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Computational systems models for human-predictive developmental toxicity
Citation:
Barham, K., R. Spencer, AND T. Knudsen. Computational systems models for human-predictive developmental toxicity. European Society For Toxicology In Vitro Toxicology (ESTIV), Barcelona, Catalonia, SPAIN, November 21 - 25, 2022. https://doi.org/10.23645/epacomptox.21817893
Impact/Purpose:
This is an invited presentation in for The 21st International Congress of the European Society of Toxicology In Vitro (ESTIV) session 2a: "Models, biomarkers and assays for endocrine disruption and developmental toxicity".
Description:
Assessing developmental toxicity has a critical role in environmental health policy. New approach methodologies (NAMs) that enable in vitro profiling aim to quickly evaluate the human toxicity potential of thousands of chemicals with less reliance on animal testing. This comes with the need for computational models to translate data into toxicological prediction. A ToxCast platform using a metabolic biomarker-based pluripotent human (H9) stem cell assay (ToxCast_STM) identified a signal for developmental toxicity in 183 of 1062 chemicals [Zurlinden et al. (2020) Toxicol Sci]. Performance-based models classified the potential for developmental toxicity with balanced accuracies (BAC) of up to 84% for 42 well-characterized reference compounds. Although highly specific, the dataset showed weaker sensitivity where developmental toxicity was less concordant between rat-rabbit fetal outcomes, coincided with adverse effects on the pregnant mother, or were missed due to limited biological coverage of the assay. Cell-oriented computational systems models with sufficient intelligence of embryology to quantitatively simulate morphodynamics are being evaluated to augment the in vitro bioactivity profiles. Case examples will be demonstrated with disruption of the retinoid signaling pathway. This abstract does not reflect EPA policy.
URLs/Downloads:
DOI: Computational systems models for human-predictive developmental toxicity
ESTIV_2022_KNUDSEN_STICS.PDF (PDF, NA pp, 2090.096 KB, about PDF)