Institute for Therapeutic Innovation, College of Medicine, University of Florida, Orlando, Florida, USA
Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, Florida, USA.
Antimicrob Agents Chemother. 2021 Jan 20;65(2). doi: 10.1128/AAC.02172-20.
The drug discovery effort has generated a substantial number of new/repurposed drugs for therapy for this pathogen. The arrival of these drugs is welcome, but another layer of difficulty has emerged. Single agent therapy is insufficient for patients with late-stage tuberculosis because of resistance emergence. To achieve our therapeutic ends, it is requisite to identify optimal combination regimens. These regimens go through a lengthy and expensive evaluative process. If we have a modest group of 6 to 8 new or repurposed agents, this translates into 15 to 28 possible 2-drug combinations. There is neither time nor resources to give an extensive evaluation for all combinations. We sought a screening procedure that would identify combinations that had a high likelihood of achieving good bacterial burden decline. We examined pretomanid, moxifloxacin, linezolid, and bedaquiline in log-phase growth, acid-phase growth, and nonreplicative persister (NRP) phase in the Greco interaction model. We employed the interaction term α and the calculated bacterial burden decline as metrics to rank different regimens in different metabolic states. No relationship was found between α and bacterial kill. We chose bacterial kill as the prime metric. The combination of pretomanid plus moxifloxacin emerged as the clear frontrunner, as the largest bacterial declines were seen in log phase and acid phase with this regimen and it was second best in NRP phase. Bedaquiline also produced good kill. This screening process may identify optimal combinations that can be further evaluated in both the hollow-fiber infection model and in animal models of infection.
该药物发现工作已经产生了大量新的/重新利用的药物，用于治疗这种病原体。这些药物的到来是受欢迎的，但又出现了另一个困难。由于耐药性的出现，单一药物治疗对于晚期结核病患者是不够的。为了达到我们的治疗目的，必须确定最佳的联合治疗方案。这些方案需要经过一个漫长而昂贵的评估过程。如果我们有一个适度的 6 到 8 种新的或重新利用的药物，这将转化为 15 到 28 种可能的 2 种药物组合。既没有时间也没有资源对所有组合进行广泛评估。我们寻求一种筛选程序，可以识别出有很大可能实现良好细菌负荷下降的组合。我们在 Greco 相互作用模型中检查了 pretomanid、莫西沙星、利奈唑胺和贝达喹啉在对数生长期、酸性生长期和非复制性持久期（NRP）的生长情况。我们使用相互作用项α和计算出的细菌负荷下降作为指标，对不同代谢状态下的不同方案进行排名。没有发现α与细菌杀灭之间存在关系。我们选择细菌杀灭作为主要指标。pretomanid 加莫西沙星的组合脱颖而出，成为明显的领跑者，因为该方案在对数期和酸性期的细菌下降幅度最大，在 NRP 期的效果也位居第二。贝达喹啉也产生了良好的杀菌效果。这种筛选过程可以识别出最佳的组合，可以在中空纤维感染模型和感染动物模型中进一步评估。
