Department of Pharmacy, Anhanguera University, Av. Dr. João Batista de Souza Soares, 4009 - Cidade Morumbi, São José dos Campos, SP, 12236-660, Brazil.
Divisão de Engenharia Aeroespacial, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, Brazil.
J Pharmacokinet Pharmacodyn. 2023 Feb;50(1):11-20. doi: 10.1007/s10928-022-09831-x. Epub 2022 Nov 2.
Colistin remains one of the few available options for the treatment of infections caused by resistant bacteria. Pharmacokinetic (PK) studies have been successful in estimating the appropriate colistin methanesulfonate (CMS) dose to achieve a target colistin concentration. Currently, there is a consensus that the dose of CMS should vary according to the patient renal function since CMS is mainly eliminated by renal route. For this same reason, the loading dose should vary according to the patient's renal capacity; however, this is not the current clinical practice. In this study we develop a framework to determine two key parameters for the loading dose regimen: (1) the optimal dose according to the characteristics (renal function and weight) of the patient; (2) the waiting time before the maintenance dose. Based on a previous PK model, our framework allows a fast parameter sweep so as to select optimal loading dose and waiting time minimizing the deviation between the plasma concentration and a target value. The results showed that patients presenting low creatinine clearance (CrCL) should receive a lower CMS loading dose with longer interval to start maintenance treatment to avoid nephrotoxic colistin concentrations. In cases of high CrCL, the dose should be higher and the interval to the next dose shorter to avoid subtherapeutic concentrations. Optimization of the loading dose should considerably improve colistin therapy, as the target concentration is reached more quickly, without reaching toxic values.
黏菌素仍然是治疗耐药菌感染的少数有效选择之一。药代动力学（PK）研究成功地估计了实现目标黏菌素浓度的适当黏菌素甲磺酸盐（CMS）剂量。目前，共识认为 CMS 剂量应根据患者肾功能而变化，因为 CMS 主要通过肾脏途径消除。出于同样的原因，负荷剂量应根据患者的肾功能而变化；然而，这不是当前的临床实践。在这项研究中，我们开发了一个框架来确定负荷剂量方案的两个关键参数：（1）根据患者的特征（肾功能和体重）确定的最佳剂量；（2）开始维持剂量之前的等待时间。基于先前的 PK 模型，我们的框架允许快速参数扫描，以便选择最佳的负荷剂量和等待时间，将血浆浓度与目标值之间的偏差最小化。结果表明，肌酐清除率（CrCL）较低的患者应接受较低的 CMS 负荷剂量，并延长开始维持治疗的间隔时间，以避免黏菌素浓度的肾毒性。在 CrCL 较高的情况下，应增加剂量并缩短下一次剂量的间隔时间，以避免治疗浓度不足。负荷剂量的优化可以极大地改善黏菌素治疗，因为目标浓度更快地达到，而不会达到毒性值。
