Cell Systems
Volume 12, Issue 11, 17 November 2021, Pages 1046-1063.e7
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Article
Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis

https://doi.org/10.1016/j.cels.2021.08.004Get rights and content
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Highlights

  • 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

Summary

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.

Keywords

tuberculosis
antibiotics
drug combinations
combination therapy
drug interactions
mycobacteria
infectious diseases

Data and code availability

  • All data reported in this paper are present within the published figures and publicly available in the supplemental information. Additionally, the data cube and IC90s have been deposited at Mendeley and are publicly available at https://doi.org/10.17632/m2y7jpz4wz.1.

  • Original code used for machine learning is available in this paper’s supplemental information.

  • The scripts used to generate the figures reported in this paper are available in this paper’s supplemental information.

  • Any additional information required to reproduce this work is available from the Lead Contact.

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