Division of Infectious Diseases, Department of Medicine, University of California, Los Angeles, California 90095, USA.
Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, California 90095, USA.
Nat Commun. 2017 Jan 24;8:14183. doi: 10.1038/ncomms14183.
The current drug regimens for treating tuberculosis are lengthy and onerous, and hence complicated by poor adherence leading to drug resistance and disease relapse. Previously, using an output-driven optimization platform and an in vitro macrophage model of Mycobacterium tuberculosis infection, we identified several experimental drug regimens among billions of possible drug-dose combinations that outperform the current standard regimen. Here we use this platform to optimize the in vivo drug doses of two of these regimens in a mouse model of pulmonary tuberculosis. The experimental regimens kill M. tuberculosis much more rapidly than the standard regimen and reduce treatment time to relapse-free cure by 75%. Thus, these regimens have the potential to provide a markedly shorter course of treatment for tuberculosis in humans. As these regimens omit isoniazid, rifampicin, fluoroquinolones and injectable aminoglycosides, they would be suitable for treating many cases of multidrug and extensively drug-resistant tuberculosis.
目前治疗结核病的药物方案冗长而繁重，因此由于依从性差导致耐药性和疾病复发，情况变得复杂。此前，我们使用一个基于输出的优化平台和一个体外结核分枝杆菌感染的巨噬细胞模型，在数十亿种可能的药物剂量组合中确定了几种实验性药物方案，这些方案优于当前的标准方案。在这里，我们使用这个平台在肺结核的小鼠模型中优化这两种方案中的两种方案的体内药物剂量。实验方案比标准方案更快地杀死结核分枝杆菌，并将无复发治愈的治疗时间缩短 75%。因此，这些方案有可能为人类结核病提供明显更短的治疗疗程。由于这些方案不包括异烟肼、利福平、氟喹诺酮类药物和注射用氨基糖苷类药物，因此它们适用于治疗许多耐多药和广泛耐药结核病病例。
