Metabolomics-Driven Exploration of the Chemical Drug Space to Predict Combination Antimicrobial Therapies
Metabolomics-Driven Exploration of the Chemical Drug Space to Predict Combination Antimicrobial Therapies
复制标题
DOI：
10.1016/j.molcel.2019.04.001
复制
发表时间：
2019-06-20
期刊：
影响因子：
16
通讯作者：
Zampieri, Mattia
中科院分区：
文献类型：
--
作者：
Campos, Adrian I.;Zampieri, Mattia
替代传统的单一靶点、单一化合物治疗方法，联合疗法可以为对抗抗生素耐药性开辟全新的机会。然而，组合的复杂性阻止了大规模的药物组合的实验测试，并且合理设计组合疗法的方法落后。在这里，我们开发了一种结合实验-计算的方法，使用高通量代谢组学来预测药物-药物相互作用。该方法在1，279种用于革兰氏阴性细菌大肠杆菌的不同药物上进行了测试。将我们的药物反应代谢谱与先前生成的3，807个单基因缺失菌株的代谢和化学基因组学谱相结合，揭示了一个意想不到的大空间的抑制基因功能，并使药物组合的合理设计成为可能。这种方法适用于其他治疗领域，可以揭示对药物耐受性，副作用和再利用的前所未有的见解。药物相关代谢组概况简编可在https：//zampierigroup.shinyapps.io/EcoPrestMet上查阅，为微生物学和药理学界提供了宝贵的资源。
Alternative to the conventional search for single-target, single-compound treatments, combination therapies can open entirely new opportunities to fight antibiotic resistance. However, combinatorial complexity prohibits experimental testing of drug combinations on a large scale, and methods to rationally design combination therapies are lagging behind. Here, we developed a combined experimental-computational approach to predict drug-drug interactions using high-throughput metabolomics. The approach was tested on 1,279 pharmacologically diverse drugs applied to the gram-negative bacterium Escherichia coli. Combining our metabolic profiling of drug response with previously generated metabolic and chemogenomic profiles of 3,807 single-gene deletion strains revealed an unexpectedly large space of inhibited gene functions and enabled rational design of drug combinations. This approach is applicable to other therapeutic areas and can unveil unprecedented insights into drug tolerance, side effects, and repurposing. The compendium of drug-associated metabolome profiles is available at https://zampierigroup.shinyapps.io/EcoPrestMet, providing a valuable resource for the microbiological and pharmacological communities.