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A Data-driven Model Analysis of Retinoid Signaling in Skeletal Dysmorphogenesis and Potential Adverse Outcome Pathways
Pierro, J., Nancy C. Baker, A. Richard, N. Kleinstreuer, AND T. Knudsen. A Data-driven Model Analysis of Retinoid Signaling in Skeletal Dysmorphogenesis and Potential Adverse Outcome Pathways. Society for Birth Defects Research and Prevention 61st Annual Meeting, Virtual, NC, June 24 - July 01, 2021. https://doi.org/10.23645/epacomptox.14893212
Presentation to the Society for Birth Defects Research and Prevention 61st Annual Meeting June 2021
Homeotic transformations and malformations of the fetal skeleton can occur as locally-regulated all-trans retinoic acid (ATRA) gradients. Such gradients determine skeletal patterning morphogenesis and can be disrupted by diverse genetic or environmental factors. Adverse Outcome Pathway (AOP) frameworks for ATRA metabolism, signaling, and homeostasis allow for the development of scalable computational models to support new approach methodologies (NAMs) to improve predictive toxicology without animal experimentation. Here, a data-driven model was constructed to identify chemicals associated with both ATRA pathway bioactivity and prenatal skeletal defects. We identified altered skeletal phenotypes in prenatal developmental toxicity studies in ToxRefDB and/or ToxCast high-throughput screening (HTS) and identified 375 chemicals associated with the alterations. Defects were organized into four skeletal phenotype groupings: cranial, post-cranial axial, appendicular, and other non-specified skeletal defects. For each chemical, the distribution of phenotype(s) was scored as a normalized fraction for inclusion in ToxPi k-means clustering. The clustering identified trends in skeletal defects due to shared structural characteristics among chemicals. Chemotypes were examined to identify structural similarities between chemicals with ATRA bioactivity and skeletal defects. In order to build a multivariate statistical model, HTS results from >8,070 chemicals in ToxCast/Tox21 across 13 in vitro assays, representing key nodes in the retinoid signaling system were evaluated and compared to candidate reference chemicals for in vitro testing. Over 40 chemicals were identified for constructing data-driven models to link this in vitro data with adverse skeletal outcomes for computational modeling. These preliminary findings will guide the development of dynamic modeling and AOPs for mechanistic validation to strengthen evidence for causality. This abstract does not represent the official views of EPA or any government agency.
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PIERRO BDRP PPT 2021 V.5 FINAL_6.10.21.PDF (PDF, NA pp, 3688.914 KB, about PDF)