Key result
NONMEM's PRIOR subroutine provides a fast, stable alternative for estimating PK parameters from previous models.
Why the study?
Due to data sparseness, population pharmacokinetic datasets often cannot estimate all suitable model parameters, making guidance needed for using the NONMEM PRIOR subroutine with previous model values.
This review provides practical guidance on using the PRIOR subroutine in NONMEM for population pharmacokinetic modeling when datasets are sparse.
May guide parameter estimation in sparse PK data; leaves open validation across therapeutic populations.
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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Kwong et al. (2020) conducted a review in Population pharmacokinetics. NONMEM PRIOR subroutine was evaluated. The PRIOR subroutine in NONMEM supports the estimation of pharmacokinetic parameters with values from previous models, providing a fast and stable alternative to fixing parameters or adding data.
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