In 2018, US EPA released a draft policy to reduce animal testing for skin sensitization. The goal<br/>of this study was to assemble experimental data from online data sources and develop QSAR (quantitative structure activity relationship) models to predict skin sensitization, skin irritation, and eye irritation. Data was extracted from a variety of online data sources including eChemPortal, NICEATM, QSAR Toolbox, and the open literature. Using Java code, the data was converted to a consistent data format and stored in an SQLite database. Each record was mapped to a unique substance ID in EPA’s Distributed Structure-Searchable Toxicology Database. The substance ID allows one to associate each record with a “QSAR-ready” SMILES string which is then used to generate molecular descriptors. Data set records consist of an ID value, a property value, and the molecular descriptor values. Records which contained the same two-dimensional inChiKey were merged. Discordant records were omitted and the data sets were randomly split into a training and prediction sets. For the skin irritation models, to account for corrosive behavior, two layers of binary classification were employed from intervals of the primary irritation index endpoint: distinguishing active vs. inactive substances, and then within the active set, distinguishing irritant vs. corrosive substances. Models were built using methods including random forest, support vector machines (SVM), XGBOOST, Deep Neural Networks (DNN), and k nearest neighbors (kNN). We optimized the hyperparameters for each model by selecting the set which performed best for internal cross validation of the training set or among many different external validation sets. We optimized the classification error, gamma and nu parameters for the SVM method and the learning rate, estimator count, and maximum depth for the XGBoost method. Consensus models averaging the results from the approaches listed above were also evaluated.

Development of skin sensitization, skin irritation, and eye irritation models using online data sources and Python-based machine learning

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.