对肝脏和/或肾脏功能障碍患者体内多粘菌素 B 的群体药代动力学模型进行系统评估

IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-04-16 DOI:10.1007/s10928-024-09916-9
Xueyong Li, Yu Cheng, Bingqing Zhang, Bo Chen, Yiying Chen, Yingbing Huang, Hailing Lin, Lili Zhou, Hui Zhang, Maobai Liu, Wancai Que, Hongqiang Qiu
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摘要

多粘菌素 B(PMB)被认为是治疗耐多药(MDR)革兰氏阴性菌感染的最后一线药物。利用群体药代动力学(PopPK)模型进行精准给药有助于实现多粘菌素 B 给药方案的个体化并改善治疗效果。然而,现有 PopPK 模型的外部预测能力尚未得到充分阐述。本研究旨在以新的独立人群为基础,系统评估十篇已发表文献中的 11 个 PMB PopPK 模型,并将其分为肝功能异常患者、肾功能异常患者、肝肾功能异常患者和肝肾功能正常患者四个不同人群。整个数据集由 146 名患者和 391 个 PMB 浓度组成。为了评估模型的可预测性,进行了基于预测和模拟的诊断以及贝叶斯预测。在整个评估过程中,没有一个模型同时在预测诊断和模拟诊断中表现出令人满意的预测能力。然而,在对肝肾功能正常的亚组患者进行评估时发现,与肝肾功能障碍患者相比,模型的预测性能有所提高。贝叶斯预测法在纳入两到三个先验观察结果后显示出更强的可预测性。外部评估强调了已发布模型的预测结果与外部验证数据集之间缺乏一致性。不过,贝叶斯预测法有望提高模型的预测性能,而治疗药物监测的反馈对于优化个体用药方案至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A systematic evaluation of population pharmacokinetic models for polymyxin B in patients with liver and/or kidney dysfunction

Polymyxin B (PMB) is considered a last-line treatment for multidrug-resistant (MDR) gram-negative bacterial infections. Model-informed precision dosing with population pharmacokinetics (PopPK) models could help to individualize PMB dosing regimens and improve therapy. However, the external prediction ability of the established PopPK models has not been fully elaborated. This study aimed to systemically evaluate eleven PMB PopPK models from ten published literature based on a new independent population, which was divided into four different populations, patients with liver dysfunction, kidney dysfunction, liver and kidney dysfunction, and normal liver and kidney function. The whole data set consisted of 146 patients with 391 PMB concentrations. The prediction- and simulation-based diagnostics and Bayesian forecasting were conducted to evaluate model predictability. In the overall evaluation process, none of the models exhibited satisfactory predictive ability in both prediction- and simulation-based diagnostic simultaneously. However, the evaluation of the models in the subgroup of patients with normal liver and kidney function revealed improved predictive performance compared to those with liver and/or kidney dysfunction. Bayesian forecasting demonstrated enhanced predictability with the incorporation of two to three prior observations. The external evaluation highlighted a lack of consistency between the prediction results of published models and the external validation dataset. Nonetheless, Bayesian forecasting holds promise in improving the predictive performance of the models, and feedback from therapeutic drug monitoring is crucial in optimizing individual dosing regimens.

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来源期刊
CiteScore
4.90
自引率
4.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Broadly speaking, the Journal of Pharmacokinetics and Pharmacodynamics covers the area of pharmacometrics. The journal is devoted to illustrating the importance of pharmacokinetics, pharmacodynamics, and pharmacometrics in drug development, clinical care, and the understanding of drug action. The journal publishes on a variety of topics related to pharmacometrics, including, but not limited to, clinical, experimental, and theoretical papers examining the kinetics of drug disposition and effects of drug action in humans, animals, in vitro, or in silico; modeling and simulation methodology, including optimal design; precision medicine; systems pharmacology; and mathematical pharmacology (including computational biology, bioengineering, and biophysics related to pharmacology, pharmacokinetics, orpharmacodynamics). Clinical papers that include population pharmacokinetic-pharmacodynamic relationships are welcome. The journal actively invites and promotes up-and-coming areas of pharmacometric research, such as real-world evidence, quality of life analyses, and artificial intelligence. The Journal of Pharmacokinetics and Pharmacodynamics is an official journal of the International Society of Pharmacometrics.
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