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\begin{document}

\title{Assessment of the nlmixr R-package for population pharmacokinetic
modeling: A metformin case study}



\author[1]{Wen Yao Mak}%
\author[2]{Qing Xi Ooi}%
\author[3]{Cintia Cruz}%
\author[4]{Irene Looi}%
\author[5]{Kah Hay Yuen}%
\author[6]{Joseph Standing}%
\affil[1]{Hospital Pulau Pinang}%
\affil[2]{Pharmetheus AB}%
\affil[3]{Mahidol University}%
\affil[4]{Hospital Seberang Jaya}%
\affil[5]{Universiti Sains Malaysia}%
\affil[6]{University College London}%


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  \date{May 26, 2024}


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\begin{abstract}
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 population pharmacokinetics with
flip-flop characteristics within nlmixr framework and investigated SAEM
and FOCEi's performance with respect to bias, precision, and robustness.
Method: Compartmental pharmacokinetic models were fitted. The final
model was determined based on the lowest objective function value and
visual inspection of goodness-of-fit plots. To examine flip-flop
pharmacokinetics, k\_a values of a typical concentration-time profile
based on the final model were perturbed and changes in the steepness of
the terminal elimination phase were evaluated. 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: A one-compartment model with
transit compartment for absorption best described the data. At low n,
Stirling's approximation of n! over-approximated plasma concentration
unlike the log-gamma function. Flip-flop pharmacokinetics were evident
as the steepness of the terminal elimination phase changed with k\_a.
Mean rRMSE for fixed-effect parameters was 0.932. When initial estimates
were perturbed, FOCEi estimates of k\_a and food effect on k\_a appeared
bimodal and were upward biased. Discussion: nlmixr is reliable for NLMEM
even if flip-flop is present but caution should be exercised when using
Stirling's approximation for n! in the transit compartment model. SAEM
was marginally superior to FOCEi in bias and precision, but SAEM was
superior against initial estimate perturbations.%
\end{abstract}%



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