Skin sensitization is a significant concern for chemical safety assessments. Traditional animal assays often fail to predict human responses accurately, and ethical constraints limit the collection of human data, necessitating a need for reliable in silico models of skin sensitization prediction. This study introduces HuSSPred, an in silico tool based on the Human Predictive Patch Test (HPPT). HuSSPred aims to enhance the reliability of predicting human skin sensitization effects for chemical agents to support their regulatory assessment. We have curated an extensive HPPT database and performed chemical space analysis and grouping. Binary and multiclass QSAR models were developed with Bayesian hyperparameter optimization. Model performance was evaluated via five-fold cross-validation. We performed model validation with reference data from the Defined Approaches for Skin Sensitization (DASS) app. HuSSPred models demonstrated strong predictive performance with CCR ranging from 55 to 88%, sensitivity between 48 and 89%, and specificity between 37 and 92%. The positive predictive value (PPV) ranged from 84 to 97%, versus negative predictive value (NPV) from 22 to 65%, and coverage was between 75 and 93%. Our models exhibited comparable or improved performance compared to existing tools, and the external validation showed the high accuracy and sensitivity of the developed models. HuSSPred provides a reliable, open-access, and ethical alternative to traditional testing for skin sensitization. Its high accuracy and reasonable coverage make it a valuable resource for regulatory assessments, aligning with the 3Rs principles. The publicly accessible HuSSPred web tool offers a user-friendly interface for predicting skin sensitization based on chemical structure.

We introduce STOPLIGHT, a web portal to assist medicinal chemists in prioritizing hits from screening campaigns and selection of compounds for optimization. STOPLIGHT incorporates services to assess 6 physiochemical and structural properties, 6 assay liabilities, and 11 pharmacokinetic properties for any small molecule represented by its SMILES string. We briefly describe each service and illustrate the utility of this portal with a case study. The STOPLIGHT portal provides a user-friendly tool to guide hit selection in early drug discovery campaigns, whereby compounds with unfavorable properties can be quickly recognized and eliminated. Availability and implementation: STOPLIGHT is freely accessible at https://stoplight.mml.unc.edu/.

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/).