Personalizing Polymyxin B Dosing Using an Adaptive Feedback Control Algorithm.
Personalizing Polymyxin B Dosing Using an Adaptive Feedback Control Algorithm.
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DOI：
10.1128/aac.00483-18
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发表时间：
2018-07
影响因子：
4.9
通讯作者：
Forrest A
中科院分区：
文献类型：
--
作者：
Lakota EA;Landersdorfer CB;Nation RL;Li J;Kaye KS;Rao GG;Forrest A
多粘菌素B被用作多重耐药革兰氏阴性菌感染患者的最后一种抗生素;然而，它具有显著的肾毒性风险。本文中，我们提出了基于浓度-时间曲线下目标面积（AUC）值的多粘菌素B治疗窗口和允许多粘菌素B给药个性化的自适应反馈控制算法（算法）。该治疗窗的上限通过多粘菌素B肾毒性数据的药物计量学荟萃分析确定，下限来自小鼠大腿感染药代动力学（PK）/药效学（PD）研究。先前开发的多粘菌素B群体药代动力学模型用作算法的主干。进行蒙特卡罗模拟（MCS），以评价使用不同稀疏PK采样策略的算法性能。肾毒性荟萃分析结果显示，肾毒性发生率与多粘菌素B暴露显著相关。基于该分析和先前报告的鼠PK/PD研究，确定目标AUC 0 -24（0 - 24 h AUC）窗口为50 - 100 mg · h/L。MCS显示，在没有自适应反馈控制的情况下，使用标准多粘菌素B给药，只有71%的模拟受试者在该窗口内达到AUC值。使用在24小时收集的单个PK样本和算法，可以计算个性化给药方案，这导致>95%的模拟受试者在目标窗口内达到AUC 0 -24值。当使用更多的样本时，目标实现率进一步提高。我们的算法通过使用少至一个药代动力学样本来增加达到目标的概率，并在脆弱的患者群体中实现精确的个性化给药。
Polymyxin B is used as an antibiotic of last resort for patients with multidrug-resistant Gram-negative bacterial infections; however, it carries a significant risk of nephrotoxicity. Herein we present a polymyxin B therapeutic window based on target area under the concentration-time curve (AUC) values and an adaptive feedback control algorithm (algorithm) which allows for the personalization of polymyxin B dosing. The upper bound of this therapeutic window was determined through a pharmacometric meta-analysis of polymyxin B nephrotoxicity data, and the lower bound was derived from murine thigh infection pharmacokinetic (PK)/pharmacodynamic (PD) studies. A previously developed polymyxin B population pharmacokinetic model was used as the backbone for the algorithm. Monte Carlo simulations (MCS) were performed to evaluate the performance of the algorithm using different sparse PK sampling strategies. The results of the nephrotoxicity meta-analysis showed that nephrotoxicity rate was significantly correlated with polymyxin B exposure. Based on this analysis and previously reported murine PK/PD studies, the target AUC0–24 (AUC from 0 to 24 h) window was determined to be 50 to 100 mg · h/liter. MCS showed that with standard polymyxin B dosing without adaptive feedback control, only 71% of simulated subjects achieved AUC values within this window. Using a single PK sample collected at 24 h and the algorithm, personalized dosing regimens could be computed, which resulted in >95% of simulated subjects achieving AUC0–24 values within the target window. Target attainment further increased when more samples were used. Our algorithm increases the probability of target attainment by using as few as one pharmacokinetic sample and enables precise, personalized dosing in a vulnerable patient population.