Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Centre for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Centre for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China; Department of Tuberculosis, Affiliated Hospital of Zunyi Medical University, Guizhou, China.
Eur J Pharm Sci. 2024 Dec 1;203:106915. doi: 10.1016/j.ejps.2024.106915. Epub 2024 Sep 26.
Population pharmacokinetic (popPK) models can optimise linezolid dosage regimens in patients with multidrug-resistant tuberculosis (MDR-TB); however, unknown cross-centre precision and poor adherence remain problematic. This study aimed to assess the predictive ability of published models and use the most suitable model to optimise dosage regimens and manage compliance.
One hundred fifty-eight linezolid plasma concentrations from 27 patients with MDR-TB were used to assess the predictive performance of published models. Prediction-based metrics and simulation-based visual predictive checks were conducted to evaluate predictive ability. Individualised remedial dosing regimens for various delayed scenarios were optimised using the most suitable model and Monte Carlo simulations. The influence of covariates, scheduled dosing intervals, and patient compliance were assessed.
Seven popPK models were identified. Body weight and creatinine clearance were the most frequently identified covariates influencing linezolid clearance. The model with the best performance had a median prediction error (PE%) of -1.62 %, median absolute PE of 29.50 %, and percentages of PE within 20 % (F, 36.97 %) and 30 % (F, 51.26 %). Monte Carlo simulations indicated that a twice-daily 300 mg linezolid dose may be more efficient than 600 mg once daily. For the 'typical' patient treated with 300 mg twice daily, half the dosage should be taken after a delay of ≥ 3 h.
Monte Carlo simulations based on popPK models can propose remedial regimens for delayed doses of linezolid in patients with MDR-TB. Model-based compliance management patterns are useful for balancing efficacy, adverse reactions, and resistance suppression.
群体药代动力学（popPK）模型可优化耐多药肺结核（MDR-TB）患者的利奈唑胺剂量方案；然而，未知的中心间精度和较差的依从性仍然是问题。本研究旨在评估已发表模型的预测能力，并使用最合适的模型来优化剂量方案和管理依从性。
使用 27 例 MDR-TB 患者的 158 个利奈唑胺血浆浓度来评估已发表模型的预测性能。进行基于预测的指标和基于模拟的可视化预测检查，以评估预测能力。使用最合适的模型和蒙特卡罗模拟优化各种延迟情况下的个体化补救剂量方案。评估了协变量、计划的给药间隔和患者依从性的影响。
确定了 7 个 popPK 模型。体重和肌酐清除率是最常被识别的影响利奈唑胺清除率的协变量。表现最好的模型的中位数预测误差（PE%）为-1.62%，中位数绝对 PE 为 29.50%，PE 在 20%（F，36.97%）和 30%（F，51.26%）范围内的百分比。蒙特卡罗模拟表明，每日两次 300mg 利奈唑胺剂量可能比每日一次 600mg 更有效。对于接受 300mg 每日两次治疗的“典型”患者，如果延迟≥3 小时后，应将剂量的一半服用。
基于 popPK 模型的蒙特卡罗模拟可以为 MDR-TB 患者延迟利奈唑胺剂量提出补救方案。基于模型的依从性管理模式对于平衡疗效、不良反应和耐药性抑制非常有用。
