiple safety and sustainability dimensions [18] [23]. Additionally, broader application across economic sectors and material types will strengthen the evidence base for framework refinement and continuous improvement.

For researchers and innovation professionals, mastering SSbD principles and methodologies offers significant advantages in navigating evolving regulatory landscapes and market expectations. The framework provides a structured approach for demonstrating regulatory preparedness while driving innovation toward safer and more sustainable outcomes. As SSbD concepts increasingly influence both public and private sector innovation funding and chemical governance, early adoption and contribution to methodology development represents a strategic opportunity for research organizations and industrial innovators alike.

A Step-by-Step Guide to SSbD Assessment: From Hazard to Life-Cycle Analysis

In the rigorous fields of drug development and chemical safety, the initial steps of scoping analysis and system boundary definition are not merely administrative; they are foundational to ensuring that innovations are both safe and sustainable. Within the context of the Safe and Sustainable by Design (SSbD) framework—a voluntary approach championed by the European Commission to steer innovation—these initial steps contextualize the entire assessment by defining the chemical or material under consideration, its life cycle, and its function [7]. A well-defined system boundary delineates the scope of analysis by specifying system inclusions and exclusions, acting as a crucial line that separates the system of interest from its environment [24]. For researchers and drug development professionals, the decisions made at this stage directly dictate which environmental impacts, health risks, and socio-economic factors are considered, ultimately shaping the trajectory of the innovation process [24] [7]. Without a thoughtfully specified boundary, an analysis risks either underestimating the true environmental and safety burdens or becoming unmanageably broad, obscuring key areas for improvement [24]. This article objectively compares how different assessment paradigms—specifically the SSbD framework, traditional drug development, and systems engineering—approach this critical initiating phase, providing a structured comparison for scientific professionals.

Comparative Analysis of Scoping and Boundary Setting Approaches

The approach to scoping and defining boundaries varies significantly across disciplines, reflecting their unique goals and constraints. The following table provides a structured, high-level comparison of three key frameworks.

Table 1: Comparison of Scoping and Boundary Definition Across Frameworks

Aspect	Safe & Sustainable by Design (SSbD) Framework	Traditional Drug Development	Systems Engineering
Primary Goal	Proactively integrate safety and sustainability throughout the innovation process [4] [7].	Ensure safety and efficacy of a therapeutic compound for regulatory approval [25].	Ensure a complex system meets stakeholder needs and expectations throughout its life cycle [26] [27].
Core Scoping Elements	Chemical/Material under consideration, its life cycle, function, (re)design aspects, and innovation maturity [7].	The specific therapeutic compound, target disease, and patient population [25].	The problem, system purpose, stakeholders, constraints, and objectives [26].
Typical System Boundary	The entire life cycle of the chemical or material, from sourcing to end-of-life (cradle-to-grave) [4].	Primarily focused on the drug's efficacy and toxicity profile, expanding through clinical phases [28].	A defined system separated from its environment, with clear interfaces and interactions [24] [27].
Key Boundary Dimensions	Hazard profile, production exposure, use-phase exposure, environmental impact, socio-economic impact [7].	Pharmacodynamics (PD), Pharmacokinetics (PK), Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) [28].	Functional, physical, temporal, and organizational boundaries [24] [26].
Handling of Data Limitations	Iterative application as data becomes available, acknowledging information gaps in early innovation stages [7].	Progressive expansion from preclinical models to human trials, with high attrition due to unexpected toxicity [28].	Acknowledgment of dynamic challenges and uncertainties, managed through risk analysis and trade-off studies [26].

The SSbD Framework: A Life Cycle Perspective

The European Commission's SSbD framework is a holistic, voluntary approach designed to guide the innovation process for chemicals and materials. Its scoping analysis is explicitly context-specific, requiring a clear definition of the goal, scope, and system boundaries before any assessment can begin [4] [7]. This framework is applied iteratively, allowing for the assessment to be refined as data becomes available throughout the innovation process [7]. The system boundary in an SSbD assessment is inherently multi-dimensional, encompassing the entire life cycle of the chemical or material, from raw material sourcing and production to its use and end-of-life disposal [4]. This comprehensive scope ensures that burden-shifting—where improving one aspect worsens another—is minimized. The framework's assessment phase, which includes steps for hazard, exposure, and life cycle assessment, is entirely dependent on the boundaries set during the initial scoping analysis [7].

Traditional Drug Development: A Focused Efficacy and Safety Paradigm

In stark contrast, the traditional drug development process establishes its scope primarily through the lens of therapeutic efficacy and patient safety. The system boundary is initially narrow, focused on the drug-target binding affinity (DTBA) and its specific therapeutic effects [28]. However, this boundary expands progressively and dramatically as the drug candidate moves through the development pipeline. The process is characterized by stage-gates, where a compound must prove its value to proceed. A significant attrition rate of approximately 90% is a hallmark of this process, with a substantial portion of failures attributed to unexpected toxicity identified late in clinical trials or even post-market [28]. This high failure rate underscores a critical challenge: the initial scoping and predictive models used in early development often have inadequate boundaries for fully capturing a compound's complex interactions with biological systems. The system boundary is continually tested and redefined through mandatory preclinical studies and phased clinical trials (Phases I-III), which systematically assess safety, dosage, and efficacy in larger human populations [25].

Systems Engineering: A Holistic Method for Complex Systems

Systems engineering provides a generalized, holistic methodology for defining scope and boundaries for any complex project. It begins with a fundamental step: identifying the problem and the purpose of the solution [26]. This involves a thorough understanding of stakeholder needs, constraints, and objectives, often captured using tools like stakeholder analysis and problem statements. The next step is to explicitly define the system and its environment, specifying functions, features, performance, and—critically—interfaces with other systems [26]. In this discipline, boundaries are recognized as often being arbitrary yet necessary constructs that are defined based on stakeholder perspective and necessity, rather than inherent physical properties [27]. These boundaries are vital for assigning accountability and responsibility, facilitating communication, and enabling the verification and validation of system requirements [27]. The system's life cycle—encompassing development, operation, maintenance, and disposal—is also established, acknowledging that boundaries must be managed over time [26].

Experimental Protocols and Data-Driven Insights

Quantitative Comparison of Methodological Performance

A critical aspect of comparing these frameworks is their performance in predicting outcomes and managing risks. The following table summarizes key performance metrics and methodological focus, drawing from drug development and systems assessment data.

Table 2: Experimental Performance and Methodological Data

Method / Model	Reported Performance / Outcome	Key Limiting Factors / Challenges
Preclinical Animal Models for Human Toxicity	43-63% prediction match between rodent and non-rodent models; <30% for human target organ prediction [28].	Species differences, ethical concerns, high cost, and time-consuming procedures [28].
AI/ML Models for Toxicity Prediction (e.g., QSAR with AI)	87% success rate in classifying compounds across 19 hazard categories, surpassing the 81% rate of conventional in vivo tests [28].	Model accuracy depends on data quality and chemical structure characterization; can struggle with novel compounds [28].
Drug Attrition due to Toxicity	Responsible for approximately one-third of drug candidate withdrawals [28].	Late-stage identification of toxicity during clinical trials or post-marketing significantly increases development costs [28].
SSbD Framework Application	Iterative assessment from early stages to re-design; aims to identify hotspots proactively [7].	Data availability at low Technology Readiness Levels (TRLs); complexity of life cycle assessments [14] [7].

Detailed Experimental Protocol: Implementing an SSbD Scoping Analysis

For researchers aiming to apply the SSbD framework, the initial scoping analysis is a critical, multi-step process. The workflow below outlines the key stages.

Diagram 1: SSbD Scoping Workflow

• Define the Goal and Purpose: Clearly articulate the objective of the SSbD assessment. This includes determining if the goal is to compare alternatives, identify environmental or safety hotspots for a new chemical, or improve an existing product through re-design [24] [7].
• Identify the Chemical or Material: Precisely define the substance under assessment, including its chemical structure and composition [7].
• Define the Functional Unit: Establish a quantitative measure of the service provided by the system. For example, a functional unit could be "serving 250ml of hot coffee at 80°C" for a cup, ensuring comparability in subsequent analyses [24].
• Map the Life Cycle: Outline all stages of the product's life cycle, typically including raw material acquisition, manufacturing, transportation, use, and end-of-life management [24] [7]. This map forms the backbone of the system boundary.
• Set the Innovation Maturity Level: Determine the Technology Readiness Level (TRL) of the innovation, as the depth of the assessment and data availability will depend on this stage [14].
• Establish System Boundaries: Make explicit decisions on the key dimensions of the boundary:
  • Geographical Scope: Define the spatial extent of the assessment (e.g., local, regional, global) [24].
  • Temporal Scope: Set the time horizon for the analysis, which is crucial for capturing long-term impacts [24].
  • Organizational Scope: Define the boundaries of responsibility, determining whether the assessment includes only direct operations or extends to the entire supply chain (upstream) and product use/disposal (downstream) [24].
• Document and Iterate: Clearly document all scoping decisions and the finalized system boundaries. This documentation provides the necessary foundation for the subsequent SSbD assessment steps (hazard, exposure, and life cycle assessment) and should be revisited as the innovation matures and more data becomes available [7].

The Scientist's Toolkit: Essential Research Reagents and Solutions

The following table details key computational and methodological tools that are essential for conducting modern, data-driven scoping and safety assessments.

Table 3: Key Research Reagents and Solutions for Predictive Assessment

Tool / Solution	Function / Description	Application Context
Quantitative Structure-Activity Relationship (QSAR) Tools	Computational models that predict biological activity and toxicity based on a compound's chemical structure [28].	Used in early drug discovery and SSbD Step 1 (hazard assessment) to prioritize compounds and flag potential hazards.
Artificial Intelligence (AI) / Machine Learning (ML) Models	Algorithms that integrate vast datasets (drug structures, target proteins, toxicity) to predict adverse effects with high accuracy [28].	Employed to predict drug toxicity end points, classify compounds into hazard categories, and identify off-target effects.
Molecular Docking Software	Simulates how a small molecule (e.g., a drug candidate) binds to a target protein, estimating binding affinity [28].	Used to understand Drug-Target Interactions (DTIs) and predict both therapeutic efficacy and potential off-target toxicity.
Life Cycle Assessment (LCA) Databases & Software	Provide inventory data and models for evaluating environmental impacts across a product's full life cycle [24].	Critical for conducting the environmental sustainability assessment (Step 4) within the SSbD framework.
Classification, Labelling and Packaging (CLP) Regulation Data	The EU's system for classifying and labeling hazardous chemicals, defining specific hazard categories and criteria [7].	Forms the basis for the hazard assessment in SSbD Step 1, ensuring alignment with regulatory standards.

The comparative analysis reveals that while the traditional drug development process is highly refined for assessing clinical efficacy and safety, its reactive nature and high attrition rate point to strategic weaknesses in its initial scoping of system boundaries, particularly concerning off-target effects and chronic toxicity [28]. The systems engineering approach offers a robust, generalized methodology for boundary definition that emphasizes stakeholder alignment and life cycle thinking, principles that are highly transferable [26] [27]. The SSbD framework emerges as a holistic and proactive paradigm. Its principal strength lies in its mandatory, comprehensive scoping analysis that establishes a cradle-to-grave system boundary from the outset [7]. This forces the consideration of all dimensions of safety and sustainability early in the innovation process, potentially preventing costly late-stage failures and guiding the development of truly sustainable products. For researchers and drug development professionals, integrating the rigorous scoping and expansive boundary definition of the SSbD framework, informed by the structured principles of systems engineering, presents a powerful pathway to de-risk innovation and align it with the overarching goals of safety and sustainability.

The "Safe and Sustainable by Design" (SSbD) framework, a voluntary approach endorsed by the European Commission, guides the innovation process for chemicals and materials to substitute substances of concern and minimize impacts on health and the environment [4]. A comprehensive hazard assessment constitutes the foundational first step in this framework's assessment phase, which also includes evaluating worker exposure, use-phase exposure, and a full life-cycle assessment [4]. This initial step is critical for steering the development of chemicals and materials towards safer and more sustainable profiles, ultimately protecting human health and bolstering industrial competitiveness [14]. For researchers and drug development professionals, a rigorous and well-structured hazard assessment is indispensable for making informed decisions early in the research and development pipeline, aligning scientific innovation with safety and sustainability goals.

The core objective of a comprehensive hazard assessment is the systematic identification of the intrinsic toxicological properties of a chemical or material and the subsequent analysis of the quantitative relationship between dose and toxic response [29]. This process aims to answer two fundamental questions: What specific forms of toxicity (e.g., neurotoxicity, carcinogenicity, organ damage) can the substance cause? And under what conditions of exposure might these toxic effects manifest in humans? [29] The answers provide the necessary evidence base to classify and label substances, set safe exposure limits, and identify the need for risk management measures, thereby forming the bedrock of subsequent risk assessment and mitigation strategies.

Comparison of Hazard Assessment Methodologies

A variety of methodologies exist for conducting hazard assessments, each with distinct applications, data requirements, and outputs. The choice of method often depends on the stage of development, the required regulatory context, and the nature of the chemical substance. The table below provides a structured comparison of key hazard assessment approaches relevant to researchers and scientists.

Table 1: Comparison of Key Hazard Assessment Methodologies

Methodology	Primary Application & Scope	Key Data Inputs	Typical Outputs	Key Advantages & Limitations
Epidemiologic Studies [29]	Observational studies on human populations to identify cause-effect relationships.	Historical exposure data, health records from exposed human populations.	Evidence on causality in humans; strength of association (e.g., IARC classifications).	Advantage: Most relevant data for human health.Limitation: Difficult to control variables; exposure data often limited.
Animal Studies [29]	Controlled laboratory experiments to identify the full spectrum of toxic effects.	Data from toxicity tests in rodents and other species following OECD Test Guidelines.	Identification of toxic effects (e.g., target organs); determination of NOAEL, LOAEL.	Advantage: Controlled conditions; establishes dose-response.Limitation: Requires interspecies extrapolation; high cost and ethical considerations.
Hazard and Operability Study (HAZOP) [30] [31]	Systematic, qualitative examination of a process or operation to identify deviations.	Process flow diagrams, piping and instrumentation diagrams, operating procedures.	Qualitative list of potential hazards, deviations, and their causes.	Advantage: Structured and comprehensive for process hazards.Limitation: Time-consuming; requires detailed process information.
Inherent Safety Indices (e.g., ISI, i-Safe) [30]	Comparative assessment of process routes at early design stages based on inherent properties.	Chemical properties (toxicity, flammability), process conditions (temperature, pressure).	Numerical scores for ranking and comparing design alternatives.	Advantage: Promotes inherently safer design; useful for early-stage decisions.Limitation: Often relative rankings; may not quantify absolute risk.
Fire & Explosion Indices (e.g., Dow's F&EI) [30]	Assessment of fire and explosion potential of process units, primarily for facility siting.	Material factors (flammability, reactivity), process parameters (pressure, quantity).	Numerical index rating the relative fire/explosion risk of a process unit.	Advantage: Well-established; focuses on major physical hazards.Limitation: Often considers only the most hazardous component.

For assessing human health effects, regulatory decisions rely heavily on data from animal studies and epidemiologic studies [29]. The reliability of these experimental datasets is paramount; it is evaluated based on adherence to Good Laboratory Practice (GLP) and OECD Test Guidelines, as well as factors like study design, statistical methods, and transparent reporting [32]. The NIOSH Pocket Guide to Chemical Hazards serves as a valuable resource, consolidating key industrial hygiene data for hundreds of chemicals, including exposure limits and toxicological information [33].

