Comprehensive dataset of TB drug combination responses in multiple in vitro models
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Computational modeling predicts mouse treatment outcome based on in vitro data
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Ensembles of in vitro models predict treatment outcomes in in vivo environments
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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.
Graphical abstract
Keywords
tuberculosis
antibiotics
drug combinations
combination therapy
drug interactions
mycobacteria
infectious diseases
Data and code availability
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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.