Semi-automated, evidence-based workflow for selection of reference chemicals for the validation of NAMs: a case study with the adipogenesis assay.

The validation of in vitro New Approach Methodologies (NAMs) requires the use of well-characterized reference chemicals to assess assay performance, reproducibility, and relevance. However, selecting such chemicals is often labor-intensive and lacks standardization. We developed a semi-automated, evidence-based workflow that integrates systematic literature review, AI-assisted data extraction, and quantitative evidence scoring to improve the efficiency and transparency of chemical selection. We conducted a structured EMBASE search, followed by AI-assisted abstract screening and data extraction, in a case study to select reference chemicals for the validation of an in vitro adipogenesis assay. Chemicals were evaluated for in vitro, in vivo, and human evidence of adipogenic or obesogenic effects. From 11,648 screened publications, 236 studies met the inclusion criteria, resulting in the identification of 243 candidate reference chemicals, of which 50 were prioritized based on scoring. The final selection encompassed a set of 22 chemicals with different levels of potency and diverse structures. This workflow illustrates how AI-assisted evidence synthesis can accelerate and standardize the selection of reference chemicals while maintaining expert oversight and ensuring regulatory relevance. It provides a reproducible and adaptable framework for future NAMs validation studies across diverse toxicological endpoints.