In 2020, a collaboration between Cyprotex and Unilever produced an award-winning paper that detailed the development of a high-throughput cell stress panel which could then be combined with other safety assessment methodologies to identify potential chemical liabilities. Following on from that collaboration, Cyprotex have contributed to a new research article ‘Are Non-animal Systemic Safety Assessments Protective? A Toolbox and Workflow’ which addresses an important question in toxicological risk assessment; whether non-animal new approach methodologies (NAMs) can be used to make safety decisions that are protective of human health, without being overly conservative in their outlook.
The research had several objectives:
- To present a foundation of NAMs (in vitro and computational) to be used in conjunction with a workflow to provide a bioactivity exposure ratio (BER) estimate for use in systemic safety assessments.
- To undertake a proof-of-concept study to evaluate the performance of the toolbox and workflow using a set of benchmark compounds with known safety risk classifications.
- Finally, to establish a preliminary decision-making model using the pilot study in order to conduct a full evaluation of the proposed strategy.
Following on from previous next-generation risk assessment (NGRA) case studies, the proposed workflow was divided into 2 compartments. The first for estimating exposure of a chemical (Cmax) and the second for estimating the points of departure (POD) based on in vitro bioactivity data gathered from high-throughput transcriptomics, a cell stress panel, and in vitro pharmacological profiling. The results from these two modules were then combined to provide an estimate of the bioactivity exposure ratio (BER), a single metric which represents a margin of safety.
The test of the toolbox was performed using 24 low- and high-risk exposure scenarios using 10 benchmark compounds, and the pilot results demonstrated that the NAM toolbox and workflow can distinguish between low- and high-risk exposure scenarios. Using the BER obtained from the toolbox, up to 69% (9/13) of the low-risk scenarios could be positively identified, whilst also being protective against all the high-risk scenarios (5/5). The results demonstrated the potential to make robust safety decisions in the absence of animal data. In addition, some important limitations of the NAMs used were identified, which can be addressed during further development of the core toolbox.
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