A framework for chemical hazard assessments under 'Safe and Sustainable by Design' using multiple in silico tools.

The hazard identification of chemicals is a key step of the 'Safe and Sustainable by Design' (SSbD) framework introduced by the European Commission, aiming to eliminate hazardous substances early in innovation. In this context, in silico methods such as (Quantitative) Structure-Activity Relationship ((Q)SAR) models offer rapid, cost-effective, and animal-free alternatives for early-stage hazard screening. The Partnership for the Assessment of Risks from Chemicals (PARC) is developing a toolbox to facilitate SSbD assessments containing numerous (Q)SAR models. Challenges, however, exist in using and combining multiple in silico tools. Here, we developed a workflow to assess chemical hazards using multiple in silico tools within the PARC toolbox. The workflow consists of three phases: 1) the preparation stage, 2) running the models, and 3) the evaluation stage. To demonstrate the approach, we applied it to a case study comparing Bisphenol A, Isosorbide, and Bisphenol AP. Tools from the PARC toolbox were screened for relevance, transparency, and open access availability. Only models aligned with SSbD required endpoints and adequately documented via (Q)SAR Model Reporting Formats were retained. The properties assessed in this study cover carcinogenicity, germ cell mutagenicity, reproductive toxicity, endocrine disruption, persistence, bioaccumulation, and aquatic toxicity. Predictions were filtered using applicability domain criteria and reliability scores. Next, three strategies were applied for integrating different model outputs. Model agreement varied across endpoints and integration methods. This emphasizes the possibility of different SSbD assessment outcomes and thus the need for transparent documentation of the chosen strategy and explicit handling of uncertainty. Our study demonstrates how multiple models can systematically and transparently be integrated via the developed workflow. Key areas for improvement are to refine integration strategies, harmonize the definition and communication of applicability domains across tools, expand in silico coverage for currently underrepresented endpoints, and to develop approaches to consider data gaps in SSbD assessments.