In silico prediction of repeated-dose toxicity using an in vivo rat toxicity database

Abstract

In order to conduct safety assessment of chemical substances, experiments are often done with animals. However, in terms of time and cost efficiencies and the need for animal protection, computational methods for the prediction of chemical toxicity have been attracting great attentions recently. There are two frameworks for the toxicity prediction methods: QSAR and read-across approaches. Several systems are already commercially available to predict yes/no-type genotoxicity, based on QSAR approaches. However, few computational prediction methods for repeated-dose toxicity have been developed because of the diversity of observation items and the complexity of toxicity mechanism. Thus, in this study, we attempted to develop a prediction method of repeated-dose toxicity of rat liver, kidney, and blood, based on a read-across approach using HESS, which is an in vivo toxicity database publicly available from NITE, Japan, and statistical methods.