Population pharmacokinetics - A regulatory perspective

The application of population approaches to drug development is recommended in several US Food and Drug Administration (Fl)A) guidance documents. Popu lation pharmacokinetic (and pharmacodynamic) techniques enable identificati on of the sources of inter- and intra-individual variability that impinge. upon drug Safety and efficacy. This article briefly discusses the 2-stage a pproach to the estimation of population pharmacokinetic parameters, which r equires serial multiple measurements on each participant, and comprehensive ly reviews the nonlinear mixed-effects. modelling approach, which can be ap plied in situations where extensive sampling is not done on all or any of t he participants. Certain preliminary information, such as the compartment model used in desc ribing the pharmacokinetics of the drug, is required for a population pharm acokinetic study. The practical design considerations of the location of sa mpling times, number of samples/participants and the need to sample an indi vidual more than once should be borne in mind. Simulation may be useful for choosing the study design that will best meet study objectives. The objectives of the population pharmacokinetic study can be secondary to the objectives of the primary clinical study lin which case an add-on popul ation pharmacokinetic protocol may be needed) or primary (when a stand-alon e protocol is required). Having protocols for population pharmacokinetic st udies is an integral part of 'good pharmacometric practice'. Real-time data assembly and analysis permit an ongoing evaluation of site c ompliance with the study protocol and provide the opportunity to correct vi olations of study procedures. Adequate policies and procedures should be in place for study blind maintenance. Real-time data assembly creates the opp ortunity for detecting and correcting errors in concentration-time data, dr ug administration history and covariate data. Population pharmacokinetic analyses may be undertaken in 3 interwoven steps : exploratory data analysis, model development and model validation (i.e. p redictive performance). Documentation for regulatory purposes should includ e a complete inventory of key runs in the analyses undertaken (with flow di agrams if possible), accompanied by articulation of objectives, assumptions and hypotheses. Use of diagnostic analyses of goodness of fit as evidence of reliability of results is advised. Finally, the use of stability testing or model validation may be warranted to support label claims. The opinions expressed in this article were revised by incorporating commen ts from various sources and published by the FDA as 'Guidance for Industry: Population Pharmacokinetics' (see the FDA home page http://www.fda.gov for further information).