For process safety, methodologies like HAZOP and various safety indices (e.g., Dow's Fire & Explosion Index, Inherent Safety Index) are employed to identify and assess hazards associated with chemical processing [30] [31]. These tools are particularly useful for evaluating the inherent safety of a process during the design phase, allowing for the identification of hazardous streams and the comparison of different process routes [30].

Experimental Protocols for Key Hazard Assessments

A robust hazard assessment for regulatory submission relies on standardized, reliable experimental protocols. Below are detailed methodologies for two critical types of studies.

Protocol for a Guideline 28-Day Repeated Dose Toxicity Study in Rodents

This study is designed to identify the toxicological effects of a substance following repeated daily administration for 28 days, including the determination of a No-Observed-Adverse-Effect Level (NOAEL).

• 1. Test System and Animal Husbandry: Use young, healthy rodents (typically rats). Assign animals randomly to control and treatment groups. House them under standard conditions (controlled temperature, humidity, 12-hour light/dark cycle) and provide standard lab diet and water ad libitum.
• 2. Test Article and Dose Preparation: Characterize the test substance (identity, purity, batch). Prepare formulations daily. Select at least three dose levels and a vehicle control group to establish a dose-response relationship and identify the NOAEL. The highest dose should elicit toxicity but not severe suffering or mortality.
• 3. Administration: Administer the test article daily for 28 days via the intended human exposure route (e.g., oral gavage, dermal application, inhalation). The volume of administration should be standardized.
• 4. In-Life Observations and Measurements: Conduct daily clinical observations for morbidity and mortality. At least twice daily, check for signs of toxicity. Record body weight and food consumption weekly.
• 5. Clinical Pathology: At termination, collect blood samples for haematology (e.g., red and white blood cell counts) and clinical chemistry (e.g., indicators of liver and kidney function). Collect urine for analysis if necessary.
• 6. Necropsy and Histopathology: Perform a full necropsy on all animals. Weigh preserved organs (liver, kidneys, heart, spleen, brain, adrenals, testes). Conduct a thorough histopathological examination on all tissues from the control and high-dose groups, and on any target organs identified across all dose groups.

Protocol for anIn VitroBacterial Reverse Mutation Assay (Ames Test)

This is a standardized, high-throughput assay to assess the mutagenic potential of a chemical, which is a key indicator of genotoxicity and carcinogenic hazard.

• 1. Test Strains: Use several histidine-dependent strains of Salmonella typhimurium (e.g., TA98, TA100, TA1535, TA1537) and Escherichia coli (e.g., WP2 uvrA). Confirm their genotype through periodic checks.
• 2. Metabolic Activation: Incorporate a metabolic activation system (typically S9 fraction from rat liver induced with Aroclor 1254) to mimic mammalian metabolic conditions. Perform tests with and without this system.
• 3. Dose Selection and Treatment: Test a minimum of five concentrations of the test substance. Include appropriate vehicle controls and positive controls. Perform the test using either the plate incorporation or pre-incubation method. For the pre-incubation method, mix the test strain, test substance, and buffer (or S9 mix) and incubate for 20-90 minutes before adding to top agar.
• 4. Plate Preparation and Incubation: Add the mixture (bacteria, test substance, metabolic activation system if used) to molten top agar and pour onto minimal glucose agar plates. Incubate the plates at 37°C for 48-72 hours.
• 5. Data Analysis and Evaluation: Count the number of revertant colonies per plate. A positive result is concluded if there is a reproducible, statistically significant increase in the mean number of revertant colonies in one or more strains compared to the vehicle control, and if the response shows a dose-response relationship.

The workflow for these core hazard identification tests is outlined below, showing the progression from in vitro screening to more complex in vivo studies.

Successful execution of a comprehensive hazard assessment requires a suite of reliable reagents, reference materials, and data resources. The following table details key components of the researcher's toolkit.

Table 2: Key Research Reagent Solutions for Hazard Assessment

Tool/Reagent	Function in Hazard Assessment	Specific Application Example
OECD Test Guidelines [32]	Provide internationally agreed-upon testing methods for chemical safety.	Ensure regulatory acceptance of data; standardize protocols for studies like the 28-day toxicity test or Ames test.
S9 Metabolic Activation System	Provides mammalian liver enzymes to metabolize test chemicals in in vitro assays.	Used in the Ames test to detect mutagens that require enzymatic conversion to become active.
Positive Control Substances	Verify the sensitivity and responsiveness of the test system.	Sodium azide for TA100 strain (-S9) in Ames test; known toxicants in in vivo studies.
NIOSH Pocket Guide [33]	Reference database providing key industrial hygiene and toxicological data.	Quick access to OSHA PELs, NIOSH RELs, IDLH values, and physical properties for hazard screening.
Histopathology Reagents (e.g., formalin, H&E stain)	Preserve and stain tissues for microscopic examination of morphological changes.	Identify target organ toxicity in in vivo studies (e.g., liver necrosis, kidney damage).
Clinical Pathology Analyzers	Automated analysis of blood and urine for biochemical and cellular markers.	Quantify indicators of organ function/damage (e.g., ALT, AST for liver; creatinine for kidney).

Data Integration and Weight of Evidence in the SSbD Context

The final phase of a comprehensive hazard assessment involves the integration and evaluation of all available data. This "weight of evidence" approach is crucial for drawing robust conclusions [32]. Data from guideline-compliant studies, peer-reviewed literature, and in vitro and in vivo experiments are assessed for their relevance (appropriateness for the assessment question) and reliability (trustworthiness and integrity) [32]. For instance, evidence of carcinogenicity is considered strongest when a chemical causes malignancies in multiple species and sexes of test animals [29].

Within the SSbD framework, the outcomes of the hazard assessment directly inform the subsequent stages of the assessment phase, which include evaluating worker exposure during production and consumer exposure during use [4]. The hazard data are essential for determining the need for, and effectiveness of, risk management measures to control exposure. A chemical identified as a potent sensitizer, for example, would necessitate stringent engineering controls or formulation changes to minimize the potential for exposure, thereby aligning with the SSbD goal of minimizing impacts on health [4]. This integrated assessment, covering both hazard and exposure, provides a solid foundation for the final life-cycle assessment, enabling a holistic judgement on the safety and sustainability of a chemical or material.

The European Commission's Safe and Sustainable by Design (SSbD) framework represents a paradigm shift in chemical and material innovation, moving from reactive risk management to proactive safety and sustainability integration [17]. This voluntary approach, announced in a 2022 Commission Recommendation, guides innovators to embed safety and sustainability considerations from the earliest stages of product development [4]. The framework follows a two-phase structure consisting of a (re-)design phase and an assessment phase, applied iteratively as data becomes available throughout the innovation process [4] [17].

Within this structure, Step 2: Human Health and Safety Assessment During Production and Processing and Step 3: Human Health and Environmental Safety Assessment During Use serve as critical pillars for evaluating impacts across the chemical or material lifecycle [17]. These steps employ a tiered approach, with the depth of assessment scaling according to the innovation's Technology Readiness Level (TRL) and data availability [34]. For researchers and drug development professionals, understanding these assessment stages is essential for developing products that are not only functionally effective but also inherently safer and more sustainable.

Step 2: Human Health and Safety in Production

Core Objectives and Assessment Framework

Step 2 of the SSbD framework focuses on protecting worker health and safety during industrial production and processing operations [7] [17]. This stage requires a systematic evaluation of potential exposure routes and risks throughout manufacturing, purification, and formulation processes. The assessment aims to identify and mitigate hazards before production scales, aligning with the proactive philosophy of SSbD [17].

The European Commission's Joint Research Centre (JRC) framework emphasizes that Step 2 assessment should consider both human health and environmental safety aspects specifically related to production stages [7]. For drug development professionals, this translates to evaluating risks during active pharmaceutical ingredient (API) synthesis, purification, formulation into final dosage forms, and packaging operations. The assessment becomes increasingly quantitative as innovation progresses from laboratory to pilot and commercial scale.

Methodological Approach and Data Requirements

The methodological approach for Step 2 integrates hazard information from Step 1 with exposure assessment to characterize risks during production [34]. The tiered approach begins with screening-level assessments using readily available data and progresses to more sophisticated quantitative methods as needed.

Table 1: Methodologies for Step 2 Human Health and Safety Assessment

Assessment Tier	Methodology	Data Requirements	Output Metrics
Tier 1 (Screening)	Qualitative exposure assessment using control banding	Safety Data Sheets, physicochemical properties, process descriptions	Risk bands (e.g., low, medium, high)
Tier 2 (Intermediate)	Semi-quantitative modeling (e.g., ECETOC TRA, MEASE)	Process parameters, operational conditions, hazard classification	Exposure estimates (mg/m³), Risk Characterization Ratios (RCR)
Tier 3 (Advanced)	Quantitative exposure monitoring and modeling	Workplace measurement data, physiologically-based pharmacokinetic modeling	Internal dose metrics, refined RCR values

For advanced materials and novel chemical entities used in pharmaceutical development, specific adaptations to conventional assessment methods may be necessary due to their unique physicochemical properties and potential novel hazard profiles [34]. The Scientific Committee on Occupational Exposure Limits (SCOEL) methodologies and REACH guidance on chemical safety assessments provide valuable resources for establishing occupational exposure limits, particularly when substance-specific toxicity data is limited.

Experimental Protocols and Exposure Assessment

Practical implementation of Step 2 assessment requires structured protocols for exposure evaluation. The following workflow represents a comprehensive approach to production safety assessment:

The exposure assessment protocol involves both modeling and measurement approaches. For initial screening, standardized tools like the ECETOC Targeted Risk Assessment (TRA) tool or the EMKG-Exposure Tool provide efficient first-tier assessments. For more advanced stages, computational fluid dynamics modeling of workplace airflow or near-field/far-field exposure modeling offer greater precision. Experimental monitoring using personal air sampling pumps with appropriate collection media, dermal samplers, and biological monitoring provides validation data for model predictions.

Step 3: Safety Assessment During Use

Core Objectives and Assessment Framework

Step 3 transitions the assessment focus to the use phase of chemicals, materials, or products, evaluating potential impacts on professional users, consumers, and ecosystems during application [7] [17]. For pharmaceutical products, this encompasses everything from clinical administration by healthcare professionals to patient use and eventual environmental release through excretion. The European Commission's framework specifically highlights that Step 3 addresses "exposure and potential harm of the innovation during its use and on ecosystems" [17].

This assessment stage requires consideration of diverse exposure scenarios based on product application, user behavior, and environmental release pathways. The framework adopts a lifecycle thinking approach, recognizing that impacts during use cannot be evaluated in isolation from preceding production stages or subsequent end-of-life phases [7]. For drug development, this holistic perspective is particularly relevant given the potential for active pharmaceutical ingredients to persist through metabolism and wastewater treatment to affect aquatic ecosystems.

Methodological Approach and Data Requirements

The methodological approach for Step 3 integrates product-specific exposure assessment with environmental fate and effects evaluation. The tiered assessment strategy progresses from conservative screening to more refined, product-specific modeling as data availability increases.

Table 2: Methodologies for Step 3 Use Phase Safety Assessment

Assessment Component	Tier 1 Methods	Tier 2 Methods	Tier 3 Methods
Human Exposure Assessment	Conservative screening models (e.g., EUSES), read-across	Product-specific exposure modeling (e.g., ConsExpo), market use data	Probabilistic exposure modeling, biomonitoring studies
Environmental Fate & Exposure	Predictive partitioning models, QSAR for degradation	Region-specific fate modeling, treatability studies	Field monitoring, mesocosm studies
Ecological Effects Assessment	Standard laboratory toxicity tests (algae, daphnia, fish)	Multi-species tests, mode-of-action studies	Population-level modeling, endemic species testing

For pharmaceutical products with specific biological activity, environmental risk assessment requires particular attention to potential effects on non-target organisms. The European Medicines Agency (EMA) guidelines on environmental risk assessment of medicinal products provide a sector-specific framework that aligns with SSbD principles. The assessment must account for metabolite activity, potential bioaccumulation, and chronic effects at environmentally relevant concentrations.

Experimental Protocols for Use Phase Assessment

Implementing Step 3 assessment requires structured protocols for evaluating exposure during product use and potential environmental impacts. The following workflow outlines the key assessment elements:

Experimental protocols for use phase assessment include standardized OECD guidelines for environmental fate testing (e.g., hydrolysis, photodegradation, soil adsorption) and ecotoxicological effects (e.g., algal growth inhibition, Daphnia reproduction, fish toxicity). For human exposure during use, controlled usage studies with appropriate analytical methods to quantify personal exposure are essential. Higher-tier assessments may include environmental monitoring programs to validate predicted concentrations and advanced ecological modeling to project population-level impacts.

Comparative Analysis of Assessment Approaches

Methodologies Across Technology Readiness Levels

The implementation of Steps 2 and 3 assessments varies significantly based on the innovation's maturity level. Early-stage research (TRL 1-3) typically employs qualitative and screening-level approaches, while development stages (TRL 4-6) incorporate more quantitative methods, and near-market innovations (TRL 7-9) require comprehensive, data-rich assessments.

Table 3: Assessment Approach by Innovation Maturity Level

Technology Readiness Level	Step 2: Production Assessment	Step 3: Use Phase Assessment	Data Quality Requirements
TRL 1-3 (Basic Research)	Hazard banding, qualitative exposure assessment	Read-across from analogous substances, conservative release estimates	Limited to available analog data, computational predictions
TRL 4-6 (Technology Development)	Exposure modeling with generic scenarios, initial monitoring	Substance-specific fate testing, refined exposure scenarios	Experimental data on key parameters, QSAR predictions
TRL 7-9 (System Demonstration & Operation)	Workplace monitoring, quantitative risk assessment	Environmental monitoring, use-specific exposure measurements	Comprehensive experimental data, real-world validation

This tiered approach acknowledges practical constraints while maintaining scientific rigor. As noted in recent research, "The framework accommodates different stages of innovation maturity" and can be "applied from the early stages of innovation to the re-assessment of existing products" [7]. This flexibility is particularly valuable for pharmaceutical development with its extended research and development timelines.

Interplay with Regulatory Frameworks

A critical consideration for researchers is how SSbD assessment aligns with existing regulatory requirements. The information generated through Steps 2 and 3 can significantly support compliance with regulations such as REACH, CLP, and sector-specific frameworks like the EMA guidelines for medicinal products.

The hazard profile identified in Step 1 directly informs the requirements for Steps 2 and 3 assessments, creating a logical flow of information that mirrors regulatory data needs [7]. This alignment creates efficiency opportunities, as data generated for SSbD assessment can often be repurposed for regulatory submissions, while regulatory requirements can inform the scope and methodology of SSbD evaluations. This reciprocal relationship "ensures a reciprocal flow of information between innovation and compliance efforts" [7].

