| アブストラクト | When events lead to clinical problems, the mechanisms involved often remain unclear. This is true for medications and therapies, in addition to problems inherent in an underlying disease. However, the recent development of modeling and metric methods makes it possible to estimate the relationship between side effects and various factors to explain inter-individual differences, such as genetic polymorphisms, co-administered drugs, age, gender, dysfunction of the liver/kidney based upon the database for side effects [such as Food and Drug Administration-Adverse Event Reporting System (FDA-AERS)] and the database in a patient's medical records. Once the mechanisms for such clinical problems have been clarified, and after revisiting preclinical studies (animal models, in vitro cell systems, etc.), those outcomes may lead to drug discovery, the development of new therapies, and methods to prevent unique drug induced side effects. Reverse translational research (rTR) is such an approach, and a worthy aim of pharmaceutical scientists skilled at basic research. In this presentation, I would like to share with you our following recent studies: (1) rTR aimed at a therapy for progressive familial intrahepatic cholestasis 2 (PFIC 2). (2) rTR aimed at developing methods to predict drug-induced side effects based on single nucleotide polymorphisms (SNPs) information and a patient's medical records database. And (3) rTR aimed at predicting drug-drug interactions in which clinical outcomes have not been obtained, yet based upon previous clinically relevant drug interaction databases. |
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