The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10-4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world's deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

中文翻译：

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转录组学特征预测药物协同作用的调节因子和临床治疗方案对结核病的疗效。

多重耐药菌株的迅速传播导致迫切需要新的药物治疗方案来治疗结核病 (TB)，结核病每年导致 180 万人死亡。由于病原体结核分枝杆菌 (MTB) 生长缓慢，加上大量可能的药物组合，确定新的治疗方案一直具有挑战性。在这里，我们提出了一个计算模型 (INDIGO-MTB)，该模型在使用 MTB 药物转录组学概况在计算机中筛选了超过 100 万种潜在药物组合后，确定了具有现有和新兴抗结核药物的协同方案。INDIGO-MTB 进一步预测 Rv1353c 基因是多种药物相互作用的关键转录调节因子，我们通过实验证实 Rv1353c 的上调降低了贝达喹啉-链霉素组合的拮抗作用。对使用 INDIGO-MTB 的 57 项结核病治疗方案的临床试验的回顾性分析显示，根据治疗 8 周后痰培养阴性患者的百分比，协同组合比拮抗组合更有效（P 值 = 1 × 10-4） . 我们的研究建立了一个快速评估结核病药物组合的框架，也适用于其他细菌病原体。重要性多药联合治疗是治疗结核病这一世界上最致命的细菌感染的重要策略。较长的治疗时间和不断增长的耐药率导致迫切需要新的方法来优先考虑有效的药物治疗方案。因此，我们开发了一个名为 INDIGO-MTB 的计算模型，该模型使用由单个药物引发的病原体反应转录组，从大量可能的药物组合中识别协同药物方案。尽管 INDIGO-MTB 的基础输入数据是在体外肉汤培养条件下生成的，但 INDIGO-MTB 的预测与临床试验的体内药物治疗效果显着相关。INDIGO-MTB 还将转录因子 Rv1353c 鉴定为多种药物相互作用结果的调节因子，可以作为合理增强药物协同作用的目标。INDIGO-MTB 的预测与临床试验的体内药物治疗效果显着相关。INDIGO-MTB 还将转录因子 Rv1353c 鉴定为多种药物相互作用结果的调节因子，可以作为合理增强药物协同作用的目标。INDIGO-MTB 的预测与临床试验的体内药物治疗效果显着相关。INDIGO-MTB 还将转录因子 Rv1353c 鉴定为多种药物相互作用结果的调节因子，可以作为合理增强药物协同作用的目标。