Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance wit
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REVIEW PAPER
Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine Anna H.-X. P. Chan Kwong1,2,3,4 Sonia Khier1,2
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Elisa A. M. Calvier4 • David Fabre4 • Florence Gattacceca3
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Received: 27 January 2020 / Accepted: 8 June 2020 The Author(s) 2020
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).
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10928-020-09695-z) contains supplementary material, which is available to authorized users. 4
& Anna H.-X. P. Chan Kwong [email protected] 1
Pharmacokinetic and Modeling Department, School of Pharmacy, Montpellier University, Montpellier, France
2
Probabilities and Statistics Department, Institut Montpellie´rain Alexander Grothendieck (IMAG), UMR 5149, CNRS, Montpellier University, Montpellier, France
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SMARTc group, Inserm, CNRS, Institut Paoli-Calmettes, CRCM, Aix-Marseille University, Marseille, France
Pharmacokinetics-Dynamics and Metabolism (PKDM), Sanofi R&D, Translational Medicine and Early Development, Montpellier, France
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Journal of Pharmacokinetics and Pharmacodynamics
Graphic abstract Previous models - From literature - From previous development studies (θp, Ωp, σp)
1. Defining the reference model
Selection of a single model empirical selection
Combining several models
quality criteria of the Bayesian estimation on the new data
Robustness of the reference model
meta-analysis with random effect
combined model
Reference model (θr, Ωr, σr)
NWPRI or TNPRI
2. Code to provide prior information with $PRIOR subroutine
Weight of the priors remove irrelevant priors
select the best prior weight
3. Objective functions of the model built with prior Model built with p
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