Drug target finding based on clinical big data analysis and pharmacological experiments

Abstract

Historically, basic pharmacology has long used adverse drug reactions to create models of human pathological conditions in experimental animals. This is because the phenotypes and/or pathogenic mechanisms of the adverse events share similarities with spontaneous human diseases. On the other hand, a large amount of real-world data (RWD), such as spontaneous reports of adverse drug reactions and insurance claims data, become available recently. Therefore statistical analysis of RWD has made it possible to accurately identify novel and unexpected confounding factors (e.g., concomitant medication) that influence the occurrence of adverse events or spontaneous disease. Such pharmacological drug-drug interactions not only lead to immediate drug repositioning, but also to the elucidation of adverse event mechanisms and the discovery of new drug targets. In addition, hypotheses derived from RWD analysis are expected to have extremely high clinical predictive value. In this presentation, we will show how RWD analysis can lead to the discovery of drug targets, by introducing examples of research from our previous reports, and discuss the potential of new research strategies in the future.