Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling

Jocylin D Pierro; Bhavesh K Ahir; Nancy C Baker; Nicole C Kleinstreuer; Menghang Xia; Thomas B Knudsen

doi:10.3389/fphar.2022.971296

Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling

Front Pharmacol. 2022 Sep 6:13:971296. doi: 10.3389/fphar.2022.971296. eCollection 2022.

Authors

Jocylin D Pierro¹, Bhavesh K Ahir², Nancy C Baker³, Nicole C Kleinstreuer⁴, Menghang Xia⁵, Thomas B Knudsen¹

Affiliations

¹ Center for Computational Toxicology and Exposure (CCTE), Computational Toxicology and Bioinformatics Branch (CTBB), Office of Research and Development (ORD), U.S. Environmental Protection Agency (USEPA), Research Triangle Park, NC, United States.
² Eurofins Medical Device Testing, Lancaster, PA, United States.
³ Scientific Computing and Data Curation Division (SCDCD), Leidos Contractor, Center for Computational Toxicology and Exposure (CCTE), USEPA/ORD, Research Triangle Park, NC, United States.
⁴ Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Toxicology Program, National Institutes of Health, Research Triangle Park, NC, United States.
⁵ Division for Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States.

Abstract

All-trans retinoic acid (ATRA) gradients determine skeletal patterning morphogenesis and can be disrupted by diverse genetic or environmental factors during pregnancy, leading to fetal skeleton defects. Adverse Outcome Pathway (AOP) frameworks for ATRA metabolism, signaling, and homeostasis allow for the development of new approach methods (NAMs) for predictive toxicology with less reliance on animal testing. Here, a data-driven model was constructed to identify chemicals associated with both ATRA pathway bioactivity and prenatal skeletal defects. The phenotype data was culled from ToxRefDB prenatal developmental toxicity studies and produced a list of 363 ToxRefDB chemicals with altered skeletal observations. Defects were classified regionally as cranial, post-cranial axial, appendicular, and other (unspecified) features based on ToxRefDB descriptors. To build a multivariate statistical model, high-throughput screening bioactivity data from >8,070 chemicals in ToxCast/Tox21 across 10 in vitro assays relevant to the retinoid signaling system were evaluated and compared to literature-based candidate reference chemicals in the dataset. There were 48 chemicals identified for effects on both in vivo skeletal defects and in vitro ATRA pathway targets for computational modeling. The list included 28 chemicals with prior evidence of skeletal defects linked to retinoid toxicity and 20 chemicals without prior evidence. The combination of thoracic cage defects and DR5 (direct repeats of 5 nucleotides for RAR/RXR transactivation) disruption was the most frequently occurring phenotypic and target disturbance, respectively. This data model provides valuable AOP elucidation and validates current mechanistic understanding. These findings also shed light on potential avenues for new mechanistic discoveries related to ATRA pathway disruption and associated skeletal dysmorphogenesis due to environmental exposures.

Keywords: New Approach Methods; ToxCast/Tox21; developmental toxicity; retinoic acid; skeletal defects.