Luo Xiangru, Wang Shiyi, Li Dong, Wen Jun, Sun Na, Fan Guangjun
Department of Pharmacy, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
Front Pharmacol. 2023 Mar 13;14:1083464. doi: 10.3389/fphar.2023.1083464. eCollection 2023.
In critically ill patients, the change of pathophysiological status may affect the pharmacokinetic (PK) process of drugs. The purpose of this study was to develop a PK model for tigecycline in critically ill patients, identify the factors influencing the PK and optimiz dosing regimens. The concentration of tigecycline was measured LC-MS/MS. We established population PK model with the non-linear mixed effect model and optimized the dosing regimens by Monte Carlo simulation. A total of 143 blood samples from 54 patients were adequately described by a one-compartment linear model with first-order elimination. In the covariate screening analysis, the APACHEII score and age as significant covariates. The population-typical values of CL and Vd in the final model were 11.30 ± 3.54 L/h and 105.00 ± 4.47 L, respectively. The PTA value of the standard dose regimen (100 mg loading dose followed by a 50 mg maintenance dose at q12 h) was 40.96% with an MIC of 2 mg/L in patients with HAP, the ideal effect can be achieved by increasing the dosage. No dose adjustment was needed for for AUC0-24/MIC targets of 4.5 and 6.96, and the three dose regimens almost all reached 90%. A target AUC0-24/MIC of ≥17.9 reached 100% in patients with cSSSI in the three tigecycline dose regimens, considering MIC ≤ 0.25 mg/L. The final model indicated that APACHEII score and age could affect the Cl and Vd of tigecycline, respectively. The standard dose regimen of tigecycline was often not able to obtain satisfactory therapeutic effects for critically ill patients. For patients with HAP and cIAI caused by one of three pathogens, the efficacy rate can be improved by increasing the dose, but for cSSSI infections caused by and , it is recommended to change the drug or use a combination of drugs.
在重症患者中，病理生理状态的变化可能会影响药物的药代动力学（PK）过程。本研究的目的是建立重症患者替加环素的PK模型，确定影响PK的因素并优化给药方案。采用液相色谱-串联质谱法（LC-MS/MS）测定替加环素浓度。我们用非线性混合效应模型建立群体PK模型，并通过蒙特卡洛模拟优化给药方案。采用一室线性一级消除模型能很好地拟合54例患者的143份血样。在协变量筛选分析中，APACHEII评分和年龄为显著协变量。最终模型中CL和Vd的群体典型值分别为11.30±3.54 L/h和105.00±4.47 L。在HAP患者中，标准剂量方案（100 mg负荷剂量，随后每12小时50 mg维持剂量）在MIC为2 mg/L时的达标概率（PTA）值为40.96%，增加剂量可达到理想疗效。对于AUC0-24/MIC目标值为4.5和6.96时无需调整剂量，三种给药方案几乎均达到90%。考虑到MIC≤0.25 mg/L，在三种替加环素给药方案中，cSSSI患者达到AUC0-24/MIC≥17.9的目标达标率为100%。最终模型表明，APACHEII评分和年龄分别可影响替加环素的清除率（Cl）和分布容积（Vd）。替加环素的标准剂量方案对重症患者往往无法获得满意的治疗效果。对于由三种病原体之一引起的HAP和cIAI患者，增加剂量可提高有效率，但对于由[具体病原体1]和[具体病原体2]引起的cSSSI感染，建议更换药物或联合用药。
