Despite two decades of intensified research to understand and cure tuberculosis disease, biological uncertainties remain and hamper progress. However, owing to collaborative initiatives including academia, the pharmaceutical industry and non-for-profit organizations, the drug candidate pipeline is promising. This exceptional success comes with the inherent challenge of prioritizing multidrug regimens for clinical trials and revamping trial designs to accelerate regimen development and capitalize on drug discovery breakthroughs. Most wanted are markers of progression from latent infection to active pulmonary disease, markers of drug response and predictors of relapse, in vitro tools to uncover synergies that translate clinically and animal models to reliably assess the treatment shortening potential of new regimens. In this Review, we highlight the benefits and challenges of 'one-size-fits-all' regimens and treatment duration versus individualized therapy based on disease severity and host and pathogen characteristics, considering scientific and operational perspectives. 

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Anti-tuberculosis treatment strategies and drug development: challenges and priorities

The physiology and genetics of bacterial responses to antibiotic combinations

Advancement in leishmaniasis diagnosis and therapeutics: An update.

The pipeline of new molecules and regimens against drug-resistant tuberculosis.

The remarkable ability of Mycobacterium tuberculosis to survive attacks from the host immune response and drug treatment is due to the resilience of a few bacilli rather than a result of survival of the entire population. Maintenance of mycobacterial subpopulations with distinct phenotypic characteristics is key for survival in the face of dynamic and variable stressors encountered during infection. Mycobacterial populations develop a wide range of phenotypes through an innate asymmetric growth pattern and adaptation to fluctuating microenvironments during infection that point to heterogeneity being a vital survival strategy. In this Review, we describe different types of mycobacterial heterogeneity and discuss how heterogeneity is generated and regulated in response to environmental cues. We discuss how this heterogeneity may have a key role in recording memory of their environment at both the single-cell level and the population level to give mycobacterial populations plasticity to withstand complex stressors. In this Review, Aldridge and colleagues describe different types of mycobacterial heterogeneity and discuss how cell-to-cell heterogeneity and environmental heterogeneity are generated and regulated in response to environmental cues. 

Types and functions of heterogeneity in mycobacteria

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.

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

Leveraging laboratory and clinical studies to design effective antibiotic combination therapy

A lengthy multidrug chemotherapy is required to achieve a durable cure in tuberculosis. Variation in Mycobacterium tuberculosis drug response is created by the differing microenvironments in lesions, which create different bacterial drug susceptibilities. To better realize the potential of combination therapy to shorten treatment duration, multidrug therapy design should deliberately explore the vast combination space. We face a significant scaling challenge in making systematic drug combination measurements because it is not practical to use animal models for comprehensive drug combination studies, nor are there well validated high-throughput in vitro models that predict animal outcomes. We hypothesized that we could both prioritize combination therapies and quantify the predictive power of various in vitro models for drug development using a dataset of drug combination dose responses measured in multiple in vitro models. We systematically measured M. tuberculosis response to all 2- and 3-drug combinations among ten antibiotics in eight conditions that reproduce lesion microenvironments. Applying machine learning to this comprehensive dataset, we developed classifiers predictive of multidrug treatment outcome in a mouse model of disease relapse. We trained classifiers on multiple mouse models and identified ensembles of in vitro models that best describe in vivo treatment outcomes. Furthermore, we found that combination synergies are less important for predicting outcome than metrics of potency. Here, we map a path forward to rationally prioritize combinations for animal and clinical studies using systematic drug combination measurements with validated in vitro models. Our pipeline is generalizable to other difficult-to-treat diseases requiring combination therapies.

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Talia Greenstein

Papers

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

Leveraging laboratory and clinical studies to design effective antibiotic combination therapy

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