Novel Synergies and Isolate Specificities in the Drug Interaction Landscape of Mycobacterium abscessus.

Mycobacterium abscessus infections are difficult to treat and are often considered untreatable without tissue resection. Due to the intrinsic drug-resistant nature of the bacteria, combination therapy of three or more antibiotics is recommended. A major challenge in treating M. abscessus infections is the absence of a universal combination therapy with satisfying clinical success rates, leaving clinicians to treat infections using antibiotics lacking efficacy data. We systematically measured drug combinations in M. abscessus to establish a resource of drug interaction data and identify patterns of synergy to help design optimized combination therapies. We measured 191 pairwise drug combination effects among 22 antibacterials and identified 71 synergistic pairs, 54 antagonistic pairs, and 66 potentiator-antibiotic pairs. We found that commonly used drug combinations in the clinic, such as azithromycin and amikacin, are antagonistic in the lab reference strain ATCC 19977, whereas novel combinations, such as azithromycin and rifampicin, are synergistic. Another challenge in developing universally effective multidrug therapies for M. abscessus is the significant variation in drug response between isolates. We measured drug interactions in a focused set of 36 drug pairs across a small panel of clinical isolates with rough and smooth morphotypes. We observed strain-dependent drug interactions that cannot be predicted from single-drug susceptibility profiles or known drug mechanisms of action. Our study demonstrates the immense potential to identify synergistic drug combinations in the vast drug combination space and emphasizes the importance of strain-specific combination measurements for designing improved therapeutic interventions.

Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements.

A challenge in tuberculosis treatment regimen design is the necessity to combine three or more antibiotics. We narrow the prohibitively large search space by breaking down high-order drug combinations into drug pair units. Using pairwise in vitro measurements, we train machine learning models to predict higher-order combination treatment outcomes in the relapsing BALB/c mouse model. Classifiers perform well and predict many of the >500 possible combinations among 12 antibiotics to be improved over bedaquiline + pretomanid + linezolid, a treatment-shortening regimen compared with the standard of care in mice. We reformulate classifiers as simple rulesets to reveal guiding principles of constructing combination therapies for both preclinical and clinical outcomes. One example ruleset combines a drug pair that is synergistic in a dormancy model with a pair that is potent in a cholesterol-rich growth environment. These rulesets are predictive, intuitive, and practical, thus enabling rational construction of drug combinations.

Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis.

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.

Efficient Measurement of Drug Interactions with DiaMOND (Diagonal Measurement of N-Way Drug Interactions).

Treatment of tuberculosis necessitates combination therapy. Therefore, development of new tuberculosis therapies should consider multidrug effects because specific combinations may improve or reduce treatment efficacy through synergistic or antagonistic drug interactions, respectively. The standard assay of drug interactions is a checkerboard assay, wherein the drug-dose combinations are well-sampled across broad dose ranges. However, measuring three or more drugs in combination with a checkerboard assay is impractical due to the high number of measurements. We describe a protocol for efficient and quantitative measurement of drug interactions called diagonal measurement of n-way drug interactions (DiaMOND). DiaMOND is a geometric optimization of the checkerboard assay, using only the diagonal and axes of the checkerboard. This protocol describes how to perform DiaMOND experiments and analysis for Mycobacterium tuberculosis growth inhibition in standard growth conditions. As a guide on how to customize the DiaMOND assay, this protocol includes notes to modify the procedures for other growth conditions and outcome measures.