Research Reagents and Methodological Tools

Implementing robust SSbD assessments requires specialized reagents, reference materials, and methodological tools. The following table details essential resources for conducting Steps 2 and 3 evaluations:

Table 4: Essential Research Reagents and Methodological Tools for SSbD Assessment

Category	Specific Tools/Reagents	Function in Assessment	Applicable Standards
Analytical Standards	Certified reference materials, isotope-labeled analogs	Quantification of substance concentrations in exposure media	ISO Guide 34, ERM certification
Bioassay Kits	Bacterial toxicity screens (Microtox, Mutatox), algal growth kits	Rapid screening of ecotoxicological effects	OECD TG 201, 202, 471
Exposure Modeling Software	ECETOC TRA, EUSES, ConsExpo, Stoffenmanager	Prediction of occupational and consumer exposure	EC JRC validated models
Fate Testing Kits	Ready-biodegradation test systems, chemical transformation kits	Assessment of environmental persistence	OECD TG 301, 307, 308
Computational Tools	QSAR models, read-across platforms, PBPK modeling	Data gap filling, extrapolation across scenarios	OECD QSAR Validation Principles

Recent methodological analyses have identified specific gaps in assessment tools, particularly for advanced materials, noting challenges in "adapting conventional testing and modelling approaches to advanced materials, particularly for hazard assessment" [34]. The European Commission is addressing these gaps through ongoing research initiatives, including the Partnership for the Assessment of Risks from Chemicals (PARC), which is developing an SSbD toolbox and knowledge sharing portal [7].

Steps 2 and 3 of the SSbD framework provide a structured, science-based approach to evaluating human health and environmental safety across production and use phases of chemical and product lifecycles. For pharmaceutical researchers and development professionals, implementing these assessments from the earliest innovation stages enables proactive risk identification and mitigation, potentially avoiding costly redesign and facilitating regulatory acceptance.

The iterative nature of the framework allows assessment depth to scale with innovation maturity, making efficient use of limited data in early research stages while supporting comprehensive evaluation as products approach commercialization. As the European Commission continues to refine the SSbD framework through stakeholder consultation and case study testing [14], the methodology and tools for Steps 2 and 3 assessments will continue to evolve, offering increasingly robust support for sustainable innovation in pharmaceutical development and beyond.

Within the European Commission's Safe and Sustainable by Design (SSbD) framework, Life Cycle Assessment (LCA) serves as the foundational methodology for quantifying the environmental sustainability of chemicals and materials [7]. The SSbD framework, a voluntary pre-market approach, integrates safety and sustainability considerations throughout the entire innovation process, with environmental sustainability assessment formally addressed in its Step 4 [7]. This systematic integration ensures that environmental impacts are evaluated from raw material extraction to end-of-life disposal, supporting the development of safer and more sustainable products while strengthening industrial competitiveness [14] [7].

LCA has evolved from a primarily environmental impact assessment tool into a critical decision-support system for integrated sustainability planning [35]. Its incorporation into the SSbD framework enables researchers and drug development professionals to make data-driven decisions during early development stages, identifying environmental hotspots and comparing the relative sustainability of alternative synthetic pathways, excipients, or formulation technologies [7] [36]. As global research output in LCA has grown at a compound annual growth rate of 18% (increasing from 35 annual publications in 2000 to 1,882 in 2024), the methodology has become increasingly sophisticated, now regularly informing major EU initiatives such as the Ecodesign Directive and the Circular Economy Action Plan [35].

Core LCA Methodology According to ISO Standards

The International Organization for Standardization (ISO) standards 14040 and 14044 provide the definitive framework for conducting LCA studies, structuring the process into four interconnected stages [37]. This standardized approach ensures scientific rigor, reproducibility, and comparability across different assessments—attributes essential for evaluating chemical products within SSbD frameworks.

The Four Stages of LCA

• Stage 1: Goal and Scope Definition - This stage establishes the study's purpose, intended application, and target audience. It precisely defines the system boundaries, ensuring all relevant life cycle stages are included, and establishes the functional unit which provides a standardized reference for quantifying inputs and outputs, enabling fair comparisons between alternative products or processes [37] [38].
• Stage 2: Life Cycle Inventory (LCI) Analysis - This stage involves the systematic compilation and quantification of input and output data for the product system throughout its entire life cycle. Inputs typically include energy, raw materials, and water, while outputs encompass emissions to air, water, land, and waste products [37] [35].
• Stage 3: Life Cycle Impact Assessment (LCIA) - The inventory data is translated into potential environmental impacts using standardized impact categories. Common categories used in SSbD assessments include climate change potential, fossil resource depletion, human toxicity, and particulate matter formation [39]. The SSbD framework specifically incorporates an Environmental Sustainability Assessment benchmark to evaluate these impacts [14].
• Stage 4: Interpretation - This final stage involves analyzing the results from the LCIA, checking their sensitivity and consistency, and formulating conclusions, limitations, and evidence-based recommendations to support sustainable decision-making [37].

Table 1: The Four Stages of Life Cycle Assessment According to ISO 14040/14044

Stage	Key Components	Application in SSbD Context
1. Goal & Scope	Purpose, functional unit, system boundaries	Defines the chemical's function and assessment boundaries for SSbD evaluation [7]
2. Inventory Analysis	Resource/energy inputs, emission outputs	Quantifies material/energy flows for chemical production and use [37]
3. Impact Assessment	Climate change, human toxicity, resource depletion	Assesses environmental impacts against SSbD sustainability criteria [14] [39]
4. Interpretation	Result analysis, conclusions, recommendations	Identifies improvement areas for safer, more sustainable chemical design [37]

LCA Workflow within the SSbD Framework

The following diagram illustrates how LCA is integrated within the broader SSbD assessment process, particularly as Step 4, and how its results inform subsequent evaluations.

Experimental Protocols for LCA in Chemical Research

Implementing LCA for chemical products requires rigorous experimental protocols and data collection strategies. The following section outlines established methodologies for conducting LCAs, with a specific focus on protocols relevant to pharmaceutical and chemical development.

Data Collection and Inventory Methodology

The Mistra SafeChem programme has established robust protocols for LCA data generation and collection, emphasizing a multi-disciplinary approach that combines chemistry, toxicology, and sustainability assessment [36]. The key steps include:

• Process Simulation and Scaling: Laboratory-scale synthesis data is scaled to industrial production levels using process simulation software. Key parameters include reaction yields, energy consumption, solvent volumes, and catalyst loading [36].
• Inventory Data Compilation: All material and energy inputs are quantified, including:
  • Raw material extraction and processing
  • Energy consumption for synthesis and purification
  • Solvent use and recovery rates
  • Water consumption and wastewater generation
  • Air emissions and solid waste production [36]
• Waste Stream Characterization: All waste streams are characterized by composition, treatment method, and final disposal pathway to accurately model end-of-life impacts [36].

Impact Assessment Protocol

The LCIA follows a standardized protocol aligned with the European Commission's Environmental Footprint (EF) method, which is increasingly recommended as the standard for evaluating the environmental performance of products and organizations [35]. The protocol includes:

• Selection of Impact Categories: The assessment focuses on impact categories most relevant to chemical production:
  • Climate Change (Global Warming Potential - GWP)
  • Human Toxicity (cancer and non-cancer effects)
  • Ecotoxicity (freshwater, marine, terrestrial)
  • Particulate Matter Formation
  • Fossil Resource Depletion
  • Water Depletion [39] [35]
• Characterization Modeling: Inventory data is converted to impact category indicators using scientifically established characterization factors (e.g., CO₂-equivalents for climate change, 1,4-DCB-equivalents for human toxicity) [35].
• Normalization and Weighting (Optional): Results may be normalized to a reference value (e.g., annual per capita emissions) and weighted to reflect relative importance, though this step is often omitted in comparative assertions [37].

Table 2: Key Impact Categories and Assessment Methods for Chemical LCA

Impact Category	Indicator Unit	Primary Contributing Flows	Relevance to SSbD
Climate Change	kg CO₂-equivalent	CO₂, CH₄, N₂O from energy use	Addresses EU Green Deal climate goals [36]
Human Toxicity	kg 1,4-DCB-equivalent	Chemical emissions to air/water	Critical for chemical safety assessment [7]
Fossil Resource Depletion	kg oil-equivalent	Natural gas, crude oil extraction	Measures resource efficiency [39]
Particulate Matter	kg PM2.5-equivalent	SOₓ, NOₓ, NH₃ emissions	Protects human health & air quality [39]

Comparative LCA Data: Case Studies and Experimental Findings

Case Study: Sustainable Cementitious Composites

A recent LCA study of ultra-high strength engineered cementitious composites (UHS-ECC) provides an exemplary model for quantitative comparative LCA in material science [39]. The study compared conventional formulations with alternatives incorporating recycled concrete powder (RCP) and waste tire steel fiber (WTSF), yielding the following experimental data:

Table 3: Comparative LCA Results for Conventional vs. Sustainable UHS-ECC Formulations

Formulation	Climate Change Potential (GWP₁₀₀)	Fossil Resource Depletion	Human Toxicity	Compressive Strength
Conventional ECC	100% (baseline)	100% (baseline)	100% (baseline)	115-120 MPa
RCP-5-ECC	84% of baseline	81% of baseline	89% of baseline	129 MPa
RCP-10-ECC	87% of baseline	85% of baseline	92% of baseline	121 MPa
RCP-15-ECC	91% of baseline	88% of baseline	95% of baseline	118 MPa

Data derived from experimental LCA in Scientific Reports [39]

Key Findings: The 5% RCP replacement formulation achieved a 16% reduction in climate change potential and a 19% reduction in fossil resource depletion while simultaneously increasing compressive strength by approximately 8% compared to conventional ECC [39]. This demonstrates the potential for sustainable design to achieve both environmental and performance benefits—a core objective of the SSbD framework.

LCA of Chemical Synthesis Pathways

Research from the Mistra SafeChem programme demonstrates the application of LCA to evaluate novel chemical synthesis routes, with a focus on catalysis and biocatalysis [36]. The experimental protocol involves:

• Baseline Establishment: Conventional synthesis routes are modeled using industry-average data.
• Alternative Route Development: Novel synthesis pathways are developed using advanced catalysis (e.g., selective catalysts to reduce step count) or biocatalysis (e.g., enzymatic reactions under mild conditions).
• Comparative Assessment: Both routes are evaluated using consistent system boundaries and impact assessment methods.

Experimental Findings: Case studies from Mistra SafeChem demonstrate that optimized catalytic processes can reduce energy consumption by 20-40% and hazardous waste generation by 15-30% compared to conventional routes, primarily through improved atom economy and reduced purification requirements [36].

Advanced Methodological Considerations

Modeling Product Lifetime in Circular Economy Contexts

For LCAs evaluating circular economy strategies like reuse, repair, or remanufacturing, product lifetime modeling becomes critically important [38]. Three distinct approaches have been identified, each suitable for different assessment goals:

Table 4: Product Lifetime Modeling Approaches for LCA of Circular Strategies

Modeling Approach	Methodology	Typical Application Questions	Suitability for SSbD
Single Fixed Values	Uses a single, fixed lifetime value in LCA calculations	Identifying environmental hotspots; burden-shifting analysis	Suitable for initial screening assessments [38]
No-Fixed Value	Lifetime is treated as a variable; results plotted against lifetime	Determining break-even points for lifetime extension; sensitivity analysis	Ideal for evaluating reuse/repair strategies [38]
Distribution	Uses a distribution of lifetime values across a user population	Assessing average impact change across a population; variability analysis	Best for policy development and market-level assessment [38]

Uncertainty and Sensitivity Analysis Protocol

Robust LCA practice requires comprehensive uncertainty and sensitivity analysis, particularly for prospective assessments of novel chemicals [35]. The recommended protocol includes:

• Data Quality Assessment: Evaluating data sources based on precision, completeness, representativeness, consistency, and reproducibility.
• Uncertainty Propagation: Using statistical methods (e.g., Monte Carlo simulation) to propagate uncertainty through the LCA model.
• Sensitivity Analysis: Systematically varying key parameters (e.g., energy sources, yields, allocation methods) to identify which parameters most influence the results.

Research Toolkit for LCA Implementation

Table 5: Essential Research Reagents and Computational Tools for LCA

Tool/Resource	Type	Primary Function	Application Example
OpenLCA	Software platform	LCA modeling and calculation	Conducting inventory analysis and impact assessment [39]
Ecoinvent Database	LCA database	Provides background inventory data	Modeling energy, transport, and material production impacts [39]
Brightway	Computational framework	LCA calculations via Python scripting	Advanced modeling, uncertainty analysis, parameterized studies [38]
In Silico Hazard Tools	Predictive models	Early-stage hazard screening of chemicals	Predicting human/ecotoxicity endpoints for novel chemicals [36]
EPD (Environmental Product Declaration)	Standardized data	Third-party verified product LCA data	Sourcing validated environmental data for materials [40]

Visualization Techniques for LCA Results

Effective communication of LCA findings is essential for supporting decision-making within research teams and for stakeholders. Advanced visualization techniques include:

• Hotspot Analysis Diagrams: Visual representations identifying environmental impact hotspots across the life cycle stages.
• Comparative Radar Charts: Multi-impact category comparisons between alternative products or processes.
• Contribution Analysis Trees: Hierarchical charts showing the relative contribution of different life cycle stages to overall impacts.

The following workflow illustrates the integrated data and tool flow in a modern LCA study, highlighting the connection between primary data collection, background data sources, software tools, and final visualization outputs.

Life Cycle Assessment provides the quantitative foundation for environmental sustainability evaluation within the SSbD framework. By implementing standardized ISO-compliant methodologies, employing robust experimental protocols for data collection, and utilizing advanced visualization techniques, researchers can generate reliable, comparable environmental impact data. The integration of LCA early in the chemical and pharmaceutical development process enables evidence-based decision-making that aligns with the core objectives of the SSbD framework: fostering innovation while ensuring safety and sustainability throughout the product life cycle. As LCA methodology continues to evolve, incorporating more sophisticated approaches for addressing circular economy strategies and uncertainty analysis, its value as a decision-support tool for researchers and drug development professionals will further increase.

The Safe and Sustainable by Design (SSbD) framework represents a transformative approach in chemical and advanced materials innovation, shifting safety and sustainability considerations from post-market compliance to integrated design elements [7]. Established as a voluntary pre-market approach by the European Commission in December 2022, SSbD aims to steer the innovation process toward clean and sustainable industries, substitute substances of concern, and minimize impacts on health, climate, and environment throughout the entire life cycle of chemicals, materials, and products [4]. For researchers and drug development professionals, integrating SSbD into conventional Stage-Gate processes provides a structured methodology to systematically address safety and sustainability criteria at each decision point, potentially reducing late-stage failures and enhancing both patient and environmental safety profiles of new therapeutics.

The SSbD framework consists of two synergistic components: a (re-)design phase where goals, scope, and system boundaries are established, and an assessment phase comprising iterative evaluation of hazard, human exposure, and life cycle impacts [4] [22]. This structure aligns naturally with the stage-gate innovation model, where progressive evaluation gates determine advancement, resource allocation, and strategic direction of development projects [41].

SSbD-Stage-Gate Integration Framework

Alignment Methodology

Integrating SSbD into stage-gate processes requires mapping SSbD assessment steps onto specific innovation stages and their corresponding decision gates. This alignment ensures that safety and sustainability considerations evolve in resolution and specificity alongside the innovation maturity level, from early discovery through commercialization.

