Development of toxicity prediction system using structure descriptors and in vitro data of chemical substances

Abstract

Currently, in Japan, 28-day repeated dose studies are conducted on animals based on the Chemical Substances Control Law as a compound toxicity test. This test has problems such as high test costs of tens of millions of yen per compound, reduced competitiveness of the Japanese science industry due to long-term test periods, and ethical issues. In addition, in conventional toxicity prediction models using only quantitative structure-activity relationship (QSAR) by machine learning, the mechanism of action of compounds on cells is a black box and the applicability domain of the model is not clear. Therefore, we are considering the three-stage model. At first, whether "the compound will be absorbed into the body", is predicted, "cytotoxicity test results from compound information" for absorbed compounds, and "toxicity in each organ from compound information and cytotoxicity test results", aiming to clarify the applicability domain (AD) for each model. In this study, we analyzed various outlier detection methods for a model that predicts "cytotoxicity test results from compound information" and investigated which method is effective as an index for setting AD.