Supplementary file 1_Population pharmacokinetic analysis of remimazolam after continuous infusion for sedation in critically ill patients.docx

IntroductionThe aim of the present prospective study was to model the population pharmacokinetics of remimazolam after continuous infusion in critically ill patients, and to provide a guide for remimazolam administration based on simulations that were conducted.
Patients and methodsA total of 32 critically ill patients were enrolled in this study, with 236 plasma concentration data ultimately included for modeling. Plasma concentrations of remimazolam were quantified by a validated high-performance liquid chromatography-tandem mass spectrometry method, and the data were analyzed using non-linear mixed effect modeling. Concentration-time curves of remimazolam at different induction and maintenance doses were simulated and context-sensitive decrement times (CSDTs) were calculated using Monte Carlo simulations.
ResultsA two-compartment model appropriately described the concentration-time profile of remimazolam in critically ill patients. The elimination clearance, volume of the central compartment, volume of the peripheral compartment, and peripheral compartmental clearance were estimated to be 58.2 L/h (95% CI, 47.8–72.3 L/h), 25.5 L (95% CI, 16.8–33.3 L), 34.5 L (95% CI, 26.0–58.8 L) and 21.9 L/h (95% CI, 12.2–34.6 L/h), respectively. No covariates significantly influenced the pharmacokinetic parameters of remimazolam. Internal validation proved the reliable predictive performance of the model. The CSDTs of remimazolam (10%–90%) was independent of the infusion time.
ConclusionRemimazolam showed a predictable pharmacokinetic profile and was demonstrated to be suitable for long-term sedation in the intensive care unit, with dose adjustments only required dependent on the degree of the sedative effect.

引言
本前瞻性研究旨在对重症患者持续输注瑞马唑仑后的群体药代动力学（population pharmacokinetics）进行建模，并基于所开展的模拟研究为瑞马唑仑的临床给药提供指导。

患者与方法
本研究共纳入32例重症患者，最终纳入236份血浆浓度数据用于建模。采用经过验证的高效液相色谱-串联质谱法（high-performance liquid chromatography-tandem mass spectrometry）对瑞马唑仑的血浆浓度进行定量分析，并使用非线性混合效应模型（non-linear mixed effect modeling）对数据进行分析。模拟不同诱导及维持剂量下瑞马唑仑的浓度-时间曲线，并通过蒙特卡洛模拟（Monte Carlo simulations）计算情境敏感递减时间（context-sensitive decrement times, CSDTs）。

结果
二室模型可较好地描述重症患者体内瑞马唑仑的浓度-时间特征。研究估算得到清除率、中央室容积、外周室容积及外周室清除率分别为58.2 L/h（95%置信区间，47.8~72.3 L/h）、25.5 L（95%置信区间，16.8~33.3 L）、34.5 L（95%置信区间，26.0~58.8 L）及21.9 L/h（95%置信区间，12.2~34.6 L/h）。未发现有协变量对瑞马唑仑的药代动力学参数产生显著影响。内部验证结果表明该模型具有可靠的预测性能。瑞马唑仑（10%~90%）的CSDTs与输注时长无关。

结论
瑞马唑仑具有可预测的药代动力学特征，且被证实适用于重症监护病房（intensive care unit）内的长期镇静治疗，仅需根据镇静效应的程度调整给药剂量。