Mak, Wen Yao;
Ooi, Qing Xi;
Cruz, Cintia Valeria;
Yuen, Kah Hay;
Standing, Joseph F;
Assessment of the nlmixr R package for population pharmacokinetic modeling: A metformin case study.
British Journal of Clinical Pharmacology
Access restricted to UCL open access staff until 31 August 2023.
AIM: nlmixr offers first-order conditional estimation with or without interaction (FOCE or FOCEi) and stochastic approximation estimation-maximisation (SAEM) to fit nonlinear mixed-effect models (NLMEM). We modelled metformin’s pharmacokinetic data using nlmixr and investigated SAEM and FOCEi’s performance with respect to bias and precision of parameter estimates, and robustness to initial estimates. METHOD: Compartmental models were fitted. The final model was determined based on the objective function value and inspection of goodness-of-fit plots. The bias and precision of parameter estimates were compared between SAEM and FOCEi using stochastic simulations and estimations. For robustness, parameters were re-estimated as the initial estimates were perturbed 100-times and resultant changes evaluated. RESULTS: Absorption kinetics of metformin depends significantly on food status. Under the fasted state, the first-order absorption into the central compartment was preceded by zero-order infusion into the depot compartment, whereas for the fed state, the absorption into the depot was instantaneous followed by first-order absorption from depot into the central compartment. The mean of relative mean estimation error (rMEE) ( ME E SAEM ME E FOCEi ) and rRMSE ( RMS E SAEM RMS E FOCEi ) was 0.48 and 0.35 respectively. All parameter estimates given by SAEM appeared to be narrowly distributed and were close to the true value used for simulation. In contrast, the distribution of estimates from FOCEi were skewed and more biased . When initial estimates were perturbed, FOCEi estimates were more biased and imprecise. DISCUSSION: nlmixr is reliable for NLMEM. SAEM was superior to FOCEi in terms of bias and precision, and more robust against initial estimate perturbations.
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