Mak Wen Yao, Ooi Qing Xi, Cruz Cintia Valeria, Looi Irene, Yuen Kah Hay, Standing Joseph F
Clinical Research Centre, Penang General Hospital, Penang, Malaysia.
Institute for Clinical Research, National Institute of Health, Selangor, Malaysia.
Br J Clin Pharmacol. 2023 Jan;89(1):330-339. doi: 10.1111/bcp.15496. Epub 2022 Aug 30.
nlmixr offers first-order conditional estimation (FOCE), FOCE with interaction (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.
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.
The absorption kinetics of metformin depend 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 means of relative mean estimation error (rMEE) ( ) and rRMSE ( ) were 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.
nlmixr is reliable for NLMEM. SAEM was superior to FOCEi in terms of bias and precision, and more robust against initial estimate perturbations.
nlmixr提供一阶条件估计（FOCE）、带交互作用的一阶条件估计（FOCEi）和随机近似期望最大化（SAEM）来拟合非线性混合效应模型（NLMEM）。我们使用nlmixr对二甲双胍的药代动力学数据进行建模，并研究了SAEM和FOCEi在参数估计的偏差和精度方面的表现，以及对初始估计值的稳健性。
拟合房室模型。根据目标函数值和拟合优度图的检查确定最终模型。使用随机模拟和估计比较SAEM和FOCEi之间参数估计的偏差和精度。为了评估稳健性，在初始估计值被扰动100次并评估由此产生的变化时，对参数进行重新估计。
二甲双胍的吸收动力学显著依赖于食物状态。在禁食状态下，进入中央房室的一阶吸收之前是进入储存房室的零阶输注，而在进食状态下，进入储存房室的吸收是瞬时的，随后是从储存房室到中央房室的一阶吸收。相对平均估计误差（rMEE）（ ）和相对均方根误差（rRMSE）（ ）的均值分别为0.48和0.35。SAEM给出的所有参数估计值似乎分布较窄，并且接近用于模拟的真实值。相比之下，FOCEi的估计值分布有偏差且偏差更大。当初始估计值被扰动时，FOCEi的估计值偏差更大且更不精确。
nlmixr对于NLMEM是可靠的。在偏差和精度方面，SAEM优于FOCEi，并且对初始估计值的扰动更具稳健性。
