PregPred: an In-Silico Alternative to Animal Testing for Predicting Developmental Toxicity Potential

Background: Understanding potential prenatal and development toxicity hazard associated with the use of pharmaceutical and cosmetic products is an important component of women health. This hazard can be estimated from chemical structure of respective agents using Quantitative Structure-Activity Relationship (QSAR) models; however, the development of reliable models is challenging due to the complex nature of this endpoint. Methods: Aggregating and curating data from the Food and Drug Administration (FDA), Teratogen Information System (TERIS) database, and select independent studies, we have created, to the best of our knowledge, the largest publicly available dataset comprising compounds annotated as developmental toxicants or not toxicants. Results: We built several binary classification QSAR models exhibiting a correct classification rate of 62-72%, a sensitivity of 66-75%, a specificity of 59-82%, and high coverage of 70-90% assessed using five-fold external validation protocol. We developed a publicly accessible web portal PregPred for developmental toxicity prediction of both overall toxicity and trimester-specific toxicity predictions. Conclusions: Due to high accuracy and coverage as well as public accessibility of the respective web portal, our models can be employed as a computational tool to support regulatory assessment of pharmaceutical and cosmetic products in alignment with the 3Rs (refining, reducing, and replacing) of animal testing. This in silico model holds the potential to substantially influence the field of developmental toxicology, steering regulatory practices toward safer drug development for pregnant women. The first-of-its-kind curated dataset of developmental toxicants and all developed models implemented as a user-friendly web tool, PregPred, are freely available at https://pregpred.mml.unc.edu/).