Semi-automated colony-forming unit counting for biosafety level 3 laboratories.

Biosafety level 3 decontamination precautions motivate measuring microbial colonies using consumable photography instead of expensive automated plate counters or smartphones, and assaying drug treatments-with multiple concentrations per treatment, replicates, and controls-produces hundreds of images. Here, we present a protocol for semi-automated image analysis by hand-tuning three parameters. The parameters control for non-uniform colony growth and artifacts such as lid condensation, reflections, and plating streaks. We describe steps to prepare images, tune parameters, and plot dose-response relationships. For complete details on the use and execution of this protocol, please refer to Larkins-Ford et al.1.

Tools to develop antibiotic combinations that target drug tolerance in Mycobacterium tuberculosis.

Combination therapy is necessary to treat tuberculosis to decrease the rate of disease relapse and prevent the acquisition of drug resistance, and shorter regimens are urgently needed. The adaptation of Mycobacterium tuberculosis to various lesion microenvironments in infection induces various states of slow replication and non-replication and subsequent antibiotic tolerance. This non-heritable tolerance to treatment necessitates lengthy combination therapy. Therefore, it is critical to develop combination therapies that specifically target the different types of drug-tolerant cells in infection. As new tools to study drug combinations earlier in the drug development pipeline are being actively developed, we must consider how to best model the drug-tolerant cells to use these tools to design the best antibiotic combinations that target those cells and shorten tuberculosis therapy. In this review, we discuss the factors underlying types of drug tolerance, how combination therapy targets these populations of bacteria, and how drug tolerance is currently modeled for the development of tuberculosis multidrug therapy. We highlight areas for future studies to develop new tools that better model drug tolerance in tuberculosis infection specifically for combination therapy testing to bring the best drug regimens forward to the clinic.

Leveraging laboratory and clinical studies to design effective antibiotic combination therapy.

Interest in antibiotic combination therapy is increasing due to antimicrobial resistance and a slowing antibiotic pipeline. However, aside from specific indications, combination therapy in the clinic is often not administered systematically; instead, it is used at the physician's discretion as a bet-hedging mechanism to increase the chances of appropriately targeting a pathogen(s) with an unknown antibiotic resistance profile. Some recent clinical trials have been unable to demonstrate superior efficacy of combination therapy over monotherapy. Other trials have shown a benefit of combination therapy in defined circumstances consistent with recent studies indicating that factors including species, strain, resistance profile, and microenvironment affect drug combination efficacy and drug interactions. In this review, we discuss how a careful study design that takes these factors into account, along with the different drug interaction and potency metrics for assessing combination performance, may provide the necessary insight to understand the best clinical use-cases for combination therapy.

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.

Tri-culture system for pro-hapten sensitizer identification and potency classification.

Allergic contact dermatitis (ACD) is an inflammatory disease that impacts 15-20% of the general population and accurate screening methods for chemical risk assessment are needed. However, most approaches poorly predict pre- and pro-hapten sensitizers, which require abiotic or metabolic conversion prior to inducing sensitization. We developed a tri-culture system comprised of MUTZ-3-derived Langerhans cells, HaCaT keratinocytes, and primary dermal fibroblasts to mimic the cellular and metabolic environments of skin sensitization. A panel of non-sensitizers and sensitizers was tested and the secretome was evaluated. A support vector machine (SVM) was used to identify the most predictive sensitization signature and classification trees identified statistical thresholds to predict sensitizer potency. The SVM computed 91% tri-culture prediction accuracy using the top 3 ranking biomarkers (IL-8, MIP-1ß, and GM-CSF) and improved the detection of pre- and pro-haptens. This in vitro assay combined with in silico data analysis presents a promising approach and offers the possibility of multi-metric analysis for enhanced ACD sensitizer screening.