Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine
Montpellier University;
Inst Paoli Calmettes;
Institut Paoli-Calmettes;
Inserm;
Montpellier Univ;
Sch Pharm;
Translational Medicine and Early;
Marseille;
Pharmacokinetic and Modeling Department;
Pharmacokinet Dynam & Metab PKDM;
CRCM;
SMARTc Grp;
Pharmacokinet & Modeling Dept;
SMARTc group;
School of Pharmacy;
Aix-Marseille University;
Montpellier;
Translat Med & Early Dev;
Sanofi R&D;
Aix Marseille Univ;
Pharmacokinetics-Dynamics and Metabolism (PKDM);
CNRS;
Inserm;
CNRS;
France;
Sanofi R&D;
关键词：
Pharmacokinetic-pharmacodynamic;
Guidance;
Population pharmacokinetics;
PRIOR;
Model;
NONMEM;
期刊名称：
Journal of pharmacokinetics and pharmacodynamics
i s s n：
1567-567X
年卷期：
2020 年
47 卷
5 期
页 码：
431-446
页 码：
摘 要：
Abstract Population pharmacokinetic analysis is used to estimate pharmacokinetic parameters and their variability from concentration data. Due to data sparseness issues, available datasets often do not allow the estimation of all parameters of the suitable model. The PRIOR subroutine in NONMEM supports the estimation of some or all parameters with values from previous models, as an alternative to fixing them or adding data to the dataset. From a literature review, the best practices were compiled to provide a practical guidance for the use of the PRIOR subroutine in NONMEM. Thirty-three articles reported the use of the PRIOR subroutine in NONMEM, mostly in special populations. This approach allowed fast, stable and satisfying modelling. The guidance provides general advice on how to select the most appropriate reference model when there are several previous models available, and to implement and weight the selected parameter values in the PRIOR function. On the model built with PRIOR, the similarity of estimates with the ones of the reference model and the sensitivity of the model to the PRIOR values should be checked. Covariates could be implemented a priori (from the reference model) or a posteriori, only on parameters estimated without prior (search for new covariates).
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