[Adverse Effect Predictions Based on Computational Toxicology Techniques and Large-scale Databases]

Abstract

Understanding the features of chemical structures related to the adverse effects of drugs is useful for identifying potential adverse effects of new drugs. This can be based on the limited information available from post-marketing surveillance, assessment of the potential toxicities of metabolites and illegal drugs with unclear characteristics, screening of lead compounds at the drug discovery stage, and identification of leads for the discovery of new pharmacological mechanisms. This present paper describes techniques used in computational toxicology to investigate the content of large-scale spontaneous report databases of adverse effects, and it is illustrated with examples. Furthermore, volcano plotting, a new visualization method for clarifying the relationships between drugs and adverse effects via comprehensive analyses, will be introduced. These analyses may produce a great amount of data that can be applied to drug repositioning.

Keywords: adverse effect; computational toxicology; database; prediction model; quantitative structure-activity relationship.