Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Korea.
Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Korea.
J Korean Med Sci. 2024 Apr 8;39(13):e104. doi: 10.3346/jkms.2024.39.e104.
The hollow-fiber infection model (HFIM) is a valuable tool for evaluating pharmacokinetics/pharmacodynamics relationships and determining the optimal antibiotic dose in monotherapy or combination therapy, but the application for personalized precision medicine in tuberculosis treatment remains limited. This study aimed to evaluate the efficacy of adjusted antibiotic doses for a tuberculosis patient using HFIM.
Model-based Bayesian forecasting was utilized to assess the proposed reduction of the isoniazid dose from 300 mg daily to 150 mg daily in a patient with an ultra-slow-acetylation phenotype. The efficacy of the adjusted 150-mg dose was evaluated in a time-to-kill assay performed using the bacterial isolate () H37Ra in a HFIM that mimicked the individual pharmacokinetic profile of the patient.
The isoniazid concentration observed in the HFIM adequately reflected the target drug exposures simulated by the model. After 7 days of repeated dose administration, isoniazid killed 4 log CFU/mL in the treatment arm, while the control arm without isoniazid increased 1.6 log CFU/mL.
Our results provide an example of the utility of the HFIM for predicting the efficacy of specific recommended doses of anti-tuberculosis drugs in real clinical setting.
中空纤维感染模型（HFIM）是评估药代动力学/药效学关系和确定单药或联合治疗最佳抗生素剂量的有价值工具，但在结核病治疗的个性化精准医学中的应用仍然有限。本研究旨在评估 HFIM 用于调整结核病患者抗生素剂量的疗效。
利用基于模型的贝叶斯预测来评估将超慢乙酰化表型患者的异烟肼剂量从每日 300mg 减少至 150mg 的方案。使用中空纤维感染模型（该模型模拟了患者的个体药代动力学特征）中的细菌分离株 （）H37Ra 进行的杀伤时间测定评估了调整后的 150mg 剂量的疗效。
HFIM 中观察到的异烟肼浓度充分反映了模型模拟的目标药物暴露。经过 7 天的重复剂量给药，异烟肼在治疗组中杀死了 4 对数 CFU/mL，而没有异烟肼的对照组增加了 1.6 对数 CFU/mL。
我们的结果提供了一个实例，说明中空纤维感染模型可用于预测特定推荐剂量的抗结核药物在真实临床环境中的疗效。