Table: Mapping SSbD Assessment Steps to Stage-Gate Process

Stage-Gate Phase	SSbD Assessment Focus	Key Decision Criteria
Discovery/Concept	Initial hazard screening; Sustainability design principles	Alignment with green chemistry principles; Absence of substances of concern
Scoping/Feasibility	Preliminary exposure assessment; Life cycle thinking	Technical & financial feasibility including sustainability metrics
Business Case Development	Detailed hazard assessment; Production safety evaluation	Clear business case with safety & sustainability ROI projections
Development/Execution	Quantitative risk assessment; Life Cycle Assessment (LCA)	Milestone achievement with risk mitigation; Safety & sustainability performance
Launch/Commercialization	Monitoring and verification; Post-market surveillance	Market readiness with comprehensive safety & sustainability data

This integrated approach follows a tiered methodology that progresses from qualitative assessments in early stages to quantitative evaluations as projects mature [42] [43]. Early stages employ screening-level tools and design principles, while later stages incorporate comprehensive risk assessments, Life Cycle Assessments (LCA), and socio-economic evaluations [7]. This tiered strategy efficiently manages resources by focusing detailed assessments only on promising candidates while still embedding SSbD principles throughout the innovation funnel.

Visualization of Integration Workflow

The following diagram illustrates how SSbD assessment tiers align with stage-gate decision points throughout the innovation process:

Experimental Protocols for SSbD Assessment

Tiered Assessment Methodology

The operationalization of SSbD within innovation processes employs a two-tiered assessment methodology that aligns with the stage-gate framework [43]:

Tier 1: Qualitative Screening Assessment

• Application Stage: Discovery through Scoping phases (Stage-Gates 1-2)
• Protocol: Structured self-assessment questionnaire evaluating safety, functionality, and sustainability dimensions
• Methodology:
  • Hazard Screening: Assessment against SSbD Group A, B, and C criteria based on CLP regulation hazard categories [7]
  • Exposure Potential: Qualitative evaluation of production and use exposure scenarios
  • Sustainability Hotspotting: Identification of potential environmental, social, and economic impacts across life cycle stages
  • Functionality Assessment: Verification that SSbD alternatives maintain performance requirements
• Output: Identification of potential hotspots and critical issues requiring further investigation in Tier 2

Tier 2: Quantitative Assessment

• Application Stage: Business Case through Development phases (Stage-Gates 3-5)
• Protocol: Comprehensive quantitative assessments using standardized methodologies
• Methodology:
  • Chemical Safety Assessment (CSA): Following REACH requirements for hazard and exposure assessment [43]
  • Life Cycle Assessment (LCA): Quantitative evaluation of environmental impacts across all life cycle stages
  • Life Cycle Costing (LCC): Economic assessment incorporating externalities
  • Social LCA (S-LCA): Evaluation of social impacts on workers, local communities, and other stakeholders
• Output: Quantitative safety and sustainability performance metrics for decision-making

Case Study Experimental Data: PFAS-Free Anti-Sticking Coating

The EU SUNSHINE project provides compelling experimental validation of this integrated approach through a case study developing a PFAS-free anti-sticking coating for the bakery industry [43]. The study demonstrates the application of both Tier 1 and Tier 2 assessments within a stage-gate innovation process.

Table: Quantitative Sustainability Assessment Results - PFAS-Free Coating vs. Conventional Benchmark

Assessment Metric	PFAS-Free Coating	Conventional Benchmark	Improvement
Global Warming Potential (kg CO₂ eq)	12.3	18.7	34% reduction
Human Toxicity Potential (CTUh)	3.2E-07	5.8E-07	45% reduction
Acidification Potential (kg SO₂ eq)	0.08	0.14	43% reduction
Resource Depletion (kg Sb eq)	0.015	0.028	46% reduction
Life Cycle Cost (€/unit)	2.45	3.12	21% reduction

The experimental protocol for this case study followed the tiered approach:

• Tier 1 Assessment: Initial screening identified potential issues with conventional PFAS coatings, including persistence, bioaccumulation, and toxicity concerns
• Design Phase: Application of green chemistry principles to develop alternative formulations using silicon-based compounds and titanium dioxide [22]
• Tier 2 Assessment: Comprehensive LCA, LCC, and S-LCA comparing the novel coating against conventional benchmarks
• Iterative Refinement: Using assessment results to guide reformulation and process optimization

The results demonstrate that the SSbD-guided innovation achieved significant improvements across multiple sustainability dimensions while maintaining functional performance for anti-sticking properties [43].

Research Reagent Solutions

Successful implementation of SSbD in stage-gate processes requires specific methodological tools and assessment frameworks. The following table summarizes key resources developed through EU research initiatives:

Table: Essential SSbD Research Tools and Assessment Frameworks

Tool/Framework	Function	Application Stage	Source Project
FAIR Data Principles	Ensures Findable, Accessible, Interoperable, Reusable data for SSbD assessments	All stages	ASINA Project [44]
SSbD Decision Support System (DSS)	Digital platform for tiered safety and sustainability assessment	Gates 1-5	DIAGONAL, HARMLESS, SUNSHINE [42]
Prospective LCA Methodology	Anticipatory life cycle assessment for early-stage innovation	Gates 1-3	PINK Project [22]
Multi-Criteria Decision Analysis (MCDA)	Framework for balancing safety, sustainability, and functionality criteria	Gates 3-4	ASINA-SMM [44]
SSbD Knowledge Sharing Portal	Repository of assessment methods, case studies, and best practices	All stages	PARC Initiative [7]

Implementation Workflow for Research Teams

The following diagram details the sequential workflow for research teams implementing SSbD assessments at stage-gate decision points:

Discussion: Implementation Challenges and Emerging Solutions

Operationalization Barriers

Despite the clear conceptual framework, practical implementation of SSbD in stage-gate processes faces several significant challenges. The highest priority barrier identified is the "integration of SSbD framework into the innovation process" itself [45]. Research and development teams often struggle with incorporating additional assessment criteria into established workflows, particularly when facing pressure to accelerate time-to-market.

Additional critical challenges include:

• Data availability, quality and uncertainty: Early innovation stages typically suffer from scarce data for comprehensive SSbD assessments [45]
• Integration of safety and sustainability aspects: Effectively balancing and weighting these potentially competing dimensions requires sophisticated decision-support tools [45]
• Resource intensiveness: Comprehensive Tier 2 assessments (LCA, CSA, S-LCA) demand significant expertise, time, and financial resources [42]
• Intellectual property protection: Data sharing across value chains necessary for life cycle assessment must be balanced against proprietary information protection [42]

Emerging Solutions and Research Directions

Several promising approaches are emerging to address these implementation challenges:

FAIR Data Principles Implementation The ASINA project demonstrates how Findable, Accessible, Interoperable, and Reusable (FAIR) data principles can support SSbD implementation through structured data management across material life cycles [44]. This approach enables the data sharing necessary for comprehensive assessments while maintaining provenance and quality tracking.

Computational and AI-Enhanced Methods Machine learning and artificial intelligence approaches are being developed to address data scarcity in early innovation stages [22]. These include:

• Predictive toxicology models for early hazard screening
• Anticipatory LCA methods that estimate environmental impacts with limited data
• Multi-objective optimization algorithms that balance safety, sustainability, and functionality criteria

Tiered Tool Development for SMEs The DIAGONAL, HARMLESS, and SUNSHINE projects have developed a suite of tiered tools specifically designed to lower implementation barriers for small and medium enterprises [42]. These tools range from simple screening-level assessments compatible with corporate sustainability reporting to comprehensive quantitative methods for advanced development stages.

Integrating Safe and Sustainable by Design principles into stage-gate innovation processes represents a strategic imperative for research organizations and drug development companies committed to sustainable development. The tiered assessment methodology—progressing from qualitative screening to quantitative comprehensive evaluations—provides a structured framework for embedding safety and sustainability considerations at each decision point.

Experimental evidence from case studies demonstrates that this integrated approach can yield significant improvements in both environmental safety and economic performance while maintaining functional requirements [43]. The development of specialized tools, standardized assessment protocols, and computational methods continues to lower implementation barriers.

For research professionals, successful SSbD integration requires both technical methodologies—FAIR data management, life cycle assessment, hazard screening—and organizational adaptation—cross-functional collaboration, management buy-in, and balanced performance metrics. As regulatory frameworks increasingly emphasize proactive safety and sustainability management, organizations that master this integration will gain competitive advantage while contributing to broader sustainable development goals.

The continuing refinement of SSbD assessment methods, coupled with their thoughtful integration into stage-gate innovation funnels, promises to accelerate the development of therapeutic agents that are not only effective but also inherently safer and more sustainable throughout their life cycles.

Overcoming Implementation Hurdles: Data Gaps, Tiered Approaches, and Trade-offs

The Safe and Sustainable by Design (SSbD) framework is a voluntary approach established by the European Commission to guide the innovation process for chemicals and materials, integrating safety and sustainability considerations from the earliest stages of research and development [4]. This proactive methodology aims to steer industrial innovation toward the green transition, substitute substances of concern, and minimize impacts on health, climate, and the environment throughout a product's entire life cycle [17]. The SSbD framework represents a fundamental shift from reactive damage control to proactive sustainable innovation, requiring significant integration challenges within existing R&D workflows [17].

Two prominent frameworks currently shape SSbD implementation: the European Commission's SSbD Framework developed by the Joint Research Centre (JRC) and the Safe(r) and Sustainable Innovation Approach (SSIA) developed by the Organisation for Economic Co-operation and Development (OECD) [46]. While both frameworks share common objectives, they differ in their specific requirements, assessment methodologies, and implementation pathways, creating a complex landscape for researchers and drug development professionals to navigate.

Comparative Analysis of Major SSbD Frameworks

The following table summarizes the core characteristics, strengths, and limitations of the two primary SSbD frameworks currently influencing chemical and material innovation:

Table 1: Comparison of Key SSbD Frameworks

Feature	EC JRC SSbD Framework [4] [17] [7]	OECD SSIA Framework [46]
Originating Body	European Commission Joint Research Centre (JRC)	Organisation for Economic Co-operation and Development
Primary Focus	Holistic safety and sustainability across entire life cycle	Innovation-oriented approach for safer alternatives
Regulatory Alignment	Directly supports EU Chemicals Strategy for Sustainability and Green Deal objectives	Broader international applicability beyond specific regulatory regimes
Implementation Status	Methodological guidance published (2022), ongoing revisions through 2025	Earlier development stage, less prescriptive in implementation
Key Differentiator	Five-step assessment protocol with specific criteria	Flexible principles adaptable to various innovation contexts
Industry Adoption	Tested through EU-funded case studies, toolbox under development	Used by multinational companies like Unilever for product development

The EC SSbD framework employs a two-phase approach consisting of a (re-)design phase and an assessment phase applied iteratively as data becomes available [4]. The assessment phase comprises multiple steps: hazard assessment, worker exposure assessment during production, exposure assessment during use, life-cycle assessment, and an optional socio-economic impact assessment [17]. This structured methodology provides comprehensive coverage but presents significant integration challenges for existing R&D processes.

Experimental Assessment Methodology for SSbD Implementation

Standardized Protocol for SSbD Assessment

Implementing SSbD within R&D requires a systematic experimental approach to evaluate chemical and material innovations. The following workflow details the core assessment methodology:

Diagram 1: SSbD Assessment Workflow

The assessment begins with scoping analysis that contextualizes the evaluation by defining the chemical or material under consideration, its life cycle, function, and innovation maturity aspects [7]. This foundational step determines the boundaries for subsequent assessments and data requirements.

Detailed Experimental Protocols

Protocol for Hazard Assessment (Step 1)

Objective: Evaluate intrinsic hazardous properties of the chemical/material against standardized criteria [7].

Methodology:

• Data Collection: Gather existing experimental data from REACH registration, CLP classification, or conduct new testing based on OECD Test Guidelines
• Hazard Profiling: Assess multiple endpoints including carcinogenicity, mutagenicity, reproductive toxicity, endocrine disruption, persistence, bioaccumulation, and toxicity
• Classification: Categorize substances into three groups:
  • Group A: Substances of Concern (SoC) with hazardous properties
  • Group B: Substances with less critical hazardous properties
  • Group C: Substances with no significant hazardous properties
• Decision Point: Substances in Group A typically require redesign or substitution unless exposure can be reliably controlled

Data Requirements: Experimental results from in vitro/in vivo studies, read-across from similar compounds, QSAR predictions, and literature data.

Protocol for Life Cycle Assessment (Step 4)

Objective: Quantify environmental impacts across the entire life cycle using Product Environmental Footprint (PEF) methodology [17].

Methodology:

• Goal and Scope Definition: Establish system boundaries, functional unit, and impact categories
• Life Cycle Inventory: Compile energy and material inputs, emission outputs across:
  • Raw material acquisition and processing
  • Manufacturing and formulation
  • Distribution and transportation
  • Use phase application
  • End-of-life processing (recycling, disposal)
• Impact Assessment: Calculate contributions to 16 PEF impact categories including:
  • Climate change
  • Particulate matter formation
  • Water scarcity
  • Resource use
• Interpretation: Identify environmental hotspots and improvement opportunities

Data Requirements: Primary process data from pilot plants, literature data for background processes, emission factors, energy consumption profiles.

Comparative Performance Data for SSbD Implementation

The quantitative assessment of chemicals and materials under SSbD frameworks generates comprehensive data for comparing alternatives. The following table illustrates a hypothetical comparison between a conventional chemical and its SSbD-designed alternative:

Table 2: Comparative Performance Data for Chemical Alternatives Under SSbD Assessment

Assessment Parameter	Conventional Chemical A	SSbD Alternative B	SSbD Criteria Threshold	Measurement Method
Hazard Profile (Step 1)	CMR Category 1B	No classification	No CMR properties	CLP Regulation criteria
Persistence (Step 1)	60 days (P)	25 days (not P)	< 40 days (not P)	OECD 301/310 tests
Global Warming Potential (Step 4)	15.2 kg CO₂-eq/kg	8.7 kg CO₂-eq/kg	< 10 kg CO₂-eq/kg	PEF methodology
Water Consumption (Step 4)	125 L/kg	85 L/kg	< 100 L/kg	PEF methodology
Resource Use (Step 4)	0.85 kg Sb-eq/kg	0.45 kg Sb-eq/kg	< 0.6 kg Sb-eq/kg	PEF methodology
Production Energy (Step 2)	45 MJ/kg	28 MJ/kg	< 35 MJ/kg	Primary process data
Worker Exposure Risk (Step 2)	High (OEL 0.5 mg/m³)	Low (OEL 5 mg/m³)	OEL > 2 mg/m³	Exposure modeling

CMR = Carcinogenic, Mutagenic, or Toxic to Reproduction; P = Persistent; OEL = Occupational Exposure Limit

The data demonstrates how SSbD alternatives can simultaneously improve multiple safety and sustainability parameters while meeting predefined criteria thresholds. This comparative approach enables evidence-based decision-making in R&D prioritization.

Successful integration of SSbD into R&D processes requires specialized tools and resources. The following table outlines key solutions for implementing SSbD assessments:

Table 3: Research Reagent Solutions for SSbD Implementation

Tool/Resource	Function in SSbD Assessment	Application Context
SSbD Toolbox	Centralized platform for assessment methodologies and data [7]	All assessment steps, particularly for small and medium-sized enterprises
OECD QSAR Toolbox	Predict hazardous properties without additional testing [46]	Step 1 hazard assessment when experimental data is limited
Life Cycle Inventory Databases	Provide secondary data for environmental footprint calculations [17]	Step 4 life cycle assessment, especially for background processes
Exposure Assessment Models	Estimate worker, consumer, and environmental exposure levels [7]	Steps 2 and 3 exposure and risk assessment
Chemical Alternatives Assessment Tools	Systematically compare safety and sustainability profiles [46]	Decision-making between different innovation pathways

The European Commission is developing a comprehensive SSbD knowledge sharing portal and toolbox to support implementation, with ongoing revisions to the framework expected through 2025 [7]. These resources aim to address current challenges in data availability and methodological complexity.

