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Exploratory analyses of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression are often referred to as the pharmacometrics (PM) analyses. The objective of the current report is to assess the role of PM, at the Food and Drug Administration (FDA), in drug approval and labeling decisions. We surveyed the impact of PM analyses on New Drug Applications (NDAs) reviewed over 15 months in 2005-2006. The survey focused on both the approval and labeling decisions through four perspectives: clinical pharmacology primary reviewer, their team leader, the clinical team member, and the PM reviewer. A total of 31 NDAs included a PM review component. Review of NDAs involved independent quantitative evaluation by FDA pharmacometricians. PM analyses were ranked as important in regulatory decision making in over 85% of the 31 NDAs. Case studies are presented to demonstrate the applications of PM analysis.

Recently, regulatory bodies have published guidance related to this topic. 2-7 A common thread among these various documents is an emphasis on risk-based approach to data management, specifically targeting data and study procedures that are critical and have the greatest impact on maintaining subject safety and determining product efficacy. For example, the Medicines and Healthcare products Regulatory Agency (MHRA) Good Practices (GXP) Data Integrity Guidance and Definitions document discusses the data lifecycle, data governance, and other organizational culture features to be considered in a risk-based approach. 2 An open reporting culture in organizations should be encouraged as fundamental to data integrity promotion throughout the data lifecycle, including processes from generation or recording of data to destruction, if needed, and the intervening processes (Figure 1).