Larkins-Ford Jonah, Greenstein Talia, Van Nhi, Degefu Yonatan N, Olson Michaela C, Sokolov Artem, Aldridge Bree B
Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA 02111, USA; Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance, Boston, MA 02111, USA; Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA; Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA 02115, USA.
Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA 02111, USA; Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance, Boston, MA 02111, USA; Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA.
Cell Syst. 2021 Nov 17;12(11):1046-1063.e7. doi: 10.1016/j.cels.2021.08.004. Epub 2021 Aug 31.
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.
在结核病治疗中，需要长期使用多种药物进行化疗才能实现持久治愈。然而，我们缺乏经过充分验证的、能够预测动物实验结果的高通量体外模型。在此，我们提供了一种可扩展的方法，用于合理地对联合疗法进行优先级排序，以便在结核病的体内小鼠模型中进行测试。我们系统地测量了结核分枝杆菌在八种模拟病变微环境的条件下对十种抗生素中所有两药和三药组合的反应，得到了超过50万次测量结果。利用这些体外数据，我们开发了能够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组合。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，这些特征可以区分在两种重要的临床前小鼠模型中联合用药是否优于标准治疗方案。我们的框架可推广到其他需要联合治疗的难治性疾病。本文透明的同行评审过程记录包含在补充信息中。
