Computational Toxicology and Exposure Communities of Practice: Computational approaches to integrate DNT NAMs for fit-for-purpose identification of DNT hazard
Date and Time
11:00 am - 12:00 pm EDT
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Topic: Computational approaches to integrate DNT NAMs for fit-for-purpose identification of DNT hazard
Who: Dr. Kelly Carstens, Biologist in the Center for Computational Toxicology and Exposure
When: March 24, 2022 from 11:00 AM- 12:00 PM EST
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Current developmental neurotoxicity (DNT) hazard assessment relies on in vivo testing that is resource intensive and lacks information on key cellular processes. To address these limitations, DNT new approach methodologies (NAMs) are being evaluated for their utility to inform DNT hazard, including: functional microelectrode array network formation assay (NFA) to evaluate neuronal network formation; and high-content imaging to evaluate proliferation, apoptosis, neurite outgrowth, and synaptogenesis. This work applies computational approaches to address three related hypotheses: (1) a broad screening battery will provide a sensitive marker of potential DNT bioactivity; (2) evaluating selective bioactivity may provide a more specific indicator of the functional processes underlying DNT; (3) some subset of the DNT NAM endpoints may optimally classify DNT in vivo reference chemicals.
The dataset contained 57 assay endpoints, with a union set of 92 screened chemicals, including 53 chemicals with evidence of in vivo DNT and 13 putative DNT negatives. K-means clustering of selectivity revealed five chemical clusters with distinct DNT-relevant activity and identified DNT evaluation chemicals with 66% sensitivity and 92% specificity, capturing 18 false negatives. We used a supervised machine learning approach to identify the most informative endpoints in classifying DNT evaluation chemicals. The five most important features included three cytotoxicity endpoints, network formation, and neurite outgrowth endpoints. This work highlights current obstacles in DNT NAM data interpretation, including a limited set of reference negatives and the question of whether cytotoxicity should be considered off-target or is relevant to deriving key DNT-related points-of-departure. Together, these data emphasize the importance of an integrated analysis that combines computational approaches, a broad chemical set, and a diverse suite of assays to demonstrate the fit-for-purpose utility of DNT NAMs for identification, hazard characterization, and prioritization of chemicals. This abstract does not reflect EPA policy.
This abstract does not necessarily reflect U.S. EPA policy.
For more information visit the EPA's Computational Toxicology Communities of Practice webpage.