Pretomanid dose selection for pulmonary tuberculosis: An application of multi-objective optimization to dosage regimen design.
Pretomanid dose selection for pulmonary tuberculosis: An application of multi-objective optimization to dosage regimen design.
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Pretomanid治疗肺结核的剂量选择：多目标优化在剂量方案设计中的应用。
DOI：
10.1002/psp4.12591
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Lyons MA
中科院分区：
文献类型：
--
作者：
Lyons MA
结核病（TB）联合化疗的临床开发因部分或受限的II期剂量发现而变得复杂。障碍包括单药治疗的耐药性倾向、组分剂量组合治疗组数量的实际限制以及当前剂量选择方法在多药方案中的应用有限。开发了一种剂量选择的多目标优化方法，作为目前不断发展的新型TB方案临床试验方法的概念和计算框架。将药代动力学-药效学（PK-PD）建模与进化算法相结合，以确定在多个相互冲突的治疗目标之间产生最佳权衡的给药方案。pretomanid是一种新批准的用于高度耐药肺结核特定病例的硝基咪唑，其IIa期研究用于证明帕累托优化给药方法，该方法可最大限度地减少痰菌负荷和药物相关不良事件的概率。结果包括推荐的200 mg每日一次剂量的人群典型特征、时间依赖性给药的最佳性、个体化治疗的示例以及最佳负荷剂量的确定。该方法将传统的PK-PD目标实现推广到可扩展到药物组合的设计问题，并为复杂药物方案的临床试验提供了获益-风险背景。
Clinical development of combination chemotherapies for tuberculosis (TB) is complicated by partial or restricted phase II dose‐finding. Barriers include a propensity for drug resistance with monotherapy, practical limits on numbers of treatment arms for component dose combinations, and limited application of current dose selection methods to multidrug regimens. A multi‐objective optimization approach to dose selection was developed as a conceptual and computational framework for currently evolving approaches to clinical testing of novel TB regimens. Pharmacokinetic‐pharmacodynamic (PK‐PD) modeling was combined with an evolutionary algorithm to identify dosage regimens that yield optimal trade‐offs between multiple conflicting therapeutic objectives. The phase IIa studies for pretomanid, a newly approved nitroimidazole for specific cases of highly drug‐resistant pulmonary TB, were used to demonstrate the approach with Pareto optimized dosing that best minimized sputum bacillary load and the probability of drug‐related adverse events. Results include a population‐typical characterization of the recommended 200 mg once daily dosage, the optimality of time‐dependent dosing, examples of individualized therapy, and the determination of optimal loading doses. The approach generalizes conventional PK‐PD target attainment to a design problem that scales to drug combinations, and provides a benefit‐risk context for clinical testing of complex drug regimens.
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