Abstract
SummaryLengthy 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.
Highlights
Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), remains a major global health issue
Drug combination compendium construction We developed an experimental and computational workflow to efficiently prioritize drug combinations early in regimen development based on drug combination measurements from in vitro models
We focused on modeling factors previously shown to influence Mtb growth and/or drug response, such as different carbon sources and abundance, low pH, low oxygen tension, and the intracellular environment (Gumbo et al, 2015; Parish, 2020; Lee et al, 2013; Early et al, 2016; Pethe et al, 2010; Guerrini et al, 2018; Baker et al, 2019; Vandal et al, 2009; Gold and Nathan, 2017; de Miranda Silva et al, 2019; Drusano et al, 2021)
Summary
Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), remains a major global health issue. Multidrug treatment regimens were developed to treat active TB infections by shortening treatment duration, reducing disease relapse, and decreasing antibiotic resistance development (Fox et al, 1999). The following four to seven months of treatment (continuation phase) consist of two drugs (isoniazid and rifampicin) aimed at reducing disease relapse by treating persisting bacteria that survived the intensive phase (Fox et al, 1999; Kerantzas and Jacobs, 2017; Mitchison, 1996). New regimens that can more efficiently treat Mtb are needed to shorten the intensive phase of treatment and reduce or eliminate the bacteria that persist and require continuation phase treatment (Kerantzas and Jacobs, 2017)
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