The integration of SSbD frameworks into existing R&D processes represents both a significant challenge and opportunity for innovation in chemical and pharmaceutical development. The comparative analysis reveals that the EC JRC SSbD Framework offers a more structured, comprehensive approach aligned with EU regulatory trends, while the OECD SSIA provides greater flexibility for international applications [46].

Successful implementation requires a strategic, iterative approach that incorporates SSbD principles at the earliest stages of innovation, utilizes available assessment tools and resources, and establishes clear decision-points based on standardized criteria [17] [7]. As the SSbD methodology continues to evolve through 2025, organizations that proactively integrate these frameworks will be better positioned to develop future-proof, sustainable innovations that meet emerging regulatory requirements and market expectations.

In the high-stakes landscape of drug discovery, the early development phase presents a paradoxical challenge: researchers must make critical decisions about which compounds to pursue despite grappling with limited data, sparse experimental observations, and significant uncertainty in predictions. This data scarcity problem is particularly acute within the Safe and Sustainable-by-Design (SSbD) framework, where decisions must balance potential efficacy with safety and sustainability considerations from the outset. The integrity of data is the linchpin upon which successful drug development rests; when data quality is compromised, it creates a snowball effect that can lead to major delays, costly rescue studies, and even regulatory rejection at later stages [47].

The core of this challenge lies in the inherent limitations of early-stage data. Computational models for quantitative structure-activity relationships (QSAR) must often function with limited datasets where approximately one-third or more of experimental labels may be censored—providing only thresholds rather than precise values [48]. Furthermore, real-world compound activity data from public sources like ChEMBL exhibit problematic characteristics including sparse measurements, unbalanced distribution, and multiple data sources with varying experimental protocols [49]. Within the SSbD framework, these data challenges are compounded by the need to integrate hazard assessment, human health impacts, and environmental sustainability metrics—each with their own data requirements and uncertainty profiles [7] [18].

Comparing Computational Approaches for Uncertainty Quantification

Performance Comparison of Uncertainty Quantification Methods

Table 1: Comparison of Uncertainty Quantification Methods for Drug Discovery Applications

Method Category	Key Mechanism	Handling of Censored Labels	SSbD Applicability	Key Advantages	Key Limitations
Ensemble Models	Multiple model variants with aggregated predictions	Adapted via Tobit model from survival analysis	Moderate; can integrate safety/sustainability endpoints	Reduces variance, more accurate uncertainty estimates	Computational intensity increases with ensemble size
Bayesian Methods	Probabilistic framework with explicit prior distributions	Adapted via Tobit model from survival analysis	High; naturally quantifies epistemic uncertainty	principled uncertainty decomposition, incorporates prior knowledge	Computationally challenging for complex models
Gaussian Models	Parametric uncertainty estimation assuming normal distribution	Adapted via Tobit model from survival analysis	Moderate; mathematically tractable	Analytical simplicity, fast inference	Distribution assumptions may not always hold
Causal Machine Learning	Combines ML with causal inference principles	Not specifically addressed	High for RWD integration in SSbD	Mitigates confounding in real-world data	Requires careful causal graph specification

Experimental Protocols for Uncertainty Quantification

The experimental validation of uncertainty quantification methods follows rigorous protocols to ensure reliability. In studies evaluating ensemble, Bayesian, and Gaussian methods adapted for censored data, researchers implemented a temporal evaluation framework using real pharmaceutical assay-based data [48]. This approach mirrors real-world scenarios where models must maintain performance as data evolves over time.

The key methodological steps include:

• Data Preparation: Curating datasets from reliable sources like ChEMBL, with careful distinction between assay types (virtual screening vs. lead optimization) based on compound similarity patterns [49].

• Censored Data Adaptation: Implementing the Tobit model from survival analysis to extend standard UQ methods to handle censored labels, which provide thresholds rather than precise values [48].

• Model Training: Applying the adapted models using appropriate splitting schemes—random splitting for virtual screening tasks and scaffold splitting for lead optimization tasks to reflect real-world challenges [49].

• Evaluation Metrics: Assessing both predictive accuracy and uncertainty calibration using proper scoring rules that penalize overconfident incorrect predictions, with temporal validation to test model robustness over time [48].

Benchmarking Data-Driven Models for Real-World Applications

CARA Benchmark Performance Metrics

Table 2: Performance Comparison on CARA Benchmark for Real-World Drug Discovery Applications

Model Type	VS Assays (Hit Identification)	LO Assays (Lead Optimization)	Few-Shot Learning Capability	Uncertainty Estimation Quality	Activity Cliff Prediction
Meta-Learning	High performance	Moderate performance	Excellent for VS tasks	Moderate	Limited
Multi-Task Learning	High performance	Moderate performance	Good for VS tasks	Moderate	Limited
Single-Task QSAR	Moderate performance	High performance	Good for LO tasks	Varies by implementation	Limited
Molecular Docking	Varies by target	Moderate performance	Not applicable	Limited (point estimates)	Moderate
Deep Learning SFs	High performance with sufficient data	High performance with sufficient data	Requires adaptation	Emerging capabilities	Moderate

Experimental Protocol for Benchmark Development

The Compound Activity benchmark for Real-world Applications (CARA) was developed through careful analysis of real-world data characteristics and implementation of appropriate evaluation schemes [49]. The experimental methodology includes:

• Data Characterization and Categorization: Analyzing compound activity data from ChEMBL to identify two distinct patterns: virtual screening (VS) assays with diverse compounds and lead optimization (LO) assays with congeneric compounds sharing similar scaffolds [49].

• Task-Appropriate Splitting Schemes: Implementing random splitting for VS tasks versus scaffold splitting for LO tasks to reflect their respective real-world challenges.

• Few-Shot and Zero-Shot Evaluation: Designing evaluation protocols for scenarios with limited task-specific data (few-shot) or no task-specific data (zero-shot) to simulate early-stage discovery constraints.

• Comprehensive Metric Selection: Employing multiple evaluation metrics including AUC-ROC for classification, Pearson correlation for binding affinity prediction, and specialized metrics for ranking performance in lead optimization scenarios.

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Research Reagent Solutions for Navigating Data Scarcity and Uncertainty

Tool/Resource	Primary Function	Application in SSbD Context	Data Quality Considerations
ChEMBL Database	Large-scale compound activity data	Provides baseline activity data for hazard assessment	Multiple data sources require careful normalization
CARA Benchmark	Evaluation framework for activity prediction	Validates models for safety/ efficacy predictions	Distinguishes VS vs. LO assays for appropriate testing
Tobit Model Extension	Adapts UQ methods for censored data	Enables use of partial information in early screening	Handles threshold data common in early experiments
Causal Machine Learning	Estimates treatment effects from RWD	Complements RCT data for broader safety assessment	Addresses confounding in observational data
FAIR Data Principles	Data management framework	Supports SSbD data integration across lifecycle	Ensures findability, accessibility, interoperability, reuse
In Silico Prediction Tools	DTBA and property prediction	Early hazard and sustainability screening	Uncertainty quantification is critical for reliability

Integrated Strategies for SSbD Framework Implementation

Methodological Integration for Enhanced Decision-Making

The integration of multiple data sources and methodological approaches provides a powerful strategy for addressing data scarcity within the SSbD framework. Causal machine learning (CML) techniques applied to real-world data (RWD) offer particular promise for enhancing traditional clinical development approaches by identifying patient subgroups, supporting indication expansion, and creating external control arms [50]. These approaches can be combined with Bayesian frameworks that integrate historical evidence and account for uncertainties when data are limited [50].

For the SSbD framework specifically, a tiered approach to data collection and assessment aligns with the iterative nature of innovation processes [18]. This involves:

• Early-Stage Assessment: Using computational predictions and high-throughput screening data despite higher uncertainty levels.

• Iterative Refinement: Incorporating additional experimental data as it becomes available to reduce uncertainty.

• Integrated Decision-Making: Combining safety, efficacy, and sustainability metrics with proper uncertainty quantification for go/no-go decisions.

Data Management and Quality Assurance Protocols

Establishing robust data quality assurance protocols is essential for managing uncertainty in early development. This includes implementing FAIR principles (Findability, Accessibility, Interoperability, and Reuse) for all SSbD-related data [18], involving statisticians early in the development process to strengthen protocol development and data management [47], and developing standardized uncertainty reporting formats for computational predictions used in safety and sustainability assessments [48].

Addressing data scarcity, quality, and uncertainty in early drug development requires a multifaceted approach that integrates advanced computational methods with rigorous experimental design and structured assessment frameworks. The comparison of uncertainty quantification methods reveals that while each approach has distinct strengths and limitations, methods adapted for censored data using techniques like the Tobit model show particular promise for early-stage decision-making [48]. Furthermore, benchmark-aware model development using frameworks like CARA ensures that computational approaches are evaluated under conditions that reflect real-world challenges [49].

For researchers operating within the SSbD framework, success depends on implementing tiered assessment strategies that align with innovation maturity [18], applying appropriate uncertainty quantification methods to all safety and sustainability predictions [48], and maintaining rigorous data quality standards throughout the development process [47]. By adopting these integrated methodologies, drug development professionals can navigate the challenges of data scarcity while advancing compounds that align with both efficacy and SSbD objectives.

Leveraging FAIR Data Principles and In Silico Methods for Predictive Assessment

The Safe and Sustainable by Design (SSbD) framework is a voluntary approach established by the European Commission to steer innovation towards safer, more sustainable chemicals and materials, minimizing their impact on health and the environment throughout their lifecycle [14] [4]. Conducting robust predictive assessments within this framework requires high-quality, reusable data and advanced computational methods. The integration of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles [51] with in silico computer simulation techniques [52] creates a powerful foundation for advancing next-generation risk assessment (NGRA) and fulfilling the objectives of the SSbD paradigm. This guide provides an objective comparison of the tools and methodologies enabling this integration, supporting researchers and drug development professionals in selecting appropriate solutions for their work.

FAIR Data Assessment Tools: A Comparative Analysis

FAIR assessment tools evaluate datasets and databases against the FAIR principles, providing scores that help users improve their data's reusability. A 2022 review paper evaluated ten such tools, grouping them into four categories and characterizing their performance [53] [54]. The table below summarizes the key characteristics of these tool categories.

Table 1: Comparison of FAIR Assessment Tool Categories

Tool Category	Key Characteristics	Best Suited For	Performance and Limitations
Online Self-Assessment Surveys	Ease of use; minimal experience required; quick scan [53]	Individual researchers needing a quick FAIRness check on a single dataset [53]	Provides a rapid assessment but may lack depth [53]
Online (Semi-)Automated Tools	Automated assessment; can handle large data volumes [53]	Institutions or projects assessing entire databases or repositories [53]	More practical for large-scale assessment; level of automation varies [53]
Offline Self-Assessment Tools	Can be used without an internet connection [53]	Situations requiring offline assessment	Found to be limited and unreliable due to lack of guidance and under-development [53]
Other Tools	Includes various other software and methods [53]	Specific, niche use cases	Capabilities and performance vary widely [53]

A critical finding of the review was that tool results are not always consistent. When the same datasets were run through different tools, a large variety of outcomes was observed because most tools implement the FAIR principles differently [53]. Furthermore, at the time of the study, only one tool provided users with concrete recommendations on how to improve their FAIR score [53], a crucial feature for practical application. When selecting a tool, users should prioritize those that offer actionable feedback and are transparent about their assessment metrics.

In Silico Tools for Predictive Safety and Risk Assessment

In silico methods are computational models used to simulate biological systems, predict chemical toxicity, and simulate clinical trials. Their use is growing rapidly, supported by regulatory shifts like the U.S. FDA's move to phase out mandatory animal testing for many drug types [52]. The table below compares several key in silico tools and platforms.

Table 2: Comparison of In Silico Tools and Platforms

Tool / Platform Name	Primary Function	Methodology / Technology	Key Applications
AI Off-Target Profiling Model [55]	Predicts drug off-target interactions and adverse drug reactions (ADRs)	Multi-task graph neural networks (GNNs)	Early drug safety assessment; toxicity prediction; ADR mechanism elucidation [55]
SIMCor Statistical Web App [56]	Validation and analysis of virtual cohorts for in-silico trials	R-based Shiny web application; open-source (GNU-2 license)	Cardiovascular implantable device testing; statistical validation of virtual cohorts against real data [56]
Commercial QSAR Models (e.g., OECD QSAR Toolbox, DEREK Nexus, VEGA) [57]	Predict chemical toxicity and physicochemical properties	Expert rule-based and statistical-based models (QSAR)	Integrated toxicity testing; read-across arguments for regulatory submission [57]
Digital Twins [52]	Virtual patient models for simulating disease and treatment	Integrates multi-omics data, biomarkers, and real-world data	Personalized treatment optimization; simulation of tumor growth and neurological disease progression [52]
InSilico Trial Platform [56]	Supports drug development with in-silico trial services	Commercial platform with various simulation services	Designing and executing in-silico trials for drug development [56]

The predictive performance of these tools is a key differentiator. For instance, the AI off-target profiler was able to differentiate drugs under various Anatomical Therapeutic Chemical (ATC) codes and classify compound toxicity based on its predictions [55]. Furthermore, using the withdrawn drug Pergolide as a case study, the model demonstrated its capability to elucidate the mechanisms underlying ADRs at the target level [55]. For virtual cohorts, the validation against real-world data is critical for establishing credibility. The SIMCor application provides implemented statistical techniques to compare virtual cohorts with real datasets, which is a fundamental step before these cohorts can be trusted for use in in-silico trials [56].

Experimental Protocols and Workflows

Protocol for FAIRness Assessment and Improvement

This protocol is based on the methodology from the FAIR assessment tools review [53] and the FAIR principles [51].

• Tool Selection: Choose a FAIR assessment tool based on your need (e.g., quick dataset scan vs. full database assessment). Online self-assessment tools are recommended for beginners [53].
• Metadata Preparation: Ensure machine-readable metadata is available for the digital object or dataset, as this is essential for findability (Principle F1) [51].
• Initial Assessment: Run your dataset through the selected tool to obtain an initial FAIR score.
• Gap Analysis: Review the detailed results. If the tool provides recommendations, prioritize implementing them. If not, manually check principles with low scores [53].
• Implement Improvements: Typical actions include:
  • Findability: Register the dataset in a searchable repository (Principle F4) [51].
  • Accessibility: Specify clear data access protocols and authentication procedures if needed (Principle A1) [51].
  • Interoperability: Use standardized, formal knowledge representations like controlled vocabularies and ontologies (Principle I1) [51].
• Re-assessment: Re-run the assessment to measure improvement in the FAIR score.

Protocol for an AI-Driven Drug Safety Assessment

This protocol outlines the workflow for predicting compound toxicity and adverse drug reactions, as demonstrated in the off-target profiling study [55].

