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评估耐多药和广泛耐药结核病患者利奈唑胺暴露的简单策略。

Simple strategy to assess linezolid exposure in patients with multi-drug-resistant and extensively-drug-resistant tuberculosis.

机构信息

University of Groningen, University Medical Centre Groningen, Department of Clinical Pharmacy and Pharmacology, Groningen, The Netherlands.

Division of Infection, Barts Health NHS Trust, London, UK; E. Morelli Hospital AOVV, Reference Centre for MDR-TB and HIV-TB, Sondalo, Italy.

出版信息

Int J Antimicrob Agents. 2017 Jun;49(6):688-694. doi: 10.1016/j.ijantimicag.2017.01.017. Epub 2017 Apr 4.

Abstract

Linezolid is used increasingly for the treatment of multi-drug-resistant (MDR) and extensively-drug-resistant (XDR) tuberculosis (TB). However, linezolid can cause severe adverse events, such as peripheral and optical neuropathy or thrombocytopenia related to higher drug exposure. This study aimed to develop a population pharmacokinetic model to predict the area under the concentration curve (AUC) for linezolid using a limited number of blood samples. Data from patients with MDR-/XDR-TB who received linezolid and therapeutic drug monitoring as part of their TB treatment were used. Mw\Pharm 3.82 (Mediware, Zuidhorn, The Netherlands) was used to develop a population pharmacokinetic model and limited sampling strategy (LSS) for linezolid. LSS was evaluated over a time span of 6 h. Blood sampling directly before linezolid administration and 2 h after linezolid administration were considered to be the most clinically relevant sampling points. The model and LSS were evaluated by analysing the correlation between AUC and AUC. In addition, LSS was validated with an external group of patients with MDR-/XDR-TB from Sondalo, Italy. Fifty-two pharmacokinetic profiles were used to develop the model. Thirty-three profiles with a 300 mg dosing regimen and 19 profiles with a 600 mg dosing regimen were obtained. Model validation showed prediction bias of 0.1% and r of 0.99. Evaluation of the most clinically relevant LSS showed prediction bias of 4.8% and r of 0.97. The root mean square error corresponding to the most relevant LSS was 6.07%. The developed LSS could be used to enable concentration-guided dosing of linezolid.

摘要

利奈唑胺越来越多地用于治疗耐多药(MDR)和广泛耐药(XDR)结核病(TB)。然而,利奈唑胺可引起严重的不良反应,如周围和视神经病变或血小板减少症,与更高的药物暴露有关。本研究旨在开发一种群体药代动力学模型,以使用有限数量的血样预测利奈唑胺的浓度-时间曲线下面积(AUC)。该研究使用了接受利奈唑胺治疗且作为其结核病治疗一部分进行了治疗药物监测的 MDR-/XDR-TB 患者的数据。Mw\Pharm 3.82(Mediware,荷兰 Zuidhorn)用于开发利奈唑胺的群体药代动力学模型和有限采样策略(LSS)。LSS 在 6 小时的时间跨度内进行评估。直接在利奈唑胺给药前和给药后 2 小时采血被认为是最具临床相关性的采样点。通过分析 AUC 与 AUC 的相关性来评估模型和 LSS。此外,使用来自意大利松多洛的 MDR-/XDR-TB 患者的外部组来验证 LSS。该模型使用 52 个药代动力学曲线进行开发。获得了 33 个 300mg 剂量方案和 19 个 600mg 剂量方案的曲线。模型验证显示预测偏差为 0.1%,r 为 0.99。对最具临床相关性 LSS 的评估显示预测偏差为 4.8%,r 为 0.97。最相关 LSS 的均方根误差为 6.07%。开发的 LSS 可用于指导利奈唑胺的浓度给药。

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