Biology subjects

Aldridge, B. B.

Publications and source records attributed to Aldridge, B. B..

3 recordsLinked to original sources

bioRxiv2021

C25-modified rifamycin derivatives with improved activity against Mycobacterium abscessus

Infections caused by Mycobacterium abscessus are difficult to treat due to its intrinsic resistance to most antibiotics. Formation of biofilms and the capacity of M. abscessus to survive inside host phagocytes further complicate eradication. Herein, we explored whether addition of a carbamate-linked group at the C25 position of rifamycin SV blocks enzymatic inactivation by ArrMab, an ADP-ribosyltransferase conferring resistance to rifampicin. Unlike rifampicin, 5j, a benzyl piperidine rifamycin derivative with a morpholino substituted C3 position, is not modified by purified ArrMab. Additionally, we show that the ArrMab D82 residue is essential for catalytic activity. Thermal profiling of ArrMab in the presence of 5j, rifampicin or rifabutin shows that 5j does not bind to ArrMab. We found that the activity of 5j is comparable to amikacin against M. abscessus planktonic cultures and pellicles. Critically, 5j also exerts potent antimicrobial activity against M. abscessus in human macrophages and shows synergistic activity with amikacin and azithromycin.

microbiology↗

bioRxiv2021

A competitive activity-based protein profiling platform yields cell wall synthesis inhibitors active against replicating and non-replicating Mycobacterium tuberculosis

The identification and validation of a small molecules targets is a major bottleneck in the discovery process for tuberculosis antibiotics. Activity-based protein profiling (ABPP) is an efficient tool for determining a small molecules targets within complex proteomes. However, how target inhibition relates to biological activity is often left unexplored. Here we studied the effects of 1,2,3-triazole ureas on Mycobacterium tuberculosis (Mtb). After screening ~200 compounds, we focused on two inhibitors active against both exponentially replicating and hypoxia-induced drug-tolerant Mtb that form part of a four-compound structure-activity series. The compound with negligible activity revealed potential false positive targets not addressed in other ABPP studies. Biochemistry, computational docking, and morphological analysis confirmed that active compounds preferentially inhibit serine hydrolases with cell wall and lipid metabolism functions and that disruption of the cell wall underlies biological activity. Our findings showed that ABPP identifies the targets most likely relevant to a compounds antibacterial activity.

microbiology↗

bioRxiv2021

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

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 generate 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. One Sentence SummarySignatures of in vitro potency and drug interaction measurements predict combination therapy outcomes in mouse models of tuberculosis.

microbiology↗