Development of in silico predictive classification models for chemical-induced hepatocellular hypertrophy based on molecular descriptors
Kaori Ambe, Tatsuya Ochibe, Kazuyuki Ohya, Masahiro Tohkin · Proceedings for Annual Meeting of The Japanese Pharmacological Society · 2018
Introduction: Because a lot of cost, time, and laboratory animals are required in the repeated dose toxicity tests, in silico prediction of toxicity is desired. Chemical-induced hepatocellular hypertrophy often influences the No-Observed-Adverse-Effect Level in repeated dose toxicity tests. Since hepatocellular hypertrophy is caused by various chemicals and the mechanisms are mostly unknown, it is necessary to develop a prediction method which does not require comprehensive understanding of mechanism. In this study, we developed predictive classification models of hepatocellular hypertrophy in rats using machine learning methods.