Overview of model‐building strategies in population PK/PD analyses: 2002–2004 literature survey

Abstract

What is already known about this subject • The reviews already published on population pharmacokinetic/pharmacodynamic (PK/PD) analyses have focused on theory and have presented some clinical applications, evaluated validation practices in limited circumstances, defined the interest and sometimes the complexity of this approach in drug development or proposed a list of relevant articles.• None of them has exhaustively evaluated published analyses and more precisely the model-building steps.• In view of the statistical complexity of population PK/PD methodology, more attention is required to how models are built and how they are reported in the literature. What this study adds• With a strict methodology and by establishing a standardized tool, this survey provides an exhaustive, objective and up-to-date review of model-building practices.• It reveals deficiencies in information reporting in most articles and the genuine need for guidance in publishing.• An initial, minimal list of items is suggested, which can be used by authors and reviewers in pharmacology journals.• The value of published peer-reviewed papers could be greatly improved if authors were to address the suggested list of items systematically. MethodsWe selected 324 articles in Pubmed using defined keywords. A data abstraction form (DAF) was then built comprising two parts: general characteristics including article identification, context of the analysis, description of clinical studies from which the data arose, and model building, including description of the processes of modelling. The papers were examined by two readers, who extracted the relevant information and transmitted it directly to a MySQL database, from which descriptive statistical analysis was performed. ResultsMost published papers concerned patients with severe pathology and therapeutic classes suffering from narrow therapeutic index and/or high PK/PD variability. Most of the time, modelling was performed for descriptive purposes, with rich rather than sparse data and using NONMEM software. PK and PD models were rarely complex (one or two compartments for PK; Emax for PD models). Covariate testing was frequently performed and essentially based on the likelihood ratio test. Based on a minimal list of items that should systematically be found in a population PK-PD analysis, it was found that only 39% and 8.5% of the PK and PD analyses, respectively, published from 2002 to 2004 provided sufficient detail to support the model-building methodology. ConclusionsThis survey allowed an efficient description of recent published population analyses, but also revealed deficiencies in reporting information on model building.

Application of Stable Isotope Methodology in the Evaluation of the Pharmacokinetics of (S,S)-3-[3-(Methylsulfonyl)phenyl]-1-propylpiperidine Hydrochloride in Rats

“…In addition to noncompartmental pharmacokinetic analysis, the pharmacokinetic model that describes the parent drug and M1 in portal and jugular veins was developed to evaluate pharmacokinetic parameters using the nonlinear mixed effect model (NONMEM) (Beal and Sheiner, 1992). The NONMEM technique has been widely used and applied to preclinical and clinical pharmacokinetic and pharmacokinetic-pharmacodynamic analyses of many xenobiotics (Yukawa, 1999;Bauer et al, 2007;Dartois et al, 2007;Yamazaki et al, 2008). In the present study, the compartmental pharmacokinetic modeling provided insights into the disposition and biotransformation rates of PNU96391 and M1, suggesting that the modeling could add further advantages to the SI coadministration approach.…”

Individual Bayesian Information Matrix for Predicting Estimation Error and Shrinkage of Individual Parameters Accounting for Data Below the Limit of Quantification

“…These population parameters are usually obtained by Maximum Likelihood approach [4]. Once population parameters are estimated, individual parameters can then be derived, using Bayesian approach which combines 'a priori' information of population parameters estimated previously and the individual data.…”

“…This approach relies on the Cramer-Rao inequality, which states that the inverse of the MF is the lower bound of the variance-covariance matrix of any unbiased estimator of the parameters. Expressions of the expected individual (MIF) and population Fisher information matrix (MPF) using first-order (FO) approximation [5,6] have been developed and implemented in several software programs [7,8] to evaluate and optimize 4 designs for standard individual regression or population analysis, respectively. Beside MIF and MPF, the expected Bayesian Fisher information matrix (MBF) was also developed to evaluate the estimation error of individual parameters obtained by MAP [9,10].…”

Population Pharmacokinetics of Therapeutic Monoclonal Antibodies: Examples and Estimation Method Performance Differences

“…In addition, a Bayesian MCMC method was incorporated in the recent release of NONMEM version VII (164). A benefit to using NONMEM is that it is the most widely used software for population PK/PD modeling (151), and since it has been available for more than 30 years, there is a wealth of studies and experience with using NONMEM in the literature. For pharmacometricians that have primarily used NONMEM for population PK/PD modeling, switching to WinBUGS may require additional training and education in Bayesian analysis and MCMC methods.…”

“…The findings from published therapeutic mAb population PK analyses were used in the present study to define aspects of the candidate population PK simulation model in order to simulate PK data for a representative therapeutic mAb. The software NONMEM was included in this study as it is the most widely used software for population PK/PD modeling (151). WinBUGS was chosen as the Bayesian MCMC method provides a different approach compared to the estimation methods in NONMEM to solving the population PK problem (152), and there is a growing interest in applying full Bayesian methods in population PK modeling.…”