O'Jeanson Amaury, Larcher Romaric, Le Souder Cosette, Djebli Nassim, Khier Sonia
Pharmacokinetic Modeling Department, UFR Pharmacie, Montpellier University (School of Pharmacy), 15 Avenue Charles Flahault, 34000, Montpellier, France.
Probabilities and Statistics Department, Institut Montpelliérain Alexander Grothendieck (IMAG), CNRS UMR 5149, Montpellier University, Montpellier, France.
Eur J Drug Metab Pharmacokinet. 2021 Sep;46(5):695-705. doi: 10.1007/s13318-021-00709-w. Epub 2021 Aug 17.
Meropenem is frequently used for the treatment of severe bacterial infections in critically ill patients. Because critically ill patients are more prone to pharmacokinetic variability than other patients, ensuring an effective blood concentration can be complex. Therefore, describing this variability to ensure a proper use of this antibiotic drug limits the rise and dissemination of antimicrobial resistance, and helps preserve the current antibiotic arsenal. The aims of this study were to describe the pharmacokinetics of meropenem in critically ill patients, to identify and quantify the patients' characteristics responsible for the observed pharmacokinetic variability, and to perform different dosing simulations in order to determine optimal individually adapted dosing regimens.
A total of 58 patients hospitalized in the medical intensive care unit and receiving meropenem were enrolled, including 26 patients with renal replacement therapy. A population pharmacokinetic model was developed (using NONMEM software) and Monte Carlo simulations were performed with different dosing scenarios (bolus-like, extended, and continuous infusion) exploring the impact of clinical categories of residual diuresis (anuria, oliguria, and preserved diuresis) on the probability of target attainment (MIC: 1-45 mg/L).
The population pharmacokinetic model included five covariates with a significant impact on clearance: glomerular filtration rate, dialysis (continuous and semi-continuous), renal function status, and volume of residual diuresis. The clearance for a typical patient in our population is 4.20 L/h and volume of distribution approximately 44 L. Performed dosing regimen simulations suggested that, for equivalent doses, the continuous infusion mode (with loading dose) allowed the obtaining of the pharmacokinetic/pharmacodynamic target for a larger number of patients (100% for MIC ≤ 20 mg/L). Nevertheless, for the treatment of susceptible bacteria (MIC ≤ 2 mg/L), differences in the probability of target attainment between bolus-like, extended, and continuous infusions were negligible.
Identified covariates in the model are easily accessible information in patient health records. The model highlighted the importance of considering the patient's overall condition (renal function and dialysis) and the pathogen's characteristics (MIC target) during the establishment of a patient's dosing regimen.
美罗培南常用于治疗重症患者的严重细菌感染。由于重症患者比其他患者更容易出现药代动力学变异性，确保有效的血药浓度可能较为复杂。因此，描述这种变异性以确保正确使用这种抗生素药物，可限制抗菌药物耐药性的上升和传播，并有助于维护现有的抗生素储备。本研究的目的是描述美罗培南在重症患者中的药代动力学，识别并量化导致观察到的药代动力学变异性的患者特征，并进行不同的给药模拟，以确定最佳的个体化给药方案。
共纳入58例入住医学重症监护病房并接受美罗培南治疗的患者，其中26例接受肾脏替代治疗。建立了群体药代动力学模型（使用NONMEM软件），并针对不同的给药方案（推注样、延长输注和持续输注）进行蒙特卡洛模拟，探讨残余尿量的临床分类（无尿、少尿和尿量正常）对达到目标概率（MIC：1 - 45 mg/L）的影响。
群体药代动力学模型包括五个对清除率有显著影响的协变量：肾小球滤过率、透析（持续和半持续）、肾功能状态和残余尿量。我们研究人群中典型患者的清除率为4.20 L/h，分布容积约为44 L。进行的给药方案模拟表明，对于等效剂量，持续输注模式（负荷剂量）能使更多患者达到药代动力学/药效学目标（MIC≤20 mg/L时为100%）。然而，对于治疗敏感菌（MIC≤2 mg/L），推注样、延长输注和持续输注在达到目标概率上的差异可忽略不计。
模型中识别出的协变量在患者健康记录中易于获取。该模型强调了在制定患者给药方案时考虑患者整体状况（肾功能和透析）以及病原体特征（MIC目标）的重要性。
