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Development of prediction models for drug-induced liver marlignant tumors using a large scale self-reporting adverse drug event database

We aim to gather researchers in the fields related to drug toxicity prediction by in silico methods, and to activate information exchanges for a research in this field in this symposium. Adverse drug reactions are serious factors for new drug candidates to drop out from the developing process. It is desired to control the risk of adverse drug reactions as well as the basic properties of drugs such as drug efficacy and physical in the early stages of drug development because the adverse drug reactions are the chemical toxic aspect of drugs. Therefore, it has been remarkable to use in silico method in recent years for quick and efficient evaluating of the risk of adverse drug reactions. In this symposium, we will introduce the developments of in silico methods for evaluating the toxicity and risk of adverse drug reactions, such as a machine learning and statistics prediction models using the existing databases related to pharmaceuticals or the in-house databases. Furthermore, we will discuss future prospects of studies in this field to establish evidences that the use of in silico methods is useful in accelerating drug development.

Prediction of drug toxicity by data-driven approach

The 142nd Annual Meeting of the Pharmaceutical Society of Japan (Nagoya)

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[GS01-2]Development of prediction models for drug-induced liver marlignant tumors using a large scale self-reporting adverse drug event database

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