Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis.
Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis.
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DOI：
10.1016/j.cels.2021.08.004
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发表时间：
2021-11-17
期刊：
影响因子：
9.3
通讯作者：
Aldridge BB
中科院分区：
文献类型：
--
作者：
Larkins-Ford J;Greenstein T;Van N;Degefu YN;Olson MC;Sokolov A;Aldridge BB
长期的多药化疗是实现结核病的持久治愈所必需的。然而，我们缺乏有效的，高通量的体外模型，预测动物的结果。在这里，我们提供了一种可扩展的方法，合理地优先考虑在体内结核病小鼠模型的测试组合疗法。我们系统地测量了结核分枝杆菌对10种抗生素中所有两种和三种药物组合的反应，在8种条件下重现病变微环境，导致> 500，000次测量。使用这些体外数据，我们开发了预测疾病复发小鼠模型中多药治疗结果的分类器，并确定了最能描述体内治疗结果的体外模型集合。我们在特定的体外模型中确定了效力和药物相互作用的特征，以区分药物组合是否优于两个重要的临床前小鼠模型中的标准治疗。我们的框架可推广到其他需要联合治疗的难治性疾病。本文的透明同行评审过程的记录包括在补充信息中。多个体外模型中结核药物联合反应的综合数据集计算建模基于体外数据预测小鼠治疗结果体外模型的集合预测体内环境中的治疗结果体外药物联合效力预测复发小鼠模型中的结果本研究通过结合体外剂量反应测量和免疫组化建立了一个框架，以优先考虑结核病的联合抗生素治疗。使用数学建模的体内治疗结果。该研究中开发的实验和计算工具确定了具有高度预测性信息的体外条件集以及区分具有改善的体内治疗结果的药物组合的效力和药物相互作用的特征。
Lengthy multidrug chemotherapy is required to achieve a durable cure in tuberculosis. However, we lack well-validated, high-throughput in vitro models that predict animal outcomes. Here, we provide an extensible approach to rationally prioritize combination therapies for testing in in vivo mouse models of tuberculosis. We systematically measured Mycobacterium tuberculosis response to all two- and three-drug combinations among ten antibiotics in eight conditions that reproduce lesion microenvironments, resulting in >500,000 measurements. Using these in vitro data, we developed classifiers predictive of multidrug treatment outcome in a mouse model of disease relapse and identified ensembles of in vitro models that best describe in vivo treatment outcomes. We identified signatures of potencies and drug interactions in specific in vitro models that distinguish whether drug combinations are better than the standard of care in two important preclinical mouse models. Our framework is generalizable to other difficult-to-treat diseases requiring combination therapies. A record of this paper’s transparent peer review process is included in the supplemental information. Comprehensive dataset of TB drug combination responses in multiple in vitro models Computational modeling predicts mouse treatment outcome based on in vitro data Ensembles of in vitro models predict treatment outcomes in in vivo environments In vitro drug combination potencies predict outcomes in a relapsing mouse model This study establishes a framework to prioritize combination antibiotic therapies for tuberculosis by combining in vitro dose response measurement and in vivo treatment outcomes using mathematical modeling. The experimental and computational tools developed in the study identified sets of in vitro conditions with highly predictive information and signatures of potency and drug interaction that distinguish drug combinations with improved in vivo treatment outcomes.
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影响因子：
13.6
作者：
Cokol M;Kuru N;Bicak E;Larkins-Ford J;Aldridge BB
通讯作者：
Aldridge BB
影响因子：
5.7
作者：
Coates, Anthony R. M.;Hu, Yanmin;Yeh, Pamela
通讯作者：
Yeh, Pamela
影响因子：
4.3
作者：
Cokol, Murat;Li, Chen;Chandrasekaran, Sriram
通讯作者：
Chandrasekaran, Sriram
影响因子：
158.5
作者：
Conradie, Francesca;Diacon, Andreas H.;Spigelman, Melvin
通讯作者：
Spigelman, Melvin
影响因子：
3.4
作者：
Cunningham-Bussel, Amy;Bange, Franz C.;Nathan, Carl F.
通讯作者：
Nathan, Carl F.