Hierarchical Mechanistic Modeling of Complex Toxicity Endpoints from Public Concentration-Response Data.

ABSTRACT: High-throughput screening (HTS) programs have generated abundant data on numerous chemicals, supporting the discovery of toxicity mechanisms and advancing understanding of adverse outcome pathways (AOPs) in chemical toxicity. However, organizing and interpreting these data for predictive modeling remain challenging due to inconsistent repository formats, varied program objectives, heterogeneous assay targets, and differences in experimental protocols, including concentration ranges. To address these limitations, we developed a hierarchical mechanistic modeling framework that systematically structures and interprets concentration response HTS data. The model integrated curated data sets by mapping metadata from 455 PubChem assays to 216 protein targets and 103 biological pathways in WikiPat

SUBMITTER: Chung E 

PROVIDER: S-EPMC12854766 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature