Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine

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). Graphic abstract

Upadacitinib pharmacokinetics and exposure‐response analyses of efficacy and safety in psoriatic arthritis patients – Analyses of phase III clinical trials

“…A Bayesian pharmacokinetic modeling approach was implemented using prior information from an upadacitinib population pharmacokinetics model, which was previously developed using data from 4170 patients (96% patients with RA and 4% healthy subjects). 29 , 30 The structural, statistical (inter‐ and intrasubject variability), and covariate components of the model were maintained. Population parameter estimates, the variance‐covariance matrix of the fixed effects, and estimates for the random effects (inter‐ and intrasubject variability) from the RA 29 model were used as priors.…”

“… 29 , 30 The structural, statistical (inter‐ and intrasubject variability), and covariate components of the model were maintained. Population parameter estimates, the variance‐covariance matrix of the fixed effects, and estimates for the random effects (inter‐ and intrasubject variability) from the RA 29 model were used as priors. All model parameters were re‐estimated using the data in patients with PsA from the SELECT‐PsA studies.…”

Improved Decision-Making Confidence Using Item-Based Pharmacometric Model: Illustration with a Phase II Placebo-Controlled Trial

“…Specifically, NWPRI subroutine was used where prior fixed and random effects are assumed to be normally and inverse-Wishart distributed, respectively. Degrees of freedom for the inverse-Wishart distribution were calculated based on standard error (SE) of estimates (29). As illustrated in Fig.…”

The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives

“…However, keeping the principle of parsimony represents a challenge since it is probable that some model parameters will be hard to estimate precisely. As a consequence, the integration of different pharmacometric techniques, such as the use of Bayesian priors based on previous knowledge to inform poorly estimated parameters, is warranted [ 106 ].…”