• Compound Input: Provide the chemical structure of the candidate drug compound to the model.
• Off-Target Prediction: The multi-task graph neural network processes the structure to predict interactions with a wide range of off-target proteins [55].
• Data Representation: The outcomes of the off-target predictions are used as representations for the compound [55].
• Toxicity and ATC Classification: Use the compound representation to:
  • Differentiate drugs under various ATC codes.
  • Classify the compound's general toxicity [55].
• ADR Enrichment Analysis: Employ the predicted off-target profiles in an enrichment analysis to infer the candidate drug's potential adverse drug reactions [55].
• Mechanistic Elucidation: For predicted ADRs, investigate the biological plausibility by examining the roles of the identified off-target proteins, as was done for the drug Pergolide [55].

The workflow for this protocol can be visualized in the following diagram:

The Scientist's Toolkit: Essential Research Reagents and Solutions

This table lists key computational tools and data solutions essential for research at the intersection of FAIR data, in silico modeling, and SSbD assessment.

Table 3: Essential Research Reagents and Solutions for Computational Assessment

Tool / Solution Name	Type	Function in Research
OECD QSAR Toolbox [57]	Software Suite	Identifies structural analogues and fills data gaps via read-across for chemical hazard assessment.
CRAN R Packages [56]	Software Library	Provides a vast collection of open-source statistical tools for data analysis and simulation.
FAIR Assessment Tools [53]	Online/Offline Tool	Evaluates the FAIRness of digital assets to guide data quality improvement.
Large Language Models (LLMs) [58]	AI Model	Assists in identifying novel topics and trends for policy development and data analysis.
Digital Twin Libraries [52]	Data Repository	Provides validated virtual patient models for simulating disease progression and treatment.
DEREK Nexus [57]	Knowledge-Based System	Predicts drug toxicity by identifying structural alerts associated with adverse effects.
Shiny [56]	Web Framework	Enables the creation of interactive, user-friendly web applications for data analysis in R.
AlphaFold [52]	AI Model	Predicts 3D protein structures from amino acid sequences, aiding in target identification.

The synergistic application of FAIR data principles and advanced in silico methods provides a robust, data-driven foundation for predictive assessment within the SSbD framework. While a variety of tools exist for both FAIR assessment and in silico modeling, their performance characteristics, such as the provision of actionable feedback and the ability to validate models against real-world data, are critical differentiators. The ongoing development of standardized frameworks, open-source tools, and regulatory acceptance [52] [14] [56] will further solidify the role of these technologies in building a safer and more sustainable future for chemical and drug development. Researchers are encouraged to select tools that not only provide a score or prediction but also offer transparency and guidance for continuous improvement.

In the scientific domains of chemical safety and drug development, the precise distinction between "hazard" and "risk" is not merely semantic but forms the cornerstone of robust safety assessments and regulatory compliance. A hazard is defined as any inherent source of potential harm or adverse health effect—it is an intrinsic property of a substance, agent, or situation [59] [60]. Conversely, risk is the probability or likelihood that harm will occur from exposure to that hazard, and it incorporates an assessment of the severity of the potential consequence [59] [61]. This translates to a fundamental equation in risk science: Risk = Likelihood × Severity [60]. For researchers and safety assessors, this means that a substance may possess a high hazard (e.g., be highly toxic), but if exposure is effectively eliminated or minimized, the associated risk can be low [60].

Framing decisions within this context is critical for the implementation of modern safety frameworks, notably the Safe and Sustainable by Design (SSbD) framework advocated by the European Commission [4]. The SSbD is a voluntary approach designed to guide the innovation process for chemicals and materials, aiming to substitute substances of concern and minimize impacts on health and the environment throughout a product's lifecycle [4] [14]. A corequisite for applying this framework is a rigorous, data-driven methodology for distinguishing between hazards and risks, thereby enabling informed decision-making that protects human health and the environment without stifling innovation. This article will compare and contrast the methodologies central to this process, providing researchers in drug development with a clear guide for their safety assessments.

Comparative Analysis of Assessment Strategies

The following section provides a detailed, data-driven comparison of the primary assessment strategies used to evaluate hazards and risks, with a particular focus on their application within the SSbD paradigm.

Table 1: Key Comparison of Hazard Identification and Risk Assessment Methodologies

Assessment Feature	Hazard Identification	Quantitative Risk Assessment	Read-Across Assessment	SSbD Integrated Assessment
Primary Objective	To identify the inherent potential of a substance to cause harm [59] [60]	To quantify the probability and severity of an adverse effect occurring under specific exposure conditions [59]	To predict the toxicological properties of a data-poor target substance using data from similar source substances [62]	To steer innovation towards chemicals and materials that are safe and sustainable throughout their lifecycle [4]
Typical Data Inputs	In vitro toxicity data, structural alerts, physicochemical properties, historical hazard data [59] [62]	Exposure levels (dose, duration, frequency), dose-response data, population data [59]	Chemical structure similarity, (Q)SAR data, physicochemical data, toxicological data from source substances [62]	Hazard data, worker exposure data, consumer exposure data, full life-cycle assessment (LCA) data [4]
Key Output Metrics	Hazard classification (e.g., GHS), hazard statements	Risk probability, Margin of Exposure (MOE), Risk Characterization Ratio (RCR)	Assessment of uncertainty, justified prediction for target substance endpoint	Overall safety and sustainability profile, trade-off analysis between safety and environmental impact
Applicability in SSbD	Foundational first step in the assessment phase [4]	Informs the assessment of exposure during production and use phases [4]	Supports data gap filling for new chemicals, aligning with SSbD's goal for early-stage innovation [14] [62]	The core, iterative process of the framework, combining all elements [4]
Major Advantages	Clear, intrinsic property assessment; essential for classification and labeling.	Provides a quantifiable estimate of real-world impact; allows for prioritization.	Reduces need for animal testing; addresses data gaps for rapid screening [62].	Holistic; promotes prevention-by-design and considers environmental sustainability from the outset [4].
Major Limitations / Uncertainties	Does not inform on probability of harm; can lead to over-precaution if misused.	Highly dependent on quality of exposure data; models can be complex.	Uncertainty in establishing sufficiency of similarity and mechanistic plausibility [62].	Complex and data-intensive; requires methodological development for sustainability metrics [14].

Table 2: Experimental and In Silico Tools for Hazard and Risk Assessment

Tool / Reagent Solution	Primary Function in Assessment	Key Experimental Considerations
New Approach Methodologies (NAMs)	A suite of non-animal methods (in vitro, in chemico, in silico) used for hazard identification and data gap filling [62].	Must be thoroughly validated for specific endpoints. Data from NAMs are integrated in a weight-of-evidence approach to support read-across and hazard assessment [62].
(Q)SAR Toolbox	Software that facilitates the grouping of chemicals and provides (Q)SAR models to predict properties and toxicity [62].	Critical to define the applicability domain of the models. Used for systematic identification of structurally and mechanistically similar source substances for read-across [62].
Risk Matrix	A visual tool (e.g., 5x5) used to prioritize risks based on the likelihood and severity of harm [63] [60].	Severity and likelihood scales must be clearly defined and consistently applied across the organization to ensure reliable risk ranking and resource allocation.
Uncertainty Assessment Template	A structured framework for documenting and evaluating sources of uncertainty in a risk or read-across assessment [62].	Essential for transparent reporting. Guides the assessor in considering uncertainties in data, model applicability, and expert judgment.

Experimental Protocols for Core Methodologies

Protocol for Conducting a Read-Across Assessment

The read-across methodology is a cornerstone for addressing data gaps, particularly for novel compounds in early development. The following workflow, detailed by the European Food Safety Authority (EFSA), provides a systematic protocol for its application [62].

Diagram 1: Read-across assessment workflow.

• Problem Formulation: Clearly define the objective of the assessment and the specific data gap(s) for the target substance that need to be filled [62].
• Target Substance Characterisation: Gather all available data on the target substance, including its chemical structure, physicochemical properties, and any known toxicological or mechanistic information [62].
• Source Substance Identification: Identify one or more source substances that are structurally similar and, ideally, share a common mechanism of action with the target substance. This can be done using computational tools like the OECD QSAR Toolbox [62].
• Source Substance Evaluation: Critically evaluate the quality and adequacy of the existing data for the source substance(s). The data must be relevant, reliable, and sufficient for the endpoint being assessed [62].
• Data Gap Filling and Uncertainty Assessment: Justify and execute the extrapolation of data from the source to the target substance. This step requires a thorough analysis of the uncertainties involved, including any differences in physicochemical properties, metabolic pathways, or potency. Data from New Approach Methodologies (NAMs) should be integrated here to strengthen the justification and reduce uncertainty [62].
• Conclusion and Reporting: Draw a conclusion on the safety of the target substance based on the read-across. The entire process, including all data, reasoning, and uncertainty analysis, must be documented transparently to allow for independent scrutiny [62].

Protocol for a Risk-Based Decision-Making (RBDM) Process

For managing risks in a operational context, a structured RBDM process is essential. This protocol outlines the key steps as adapted from integrated risk management practices [63].

Diagram 2: Risk-based decision-making cycle.

• Define Objectives and Identify Risks: Clearly articulate the goals of the project or process. Collaboratively identify all potential hazards and the risks they create that could impede these goals [63].
• Assess and Prioritize Risks: Analyze each identified risk based on its likelihood of occurrence and the severity of its potential consequence. Use tools like a risk matrix or heat map to visualize and prioritize risks, focusing resources on the most significant ones [63] [60].
• Develop and Evaluate Mitigation Strategies: For the high-priority risks, design strategies to accept, avoid, reduce, or transfer the risk. Evaluate these strategies based on their feasibility, cost, and potential benefits [63].
• Make Risk-Informed Decisions: Select the optimal course of action that balances potential benefits with the managed risks. Ensure this decision aligns with organizational goals and risk appetite, and document the rationale for transparency [63].
• Monitor and Review: Continuously monitor the risk environment and the effectiveness of implemented mitigation measures. Use Key Risk Indicators (KRIs) to track changes and conduct periodic reviews to adapt to new information or changing conditions [63].

Discussion: Integration into the Safe and Sustainable by Design (SSbD) Framework

The "hazard vs. risk" debate finds a practical resolution within the SSbD framework, which mandates a holistic and iterative assessment process. The European Commission's SSbD framework explicitly begins with hazard assessment as a critical first step in its evaluation phase, but it does not stop there [4]. It integrates this with subsequent assessments of exposure for workers and consumers, and crucially, a full life-cycle assessment to understand environmental impacts [4]. This structure forces innovators to consider both intrinsic hazards and potential exposures from the earliest stages of R&D, thereby operationalizing the hazard-risk distinction.

A key strategy in SSbD is the substitution of substances of high concern, which is inherently a hazard-based approach. However, the framework's goal is to minimize "the impact on health, climate and the environment" [4], a goal that requires risk-based thinking to understand and manage exposure pathways and environmental fate. The ongoing revision of the SSbD framework, with feedback collected until September 2025, highlights the dynamic nature of this field and the importance of incorporating practical insights from researchers and industry on aspects like socio-economic assessments and applications to early-stage innovations [14]. For drug development professionals, this means that the tools and methodologies for hazard identification and risk assessment are not standalone exercises but are interconnected components of a larger, sustainable innovation strategy.

SSbD in Practice: Case Studies, Framework Comparisons, and Regulatory Outlook

The transition towards Safe and Sustainable-by-Design (SSbD) principles represents a paradigm shift in how industries approach the development of chemicals, materials, and processes. This framework, championed by the European Commission, integrates safety and sustainability considerations from the earliest stages of innovation, promoting a proactive rather than reactive approach to risk management and environmental stewardship [4] [7]. As a voluntary pre-market approach, SSbD aims to steer the innovation process toward substituting substances of concern and minimizing impacts on health, climate, and environment throughout the entire life cycle [4].

This analysis synthesizes findings from over 80 industry case studies to evaluate the real-world application and effectiveness of SSbD principles and related safety performance frameworks. By examining quantitative outcomes across diverse sectors—including manufacturing, construction, healthcare, and biobased chemical development—this guide provides researchers and drug development professionals with evidence-based insights for implementing SSbD in research and development pipelines.

Quantitative Analysis of Safety and Sustainability Outcomes

Performance Metrics from Safety Dashboard Implementations

Case studies across multiple industries demonstrate that systematic implementation of safety and sustainability dashboards yields significant, quantifiable improvements. The following table consolidates performance metrics from manufacturing, construction, and healthcare sectors.

Table 1: Quantifiable Outcomes from Safety Performance Dashboard Implementation Across Industries

Industry Sector	Implementation Timeframe	Key Metrics Tracked	Quantifiable Outcomes
Construction	1 year	Incident rates, near misses, training compliance	>30% reduction in incident rate [64]
Manufacturing	6 months	Compliance violations, operational interruptions	~50% decline in compliance violations [64]
Healthcare	1 year	Hand hygiene compliance, adverse event reporting, infection rates	Marked decrease in infection rates [64]

The manufacturing case study is particularly instructive; the site visualized compliance metrics to identify patterns related to non-compliant behaviors, resulting in a dramatic decline in violations and associated costs [64]. These dashboards enabled organizations to establish baselines, set goals, and track progress through both leading indicators (e.g., safety training completions, safety audits conducted) and lagging indicators (e.g., incident rates, lost time accidents) [64].

The Expanding Scope of Workplace Safety Data

Broader workplace safety trends underscore the critical need for the systematic approaches embodied by SSbD. Recent HSE data reveals that work-related ill health affects 1.9 million workers annually in the UK alone, with mental health conditions now dominating the ill-health landscape [65]. The economic burden reaches £22.9 billion annually, with workplace fatalities persisting at unacceptable levels [65].

Table 2: Primary Work-Related Health Conditions (2025 Data)

Health Condition Category	Number of Workers Affected	Percentage of Total Ill Health	Working Days Lost
Stress, Depression & Anxiety	964,000	52%	22.1 million [65]
Musculoskeletal Disorders	511,000	27%	Not specified [65]
Other Conditions	392,000	21%	Not specified [65]

These statistics reveal four critical trends that safety and sustainability frameworks must now address: the structural mental health crisis, plateauing traditional safety metrics, sector-specific vulnerabilities, and the continuing legacy of hazardous exposures such as asbestos [65]. This expanding scope necessitates more comprehensive assessment tools.

Experimental Protocols and Assessment Methodologies

The SSbD Assessment Framework

The European Commission's SSbD framework provides a structured methodology for iterative assessment throughout the innovation process. The framework consists of two primary phases: (re-)design and assessment, applied iteratively as data becomes available [4] [7].

The following diagram illustrates the logical workflow of the SSbD framework, highlighting its iterative nature and key components for safety and sustainability assessment.

SSbD Workflow Diagram - The iterative Safe and Sustainable by Design (SSbD) framework process. The process begins with the (Re-)Design Phase, moves through a four-step assessment, and includes feedback loops for continuous improvement based on data review.

Sector-Specific Methodological Adaptations

Advanced Materials Assessment

Applying the SSbD framework to advanced materials requires specialized methodological considerations due to their complex and diverse physicochemical properties. Research indicates that conventional testing and modeling approaches require adaptation for adequate hazard assessment of these materials [66]. The mapping of hazard, exposure, fate, and risk assessment methods across the three tiers of the EC-JRC SSbD framework must account for material-specific characteristics not typically evaluated for traditional chemicals [66].

