A perspective and a new integrated computational strategy for skin sensitization assessment

Abstract

Traditionally, the skin sensitization potential of chemicals has been assessed using animal models. Due to growing ethical, political, and financial concerns, sustainable alternatives to animal testing need to be developed. As publicly available skin sensitization data continues to grow, computational approaches, such as alert-based systems, read-across, and QSAR models, are expected to reduce or replace animal testing for the prediction of human skin sensitization potential. Herein, we discuss current computational approaches to predicting skin sensitization and provide future perspectives of the field. As a proof-of-concept study, we have compiled the largest skin sensitization data set in the public domain and benchmarked several methods for building skin sensitization models. We propose a new comprehensive approach, which integrates multiple QSAR models developed with in vitro, in chemico, animal, and human data, and a Naive Bayes model for predicting human skin sensitization. Both the data sets and the KNIME implementation of the model allowing skin sensitization prediction for molecules of interest have been made freely available.