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. 2005 Oct 7;7(3):E503–E512. doi: 10.1208/aapsj070351

Impact of pharmacometrics on drug approval and labeling decisions: A survey of 42 new drug applications

Venkatesh A Bhattaram 1,✉, Brian P Booth 1, Roshni P Ramchandani 1, B Nhi Beasley 1, Yaning Wang 1, Veneeta Tandon 1, John Z Duan 1, Raman K Baweja 1, Patrick J Marroum 1, Ramana S Uppoor 1, Nam Atiqur Rahman 1, Chandrahas G Sahajwalla 1, J Robert Powell 1, Mehul U Mehta 1, Jogarao V S Gobburu 1,✉
PMCID: PMC2751253  PMID: 16353928

Abstract

The value of quantitative thinking in drug development and regulatory review is increasingly being appreciated. Modeling and simulation of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression is often referred to as the pharmacometrics analyses. The objective of the current report is to assess the role of pharmacometrics at the US Food and Drug Administration (FDA) in making drug approval and labeling decisions. The New Drug Applications (NDAs) submitted between 2000 and 2004 to the Cardio-renal, Oncology, and Neuropharmacology drug products divisions were surveyed. For those NDA reviews that included a pharmacometrics consultation, the clinical pharmacology scientists ranked the impact on the regulatory decision(s). Of about a total of 244 NDAs, 42 included a pharmacometrics component. Review of NDAs involved independent, quantitative evaluation by FDA pharmacometricians, even when such analysis was not conducted by the sponsor. Pharmacometric analyses were pivotal in regulatory decision making in more than half of the 42 NDAs. Of the 14 reviews that were pivotal to approval related decisions, 5 identified the need for additional trials, whereas 6 reduced the burden of conducting additional trials. Collaboration among the FDA clinical pharmacology, medical, and statistical reviewers and effective communication with the sponsors was critical for the impact to occur. The survey and the case studies emphasize the need for early interaction between the FDA and sponsors to plan the development more efficiently by appreciating the regulatory expectations better.

Keywords: regulatory decisions, modeling, simulation, FDA, dose-response

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