Automated Quantitative Dose-Response Modeling and Point of Departure Determination for Large Toxicogenomic and High-Throughput Screening Data Sets

Abstract

Regulatory and homeland security agencies undertake safety and risk assessments to assess the potential hazards of radiation, chemical, biological, and pharmaceutical agents. By law, these assessments must be science-based to ensure public safety and environmental quality. These agencies use dose-response modeling and benchmark dose methods to identify points of departure across single end points elicited by the agent. Regulatory agencies have also begun to examine toxicogenomic data to identify novel biomarkers of exposure and assess potential toxicity. The ToxResponse Modeler streamlines analyses and point of departure (POD) calculations across hundreds of responses (e.g., differential gene expression, changes in metabolite levels) through an automated process capable of large-scale modeling and model selection. The application identifies the best-fit dose-response model utilizing particle swarm optimization and calculates the probabilistic POD. The application analyzed a publicly available 2,3,7,8-tetrachlorodibenzo-p-dioxin dose-response data set of hepatic gene expression data in C57BL/6 mice to identify putative biomarkers. The Gene Ontology mapped these responses to specific functions to differentiate adaptive effects from toxic responses. In principle, safety and risk assessors could use the automated ToxResponse Modeler to analyze any large dose-response data set including outputs from high-throughput screening assays to assist with the ranking and prioritization of compounds that warrant further investigation or development.