Overall goal/objectives:
Traditional toxicity testing for environmental chemicals follows the preclinical model of drug safety testing, resting on the assumption that rodents serve as sufficient predictors of adverse health effects that may occur in humans. However, drug development suffers from high attrition rates, in large part due to observations of unexpected toxicity in early phase clinical trials that were not predicted by preclinical testing. This attrition due to safety concerns suggests a potential lack of translation between preclinical models, such as rodents, and human adverse events. To advance our ability to use new approach methods (NAMs) in setting protective exposure levels for environmental chemicals, it is critical to better understand performance of the current paradigm. Evaluating the concordance between common rodent models and humans provides a target benchmark of predictive performance for NAMs. While generally assumed to be predictive, reports on rates of qualitative concordance (presence/absence) between rodent and human adverse health outcomes have been highly variable. Other reports have suggested that the absence of effects in rodent models are protective of human responses, while specific effects are generally not predictive. Importantly, no large-scale studies have yet examined quantitative concordance (correlation in dose) between rodents and humans in relation to observation of adverse events. Because health protective levels for environmental chemicals are often set based on no/lowest observed adverse effects levels in rodents, assessing the correlation between toxic doses in rodents (using multiple dose metrics) and doses associated with adverse health effects in humans, for both matching (predictive) and non-matching (protective) hazard endpoints, can inform traditional and NAM-based approaches to environmental health protection. Therefore, the goal for this project is to use drug safety data to quantify concordance for both human to rodent and human to bioactivity-based toxicity measures.
Case Study Leader:
US EPA (Chelsea Weitekamp)
Collaborators:
US EPA, ECHA, Health Canada, JRC, FDA, NIEHS/DTT
Status: In progress
- Data mining and curation; preliminary analysis of concordance using correlation metrics
Presentations or publications that have been publicly released:
Manuscript in preparation
