University at Buffalo, Buffalo, New York, USA.
Institute for Clinical Pharmacodynamics, Schenectady, New York, USA.
Antimicrob Agents Chemother. 2018 Jun 26;62(7). doi: 10.1128/AAC.00483-18. Print 2018 Jul.
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 AUC (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 AUC 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.
黏菌素 B 被用作治疗多重耐药革兰氏阴性菌感染患者的最后手段抗生素；然而，它有很大的肾毒性风险。在此，我们提出了一个基于目标浓度-时间曲线下面积（AUC）值和自适应反馈控制算法（算法）的黏菌素 B 治疗窗，该算法允许对黏菌素 B 的剂量进行个性化调整。该治疗窗的上限通过对黏菌素 B 肾毒性数据的药代动力学 meta 分析确定，下限则来自于鼠大腿感染药代动力学（PK）/药效动力学（PD）研究。先前开发的黏菌素 B 群体药代动力学模型被用作算法的基础。通过使用不同的稀疏 PK 采样策略进行 Monte Carlo 模拟（MCS），评估算法的性能。肾毒性 meta 分析的结果表明，肾毒性发生率与黏菌素 B 暴露显著相关。基于这项分析和先前报道的鼠 PK/PD 研究，确定目标 AUC（0 至 24 小时 AUC）窗为 50 至 100mg·h/L。MCS 表明，在没有自适应反馈控制的标准黏菌素 B 给药情况下，只有 71%的模拟受试者达到了该窗内的 AUC 值。使用在 24 小时采集的单个 PK 样本和算法，可以计算出个性化的给药方案，从而使超过 95%的模拟受试者达到目标窗内的 AUC 值。当使用更多样本时，目标达成率进一步提高。我们的算法通过使用最少一个药代动力学样本来提高目标达成率，并在脆弱的患者群体中实现精确的个体化给药。
