Division of Clinical Pharmacy, Faculty of Pharmaceutical Sciences, Khon Kaen University.
Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Ther Drug Monit. 2020 Dec;42(6):856-865. doi: 10.1097/FTD.0000000000000801.
Vancomycin is widely used to treat gram-positive bacterial infections. However, given significant interpatient variability in its pharmacokinetics, maintaining plasma concentrations is difficult within its characteristically narrow therapeutic window. This is especially challenging in patients with unstable renal function. Thus, the aim of this study was to develop a population pharmacokinetic model for vancomycin that is suitable for Thai patients with variable renal functions, including those with unstable renal function.
Data from 213 patients, including 564 blood samples, were retrospectively collected; approximately 70% patients exhibited unstable renal function during vancomycin treatment. The model building group was randomly assigned 108 patients and the remaining 33 patients comprised the validation group. A population pharmacokinetic model was developed that incorporated drug clearance (CL) as a function of time-varying creatine clearance (CrCL). The predictive ability of the resulting population model was evaluated using the validation data set, including its ability to forecast serum concentrations within a Bayesian feedback algorithm.
A 2-compartment model with drug CL values that changed with time-varying CrCL adequately described vancomycin pharmacokinetics in the evaluated heterogeneous patient population with unstable renal function. Vancomycin CL was related to time-varying CrCL as follows: CL (t) = 0.11 + 0.021 × CrCL (t) (CrCL <120 mL/min. Using the population model, Bayesian estimation with at least one measured serum concentration resulted in a forecasting error of small bias (-2.4%) and adequate precision (31.5%).
In hospitals with a high incidence of unstable renal function, incorporating time-varying CrCL with Bayesian estimation and at least one measured drug concentration, along with frequent CrCL monitoring, improves the predictive performance of therapeutic drug monitoring of vancomycin.
万古霉素被广泛用于治疗革兰氏阳性菌感染。然而，由于其药代动力学在患者间存在显著的变异性，因此要使其在狭窄的治疗窗内维持稳定的血药浓度较为困难。这在肾功能不稳定的患者中尤其具有挑战性。因此，本研究旨在建立一个适合肾功能变化的泰国患者（包括肾功能不稳定的患者）的万古霉素群体药代动力学模型。
回顾性收集了 213 例患者（共 564 份血样）的数据，其中约 70%的患者在万古霉素治疗期间肾功能不稳定。模型建立组随机分配 108 例患者，其余 33 例患者为验证组。建立了一个群体药代动力学模型，将药物清除率（CL）作为时间变化的肌酐清除率（CrCL）的函数。使用验证数据集评估了所得群体模型的预测能力，包括其在贝叶斯反馈算法内预测血清浓度的能力。
一个 2 室模型，其中药物 CL 值随时间变化的 CrCL 而变化，能够很好地描述肾功能不稳定的异质患者群体中的万古霉素药代动力学。万古霉素 CL 与时间变化的 CrCL 相关，如下所示：CL（t）=0.11+0.021×CrCL（t）（CrCL<120mL/min）。使用群体模型，至少有一个测量的血清浓度进行贝叶斯估计，预测误差为小偏差（-2.4%）和足够的精度（31.5%）。
在肾功能不稳定发生率较高的医院中，结合时间变化的 CrCL、贝叶斯估计和至少一个测量的药物浓度，并频繁监测 CrCL，可以提高万古霉素治疗药物监测的预测性能。
