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Model‐Informed Drug Development: Current US Regulatory Practice and Future Considerations
Author(s) -
Wang Yaning,
Zhu Hao,
Madabushi Rajanikanth,
Liu Qi,
Huang ShiewMei,
Zineh Issam
Publication year - 2019
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1002/cpt.1363
Subject(s) - scope (computer science) , drug development , variety (cybernetics) , process (computing) , computer science , drug , management science , risk analysis (engineering) , engineering ethics , drug approval , data science , knowledge management , business , process management , medicine , pharmacology , economics , artificial intelligence , engineering , programming language , operating system
Model‐informed drug development (MIDD) refers to the application of a wide range of quantitative models in drug development to facilitate the decision‐making process. MIDD was formally recognized in Prescription Drug User Fee Act (PDUFA) VI. There have been many regulatory applications of MIDD to address a variety of drug development and regulatory questions. These applications can be broadly classified into four categories: dose optimization, supportive evidence for efficacy, clinical trial design, and informing policy. Case studies, literature papers, and published regulatory documents are reviewed in this article to highlight some common features of these applications in each category. In addition to the further development and investment in these established domains of application, new technology, and areas, such as more mechanistic models, neural network models, and real‐world data/evidence, are gaining attention, and more submissions and experiences are being accumulated to expand the application of model‐based analysis to a wider scope.