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.

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.

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.

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.