Volume 105, Issue 4 p. 899-911
State of the Art

Model-Informed Drug Development: Current US Regulatory Practice and Future Considerations

Yaning Wang

Corresponding Author

Yaning Wang

Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver Spring, Maryland, USA

Correspondence: Yaning Wang ([email protected])Search for more papers by this author
Hao Zhu

Hao Zhu

Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver Spring, Maryland, USA

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Rajanikanth Madabushi

Rajanikanth Madabushi

Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver Spring, Maryland, USA

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Qi Liu

Qi Liu

Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver Spring, Maryland, USA

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Shiew-Mei Huang

Shiew-Mei Huang

Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver Spring, Maryland, USA

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Issam Zineh

Issam Zineh

Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver Spring, Maryland, USA

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First published: 17 January 2019
Citations: 130

Abstract

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.

Conflicts of Interest

The authors declared no competing interests for this work. As an Associate Editor for Clinical Pharmacology & Therapeutics, Shiew-Mei Huang was not involved in the review or decision process for this paper.