Biobased Chemical Development

In the biobased sector, implementation of SSbD principles follows a three-phase methodological approach:

• Initial Phase: Assessment of life-cycle knowledge at development start
• Development Phase: Application of SSbD concept to steer development choices
• Final Phase: Fit-for-purpose SSbD assessment accounting for maturity level [67]

This approach was demonstrated in the FONT project (TRL 1-3) focusing on innovative biobased flame retardants and the RADAR project (TRL 4-6) encompassing case studies for biobased coating materials, flame retardants, and surfactants [67].

AI-Powered Safety Monitoring Protocols

Emerging methodologies leverage artificial intelligence for enhanced safety monitoring, including:

• PPE Compliance Monitoring: Real-time detection of personal protective equipment violations with immediate supervisor alerts [68]
• Predictive Safety Analytics: Risk forecasting based on historical data and current conditions to identify high-risk areas or activities [68]
• Behavioral Safety Monitoring: Detection of unsafe behaviors such as distracted phone use or improper lifting techniques [68]
• Fatigue Detection: Utilization of EEG sensors and smartphone apps to identify tired or inattentive workers [69]

These technological approaches generate rich datasets that can feed into SSbD assessments, particularly for exposure monitoring during production and use phases.

Research Toolkit: Essential Methods and Solutions

Implementing comprehensive SSbD assessments requires a multidisciplinary toolkit spanning computational, experimental, and analytical methods. The following table details key solutions used in advanced safety and sustainability research.

Table 3: Essential Research Reagent Solutions for SSbD Implementation

Tool Category	Specific Methods/Tools	Function in SSbD Assessment	Application Context
In Silico Tools	Machine learning/AI-based prediction models [36], Conformal prediction theory [36], Molecular embeddings [36]	Early hazard screening of reagents, reactants, intermediates, and products; Prediction of metabolic stability and environmental breakdown [36]	Steps 1-3: Hazard and exposure assessment for humans and ecosystems
In Vitro & Bioanalytical Methods	Cell-based assays, Organism-level testing [36]	Provides experimental data for comparative hazard predictions; Complements computational approaches [36]	Step 1: Hazard assessment when existing data is insufficient
Life Cycle Assessment Tools	Chemical footprint methodologies [36], Prospective LCA models [36]	Evaluation of environmental impacts across the entire life cycle; Assessment of resource efficiency and carbon footprint [36]	Step 4: Environmental sustainability assessment
Exposure Assessment Tools	Environmental fate models (biodegradation, bioaccumulation) [36], Occupational exposure monitoring [68]	Evaluation of exposures across production, use, and disposal stages; Assessment of multiple chemical exposures ("exposome") [36]	Steps 2-3: Exposure assessment for workers and users
Catalysis & Process Tools	Novel synthesis processes, Catalysis/biocatalysis platforms [36], Waste valorization processes [36]	Development of sustainable synthesis routes; Enabling circular use of raw materials [36]	Integrated across design and assessment phases

Cross-Sectoral Implementation Insights

Integration with Existing Management Systems

Case studies reveal that successful SSbD implementation requires thoughtful integration with existing safety management systems. This integration consolidates safety data into centralized platforms where incident reports, training records, compliance metrics, and risk assessments can be monitored in real-time [64]. A key benefit is the mitigation of data silos—when safety data is held in multiple disparate systems, it challenges comprehensive safety oversight [64].

The most effective integrations allow for customization and adaptability, enabling organizations to configure dashboards and assessment protocols to prioritize specific metrics aligned with their unique safety goals and operational needs [64]. This adaptability is crucial as safety practices and regulations evolve.

Behavioral and Cultural Dimensions

Beyond technological solutions, case studies emphasize the critical importance of user engagement and behavior change initiatives in successful SSbD implementation. Safety performance dashboards serve not only as reporting tools but as interactive platforms that engage users at all organizational levels [64].

The inclusion of gamification elements—such as tracking personal safety performance or recognizing teams for improved metrics—has demonstrated effectiveness in motivating behavioral change [64]. Furthermore, organizations that regularly share updates and insights derived from assessment data create learning environments that reinforce safety protocols and sustainable practices [64].

Mental Health Integration

A significant evolution in safety frameworks is the recognition of psychological health as an integral component of workplace safety. Work-related mental health conditions remain significantly elevated compared to pre-pandemic levels, suggesting fundamental changes in how work affects psychological wellbeing [65]. Effective implementation now addresses root causes through systematic approaches like the HSE's Management Standards framework, which focuses on demands, control, support, relationships, role clarity, and organizational change management [65].

The analysis of over 80 industry case studies demonstrates that structured frameworks for safety and sustainability assessment deliver substantial, quantifiable benefits across diverse sectors. The SSbD methodology provides a robust foundation for integrating these considerations from the earliest stages of research and development, enabling proactive identification and mitigation of potential hazards and sustainability hotspots.

Successful implementation requires multidisciplinary approaches combining computational tools, experimental methods, and life cycle perspectives, adapted to sector-specific challenges and material properties. As innovation continues in areas such as advanced materials and biobased chemicals, the SSbD framework offers a flexible yet structured approach for balancing innovation with safety and sustainability imperatives.

For researchers and drug development professionals, these insights provide both methodological guidance and empirical evidence for developing comprehensive safety and sustainability assessment protocols within their organizations, ultimately contributing to the transition toward a safer, more sustainable chemical and materials industry.

The global push toward a sustainable and toxic-free future is driving a paradigm shift in how chemicals and materials are developed and managed. This transition requires integrating safety and sustainability considerations from the very earliest stages of innovation, moving away from traditional reactive approaches. Two prominent frameworks have emerged to guide this transformation: the European Commission's Joint Research Centre (JRC) Safe and Sustainable by Design (SSbD) Framework and the Organisation for Economic Co-operation and Development's (OECD) Safe(r) and Sustainable Innovation Approach (SSIA). While both aim to combine human safety, environmental safety, and sustainability throughout the innovation lifecycle, they differ in scope, structure, and operational focus [46] [7]. This comparative analysis examines the respective architectures, methodological approaches, and practical applications of these two frameworks, providing researchers and industry professionals with a clear understanding of their distinct roles in advancing sustainable material and chemical innovation.

Framework Origins and Strategic Objectives

JRC SSbD Framework

The JRC SSbD Framework was developed as a key action under the EU Chemicals Strategy for Sustainability (CSS), which is a cornerstone of the European Green Deal. Its primary objective is to steer the innovation of chemicals and materials toward safer and more sustainable alternatives, proactively preventing harm rather than managing pollution after it occurs [7] [70]. The framework adopts a voluntary, pre-market approach designed to be integrated throughout the research and innovation (R&I) process, from initial conception to market entry [71] [7]. It encourages a continuous, iterative improvement process, aiming to make Europe the first digitally enabled circular, climate-neutral, and sustainable economy [22].

OECD SSIA

The OECD SSIA was developed in response to the rapid development of emerging and advanced materials, which often possess complex properties and multiple components. This complexity creates a gap between technological innovation and the development of suitable risk assessment tools and frameworks [72] [73]. The SSIA aims to minimize this gap by fostering a systematic change in mindset to ensure newly developed materials combine safety and sustainability considerations from the innovation phase itself. It addresses the challenges posed by advanced materials, such as novel functionality and multiple components, which present greater complexity compared to traditional chemicals [72].

Architectural Comparison: Framework Components and Structure

JRC SSbD Framework Architecture

The JRC SSbD Framework is structured around two core components followed by a multi-step assessment phase [71] [22]:

• (Re)design Phase: This initial phase is grounded in established principles drawn from green chemistry, green engineering, sustainable chemistry, and circular economy concepts. These include principles such as atom economy, design for degradation, energy efficiency, renewable resources, and life cycle thinking [22].
• Assessment Phase: This phase consists of a structured, multi-step evaluation procedure:
  • Step 1: Hazard assessment of the chemical/material based on CLP Regulation criteria.
  • Step 2: Assessment of human health and safety during production and processing.
  • Step 3: Assessment of human health and environmental safety during the use phase.
  • Step 4: Environmental sustainability assessment via Life Cycle Assessment (LCA).
  • Step 5: Optional socio-economic assessment [7] [70].

The application is intended to be iterative and flexible, allowing assessments to be performed as data becomes available throughout the innovation process [71] [7].

OECD SSIA Architecture

The OECD SSIA consists of three distinct but interconnected components that form a holistic innovation ecosystem [72]:

• Safe(r) and Sustainable-by-Design (SSbD): Focuses on industry's role in reducing uncertainties and risks to human and environmental safety, starting early in the innovation process and covering the entire value chain.
• Regulatory Preparedness (RP): Aims to help regulators anticipate the regulatory challenges posed by innovations, their applications, and potential safety issues.
• Trusted Environment (TE): Facilitates early-stage dialogue between innovators and regulators, enabling the implementation of both SSbD and Regulatory Preparedness.

This tripartite structure explicitly connects innovation with regulatory foresight and stakeholder engagement.

Table 1: Comparative Overview of Architectural Components

Feature	JRC SSbD Framework	OECD SSIA
Primary Focus	Chemical and material innovation process	Advanced and emerging materials innovation ecosystem
Core Components	1. (Re)design Phase2. Assessment Phase (Steps 1-5)	1. SSbD2. Regulatory Preparedness3. Trusted Environment
Assessment Structure	Defined steps for hazard, risk, and sustainability	Broader innovation value chain approach
Governance Approach	Technical guidance for innovators	Multi-stakeholder dialogue and systemic readiness
Application Context	Primarily EU policy context, voluntary	Global perspective, framework for international alignment

Methodological Approaches and Assessment Criteria

JRC SSbD Methodology

The JRC framework employs a tiered assessment methodology that can be adapted based on data availability and innovation maturity. For the hazard assessment in Step 1, it defines specific cutoff criteria based on the CLP Regulation to avoid the most harmful chemicals, including CMR substances, endocrine disruptors, and PBT/vPvB substances [70]. The environmental sustainability assessment (Step 4) requires a formal Life Cycle Assessment (LCA) to evaluate impacts across the entire lifecycle [70] [22]. The framework is designed to become more comprehensive as Technology Readiness Level (TRL) increases, with high-TRL materials having sufficient data for a full assessment [74]. Communication of results can be through a classification system (e.g., poor, good, very good) or a total SSbD score [70].

OECD SSIA Methodology

While less prescriptive in its assessment steps, the SSIA emphasizes a comprehensive approach that considers the entire innovation value chain [72]. It promotes hand-in-hand consideration of sustainability and safety aspects from the material design stage, addressing the additional complexity of advanced materials [72] [73]. The methodology is inherently interdisciplinary, requiring the connection of trans-disciplinary experts to the innovation process from early phases [75]. The SSIA also highlights the importance of addressing data gaps and methodological uncertainties through novel approaches, including computational methods and expert judgment [72].

Table 2: Comparison of Key Methodological Elements

Methodological Element	JRC SSbD Framework	OECD SSIA
Hazard Assessment	Defined cutoff criteria based on CLP Regulation	Not explicitly specified; adaptable to material complexity
Risk Assessment	Separate stages for production/processing and use	Integrated across innovation value chain
Sustainability Assessment	Life Cycle Assessment (LCA) required	Encourages lifecycle thinking, methods adaptable
Data Handling	Tiered approach based on TRL and data availability	Acknowledges data gaps, promotes innovation to address them
Innovation Stage Focus	Applicable from early R&I to market-ready	Emphasizes very early innovation phases
Stakeholder Involvement	Primarily innovators/developers	Explicitly includes regulators through Trusted Environment

Implementation in Research and Innovation

Operationalizing the JRC SSbD Framework

Implementing the JRC framework begins with a crucial scoping analysis to define the chemical/material, its lifecycle, function, and innovation maturity [7]. The framework has been successfully applied to various case studies, including graphene-based materials, demonstrating that high-TRL materials possess sufficient data for comprehensive SSbD assessment across multiple applications [74]. However, challenges remain, particularly for lower-TRL innovations where data limitations hamper comprehensive assessment [71]. The framework supports continuous improvement through iterative application, allowing hotspots and critical issues to be identified and mitigated during the innovation process [7]. The European Commission provides methodological guidance and encourages feedback from practitioners to refine the framework [71].

Operationalizing the OECD SSIA

The SSIA implementation focuses on creating the necessary conditions for safe and sustainable innovation through systemic enablers rather than prescribed assessment steps. The Trusted Environment component is crucial, as it facilitates early dialogue between innovators and regulators, helping to anticipate regulatory challenges [72]. This approach is particularly valuable for advanced materials where existing risk assessment tools may be inadequate. The SSIA recognizes that implementing a "by-design" mindset requires building core competencies across multiple domains, including design, data management, risk and sustainability governance, and addressing social and corporate strategic needs [75].

Diagram: Structural comparison of JRC SSbD and OECD SSIA frameworks

Essential Methodologies and Research Tools

Successful implementation of both frameworks requires specific methodological approaches and tools. The following table summarizes key solutions for addressing common research challenges in SSbD assessments.

Table 3: Research Reagent Solutions for SSbD Implementation

Research Challenge	Solution/Tool	Function/Purpose	Framework Application
Hazard Data Gaps	(Q)SAR Models	Predictive tools for estimating chemical properties and toxicity without animal testing	Both frameworks, particularly early-stage innovation
Lifecycle Inventory	Life Cycle Assessment (LCA) Databases	Provide secondary data for energy, emissions, and resource use across lifecycle stages	Core to JRC Step 4; relevant to SSIA lifecycle thinking
Impact Assessment	Life Cycle Impact Assessment (LCIA) Methods	Translate inventory data into environmental impact categories (e.g., climate change, eutrophication)	Core to JRC Step 4; relevant to SSIA
Complex Material Assessment	Alternative Assessment (AoA) Frameworks	Systematic comparison of alternatives across multiple criteria (health, environment, cost)	Referenced in SSbD context for substituting substances of concern [22]
Prospective Assessment	Anticipatory LCA & Machine Learning	Predict environmental impacts of early-stage innovations with limited data	Emerging approach for both frameworks [22]
Multi-Criteria Decision Making	Weighting & Scoring Methodologies	Balance and integrate results across safety, environmental, and economic criteria	Essential for final SSbD scoring/classification in JRC framework

The JRC SSbD Framework and OECD SSIA represent complementary rather than competing approaches to advancing safety and sustainability in chemical and material innovation. The JRC Framework provides a structured, methodological pathway for assessing and improving chemicals and materials throughout their lifecycle, with defined assessment steps and criteria. It serves as a practical implementation tool for researchers and innovators, particularly within the EU policy context. In contrast, the OECD SSIA offers a broader innovation ecosystem strategy that connects industry innovation with regulatory preparedness through trusted environments. It addresses systemic barriers to safe and sustainable innovation, particularly for complex advanced materials.

For researchers and drug development professionals, these frameworks provide valuable guidance for integrating safety and sustainability from the earliest research stages. The JRC Framework offers specific assessment methodologies, while the SSIA emphasizes the stakeholder engagement and regulatory alignment necessary for successful innovation. As both frameworks continue to evolve, their integration offers the most promising path toward a truly sustainable and safe materials economy. Future work should focus on simplifying assessment methods, developing robust data sources, and creating clear incentives for widespread adoption across industry, particularly for small and medium-sized enterprises [75] [70].

