Literature DB >> 20208385

Drug interaction studies on new drug applications: current situations and regulatory views in Japan.

Naomi Nagai1.   

Abstract

Drug interaction studies on new drug applications (NDAs) for new molecular entities (NMEs) approved in Japan between 1997 and 2008 are examined in the Pharmaceuticals and Medical Devices Agency (PMDA). The situations of drug interaction studies in NDAs have changed over the past 12 years, especially in metabolizing enzyme and transporter-based drug interactions. Materials and approaches to study drug-metabolizing enzyme-based drug interactions have improved, and become more rational based on mechanistic theory and new technologies. On the basis of incremental evidence of transporter roles in human pharmacokinetics, transporter-based drug interactions have been increasingly studied during drug development and submitted in recent NDAs. Some recently approved NMEs include transporter-based drug interaction information in their package inserts (PIs). The regulatory document "Methods of Drug Interaction Studies," in addition to recent advances in science and technology, has also contributed to plan and evaluation of drug interaction studies in recent new drug development. This review summarizes current situations and further discussion points on drug interaction studies in NDAs in Japan.

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Year:  2010        PMID: 20208385     DOI: 10.2133/dmpk.25.3

Source DB:  PubMed          Journal:  Drug Metab Pharmacokinet        ISSN: 1347-4367            Impact factor:   3.614


  3 in total

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Authors:  Robert Gharavi; William Hedrich; Hongbing Wang; Hazem E Hassan
Journal:  Pharm Res       Date:  2015-05-14       Impact factor: 4.200

2.  TP-DDI: A Two-Pathway Deep Neural Network for Drug-Drug Interaction Prediction.

Authors:  Jiang Xie; Chang Zhao; Jiaming Ouyang; Hongjian He; Dingkai Huang; Mengjiao Liu; Jiao Wang; Wenjun Zhang
Journal:  Interdiscip Sci       Date:  2022-05-27       Impact factor: 3.492

3.  Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data.

Authors:  Wen Zhang; Yanlin Chen; Feng Liu; Fei Luo; Gang Tian; Xiaohong Li
Journal:  BMC Bioinformatics       Date:  2017-01-05       Impact factor: 3.169

  3 in total

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