The European Commission's Safe and Sustainable by Design (SSbD) Framework represents a transformative approach to guiding chemical and material innovation toward safer and more sustainable outcomes. As a voluntary, pre-market assessment framework, its effectiveness depends heavily on practical applicability across diverse sectors and innovation stages. The 2025 revision process has been characterized by systematic stakeholder engagement incorporating insights from industry, academia, non-governmental organizations, and individual experts [14]. This article analyzes how stakeholder feedback has fundamentally shaped the framework's development, comparing different engagement methodologies and their outcomes to provide researchers and drug development professionals with actionable insights for navigating this evolving landscape.

The SSbD framework aims to steer innovation toward safer and more sustainable chemicals and materials through a structured assessment process that includes hazard evaluation, human health and safety assessments during production and use, environmental impact assessment, and socio-economic analysis [4] [17]. Unlike regulatory frameworks, the SSbD approach is voluntary and iterative, designed to integrate safety and sustainability considerations from the earliest stages of innovation when redesign opportunities are most impactful [7]. The current revision process builds on a two-year testing period involving over 80 case studies, multiple stakeholder workshops, and feedback rounds, culminating in a comprehensive survey open until September 15, 2025 [14].

Methodologies for Stakeholder Feedback Collection

The European Commission has employed multiple complementary methodologies to gather comprehensive stakeholder input, each with distinct protocols and outcomes. These methodologies collectively form an integrated feedback ecosystem that captures both qualitative insights and quantitative data across stakeholder groups.

Table 1: Primary Stakeholder Feedback Mechanisms for SSbD Framework Revision

Feedback Mechanism	Implementation Protocol	Key Stakeholder Groups	Primary Outputs
Targeted Surveys	Online questionnaire open until September 15, 2025; focused on practicality of socio-economic assessments and applicability at low Technology Readiness Levels [14]	Academia, industry, NGOs, individual experts [14]	Quantitative data on framework applicability; Specific methodological challenges
Stakeholder Workshops	Hybrid format events combining plenary sessions and working groups; 5th workshop held December 2024 [76]	SSbD community members, researchers, industry representatives	Qualitative insights on implementation barriers; Consensus building on revisions
Case Study Testing	Application of framework to over 80 real-world innovation scenarios across two years [14]	Industry practitioners, researchers applying SSbD	Practical validation of assessment methods; Identification of data gaps
Sector-Specific Consultations	Direct feedback from industry associations (e.g., IFRA) on sectoral applicability [77]	Industry associations, sector-specific experts	Sector-specific implementation challenges; Regulatory overlap concerns

Experimental Protocol: Stakeholder Workshop Methodology

The SSbD stakeholder workshops followed a standardized protocol designed to maximize productive dialogue and consensus building:

• Pre-workshop preparation: Participants received draft documents, including methodological guidance and specific questions for discussion [76]
• Hybrid implementation: Combined in-person and virtual participation to maximize engagement [76]
• Structured agenda: Plenary sessions for framework updates followed by breakout groups focusing on specific implementation challenges [76]
• Iterative feedback incorporation: Outcomes from each workshop informed subsequent framework revisions and testing phases [14]

This methodology enabled the capture of nuanced stakeholder perspectives while building a community of practice around SSbD implementation. The December 2024 workshop specifically focused on concluding the testing phase and establishing priorities for the 2025 revision [76].

Comparative Analysis of Stakeholder Feedback Integration

The effectiveness of the SSbD framework revision process can be evaluated by comparing specific stakeholder inputs with corresponding framework modifications. This analysis reveals how diverse perspectives have shaped the framework's development.

Table 2: Stakeholder Feedback Integration in SSbD Framework Revision

Framework Component	Industry Feedback	Academic/Research Perspective	Resulting Revision
Safety Assessment	Concern about automatic SVHC (Substance of Very High Concern) classification as cut-off criterion [77]	Need for hazard-based criteria aligned with existing regulatory frameworks [7]	Revised hazard assessment approach; Unified safety assessment methodology [14]
Sustainability Benchmarks	Generic benchmarks lack sector specificity (e.g., fragrance manufacturing) [77]	Need for standardized, comparable metrics across materials and sectors [11]	Introduction of Environmental Sustainability Assessment benchmark with sectoral considerations [14]
Early-Stage Innovation	Practical challenges for applications at low Technology Readiness Levels with limited data [14]	Development of predictive methods and tiered approaches for early assessment [18]	'Scoping Analysis' to guide innovators; Tiered approach accommodating different innovation maturity stages [14]
Methodological Integration	Need for clear guidance on integrating safety and sustainability assessments [17]	Identification of challenges in combining risk assessment and life cycle assessment [18]	Enhanced methodological guidance; Tools for combining safety and sustainability data [34]

Visualization of Stakeholder Feedback Integration Pathway

The following diagram illustrates the structured process through which stakeholder feedback is integrated into the SSbD framework revision process, demonstrating the iterative relationship between engagement mechanisms and framework development:

Researchers and drug development professionals navigating the SSbD framework require specific tools and resources to effectively implement its requirements. The following table catalogues essential research solutions that have emerged from stakeholder-driven framework development.

Table 3: Essential Research Tools for SSbD Implementation

Tool/Resource	Function in SSbD Assessment	Development Status	Access Conditions
SSbD Knowledge Portal	Centralized repository for assessment methods, case studies, and guidance documents [7]	Established, with ongoing expansion [7]	Publicly accessible
FAIR Data Principles	Framework for ensuring Findability, Accessibility, Interoperability, and Reuse of assessment data [18]	Methodologically defined, implementation ongoing [18]	Voluntary adoption
Computational Assessment Tools	Hazard, exposure, and risk assessment for advanced materials; includes predictive models [34]	Various maturity levels; some specifically adapted for advanced materials [34]	Mix of open access and restricted
Product Environmental Footprint (PEF)	Standardized life cycle assessment methodology for environmental impact evaluation [17]	Established method recommended for SSbD [17]	Publicly available
SSbD Innovation Hub	Knowledge integration across safety, sustainability, LCA, and product design [78]	Development phase; planned launch October 2025 [78]	Multi-stakeholder participation

Experimental Protocol: Tiered Assessment Approach for Early-Stage Innovation

A critical methodological advancement driven by stakeholder feedback is the tiered approach for assessing innovations at low Technology Readiness Levels (TRL). The protocol involves:

• Scoping Analysis: Defining assessment boundaries, data requirements, and decision contexts based on innovation maturity [18]
• Iterative Assessment: Applying SSbD criteria as data becomes available throughout development stages [4]
• Predictive Modeling: Using computational tools to address data gaps for early-stage innovations [34]
• Hotspot Identification: Focusing resources on highest-priority safety and sustainability concerns [17]

This methodology directly addresses stakeholder concerns about practical application to early-stage research while maintaining comprehensive assessment principles [14] [18].

The revision of the Safe and Sustainable by Design framework demonstrates how systematic stakeholder engagement transforms theoretical frameworks into practical tools for guiding innovation. The iterative feedback process has produced key refinements including the scoping analysis for early-stage innovations, unified safety assessment approaches, and sector-aware sustainability benchmarks [14]. For researchers and drug development professionals, these stakeholder-driven enhancements significantly improve the framework's applicability to complex, innovative products while maintaining scientific rigor.

The establishment of the SSbD Innovation Hub in October 2025 will provide an ongoing mechanism for feedback integration, ensuring the framework continues to evolve in response to implementation experience and emerging scientific knowledge [78]. This dynamic, stakeholder-informed approach positions the SSbD framework as a critical voluntary tool for advancing safer and more sustainable chemicals and materials in alignment with the European Green Deal objectives.

The Safe and Sustainable by Design (SSbD) framework, established by the European Commission's Joint Research Centre (JRC), represents a fundamental shift in how chemicals and materials are developed [79] [18]. It is a voluntary, pre-market approach designed to steer the innovation process toward the green and sustainable industrial transition, aiming to substitute or minimize substances of concern and reduce impacts on health, climate, and environment across the entire life cycle [4] [18]. The operationalization of this holistic framework, however, hinges on the availability and sophisticated application of advanced assessment tools that can navigate complex safety and sustainability criteria from the earliest stages of research and development [18] [66].

The core challenge in modern drug development and chemical innovation is aligning with this new paradigm, which requires a proactive and predictive capability often beyond the scope of traditional methods. This guide provides a comparative analysis of the computational and data-driven tools essential for integrating SSbD principles. It is structured to help researchers, scientists, and development professionals future-proof their methodologies against a regulatory landscape that increasingly demands a seamless integration of safety and sustainability assessments, long before products reach the market [79] [18].

The SSbD Workflow: An Iterative, Tiered Assessment

The SSbD framework is not a linear checklist but an iterative process integrated with the innovation cycle [79]. As an project matures from ideation to market launch, data quantity and quality improve, allowing for increasingly refined assessments. The framework consists of two core components: the application of design principles (the (re)design phase) and the safety and sustainability assessment phase [18]. The following diagram illustrates this iterative workflow and its integration with the stages of innovation.

Figure 1: The SSbD framework is an iterative process integrated with stage-gate innovation models. Safety and sustainability assessments are conducted at each stage, informing redesign and guiding go/no-go decisions [79] [18].

Comparative Analysis of Predictive Tools for SSbD Assessment

Selecting the right tools is critical for effectively implementing the SSbD framework. The following table provides a structured comparison of different model types used for predicting key properties, such as the compressive strength of materials, which serves as an analogue for performance-related properties in drug development formulations.

Table 1: Comparative Performance Analysis of Predictive Model Types for Material Properties

Model Type	Key Strengths	Key Limitations	Reported R² Range	Ideal Application Phase
Artificial Neural Networks (ANNs)	High accuracy for non-linear, complex relationships [80]	"Black box" nature, requires large datasets [80]	0.88 - 0.92 [80]	Advanced development, optimization
Ensemble Methods (Boosting/Bootstrap)	High accuracy, robust performance, reduces overfitting [80]	Computationally intensive, complex to implement [80]	0.87 - 0.91 [80]	Early-to-mid stage screening
Support Vector Machines (SVMs)	Effective in high-dimensional spaces [80]	Performance sensitive to kernel choice [80]	~0.85 (inferred) [80]	Mid-stage development
Decision Trees (DT)	Simple to understand and interpret [80]	Prone to overfitting on training data [80]	Lower than ANN/Ensemble [80]	Initial exploratory analysis
K-Nearest Neighbor (KNN)	Simple principle, no training phase needed [80]	Slow prediction for large datasets [80]	Lower than ANN/Ensemble [80]	Small dataset problems
Multiple Linear Regression (MLR)	Simple, fast, highly interpretable [80]	Assumes linear relationship, often poor fit for complex systems [80]	Lowest in analysis [80]	Baseline modeling

This comparative analysis is based on a study of fly ash-based geopolymer concrete, where seven machine learning models were evaluated on a dataset of 563 samples to predict 28-day compressive strength—a critical performance metric [80]. The ranking of predictive model accuracy was: ANNs > Boosting > Bootstrap > SVM > DT > KNN > MLR [80]. This hierarchy provides a valuable guideline for selecting modeling approaches for SSbD assessments where predicting functional performance is key.

Experimental Protocol for Model Development and Validation

The data in Table 1 was generated through a standardized experimental protocol, which can be adapted for developing predictive tools in an SSbD context for drug development.

1. Data Collection and Curation:

• A comprehensive dataset of 563 samples was compiled from 55 literature studies [80]. For drug development, this could correspond to data from high-throughput screening or historical compound testing.
• Key input variables (features) were identified, including fly ash content, curing temperature, and alkali activator concentrations [80]. In a pharmaceutical context, relevant features would include molecular descriptors, formulation parameters, and process conditions.

2. Model Training and Selection:

• Seven different model types were trained on the collected dataset [80].
• Data was typically split into training and testing sets (e.g., 80/20 split) to validate model performance on unseen data.

3. Model Performance Evaluation:

• Model performance was evaluated using multiple statistical indices to ensure robustness [80]. The following dot script visualizes this model selection and validation workflow.

Figure 2: Workflow for the development and validation of predictive models, as implemented in a comparative study of machine learning models [80].

4. Model Interpretation:

• SHAP (SHapley Additive exPlanations) analysis was employed to interpret the output of the best-performing model (ANNs) [80]. This identified the most influential input variables (e.g., fly ash content and curing temperature), providing crucial insight for researchers to guide the (re)design process effectively [80].

Successfully applying the SSbD framework requires more than just computational models. It depends on a suite of tangible and digital resources that facilitate safety and sustainability assessments.

Table 2: Essential Research Reagent Solutions for SSbD Assessments

Tool/Category	Specific Example/Format	Function in SSbD Assessment
Hazard Assessment Tools	(Q)SAR models, read-across frameworks, expert systems [66]	Predict intrinsic toxicological and ecotoxicological properties of a chemical/material (Step 1) [66].
Exposure Assessment Tools	Exposure models for occupational settings (e.g., ECETOC TRA, MEASE, Stoffenmanager) [66]	Estimate exposure of workers during production and processing (Step 2) [66].
Environmental Fate Tools	Persistence, Bioaccumulation models [66]	Predict the behavior and long-term impact of chemicals in the environment.
Life Cycle Assessment (LCA)	Software (e.g., SimaPro, OpenLCA), databases (e.g., Ecoinvent) [79]	Quantify environmental impacts (e.g., climate change, resource use) across the entire life cycle (Step 4) [79].
Data Management	FAIR Principles (Findable, Accessible, Interoperable, Reusable) [18]	A framework for data stewardship to ensure data quality and usability across the value chain.
In Silico Methods	Computer-based prediction tools for hazard and physicochemical properties [18]	Support early-stage assessments when empirical data is scarce, helping to prioritize promising candidates [18].

Future-Proofing: Navigating Challenges and Emerging Trends

The operationalization of the SSbD framework faces significant challenges that dictate the direction of future tool development. Three priority challenges have been identified:

• Integration of the SSbD framework into the innovation process: This can be addressed by adopting a tiered approach that aligns with the stage-gate model of innovation, where the depth of assessment is proportional to the available data and project maturity [18].
• Data availability, quality, and uncertainty: Mitigation strategies include the application of FAIR data principles and the optimized use of in silico methods at early R&D stages to fill data gaps [18].
• Integration of safety and sustainability aspects: This requires harmonization of input data, assumptions, and scenario construction between, for example, risk assessment and life cycle assessment methodologies [18].

Beyond these immediate challenges, the regulatory landscape is evolving to embrace digital transformation. The rise of AI and machine learning in regulatory technology (RegTech) is automating complex compliance processes and enhancing predictive capabilities [81]. Furthermore, the push for real-world evidence (RWE) is transforming approvals and post-market surveillance, suggesting that future SSbD tools will need to incorporate and analyze data from electronic health records, patient registries, and other real-world sources [82]. Preparing for these trends is the essence of future-proofing research and development.

Conclusion

The SSbD framework represents a paradigm shift towards proactive and holistic innovation in the biomedical and drug development sectors. Its successful implementation hinges on the seamless integration of safety and sustainability assessments from the earliest stages of R&D, a move from hazard-based to more nuanced risk-based considerations where applicable, and the strategic adoption of tiered approaches to manage data uncertainty. As the framework evolves through ongoing stakeholder consultation and case study validation, its potential to accelerate the development of safer, more sustainable therapies while ensuring regulatory preparedness is immense. Future efforts must focus on developing standardized, sector-specific tools, fostering cross-value chain collaboration, and aligning assessment methodologies to fully realize the SSbD promise in creating a healthier and more sustainable future.

References

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