The pursuit of mechanism of action: uncovering drug complexity in TB drug discovery

A Chemoproteomic Approach to Elucidate the Mechanism of Action of 6-Azasteroids with Unique Activity in Mycobacteria

By illuminating key 6-azasteroid-protein interactions in both Mycobacterium tuberculosis (Mtb) and the closely related model organism Mycobacterium marinum (Mm), we sought to improve the antimycobacterial potency of 6-azasteroids and further our understanding of the mechanisms responsible for their potentiation of the antituberculosis drug bedaquiline. We selected a newly developed 6-azasteroid analog and an analog reported previously (ACS Infect. Dis. 2019, 5 (7), 1239-1251) to study their phenotypic effects on Mtb and Mm, both alone and in combination with bedaquiline. The 6-azasteroid analog, 17β-[N-(4-trifluoromethoxy-diphenylmethyl)carbamoyl]-6-propyl-azaandrostan-3-one, robustly potentiated bedaquiline-mediated antimycobacterial activity, with a nearly 8-fold reduction in Mm bedaquiline minimal inhibitory concentration (85 nM alone versus 11 nM with 20 μM 6-azasteroid). This analog displayed minimal inhibitory activity against recombinant mycobacterial 3β-hydroxysteroid dehydrogenase, a previously identified target of several 6-azasteroids. Dose-dependent potentiation of bedaquiline by this analog reduced mycobacterial intracellular ATP levels and impeded the ability of Mtb to neutralize exogenous oxidative stress in culture. We developed two 6-azasteroid photoaffinity probes to investigate azasteroid-protein interactions in Mm whole cells. Using bottom-up mass spectrometric profiling of the cross-linked proteins, we identified eight potential Mm/Mtb protein targets for 6-azasteroids. The nature of these potential targets indicates that proteins related to oxidative stress resistance play a key role in the BDQ-potentiating activity of azasteroids and highlights the potential impact of inhibition of these targets on the generation of drug sensitivity.

Sensing of Antibiotic-Bacteria Interactions

Sensing of antibiotic-bacteria interactions is an important area of research that has gained significant attention in recent years. Antibiotic resistance is a major public health concern, and it is essential to develop new strategies for detecting and monitoring bacterial responses to antibiotics in order to maintain effective antibiotic development and antibacterial treatment. This review summarizes recent advances in sensing strategies for antibiotic-bacteria interactions, which are divided into two main parts: studies on the mechanism of action for sensitive bacteria and interrogation of the defense mechanisms for resistant ones. In conclusion, this review provides an overview of the present research landscape concerning antibiotic-bacteria interactions, emphasizing the potential for method adaptation and the integration of machine learning techniques in data analysis, which could potentially lead to a transformative impact on mechanistic studies within the field.

Biomarker discovery for tuberculosis using metabolomics

Tuberculosis (TB) is the leading cause of death among infectious diseases, and the ratio of cases in which its pathogen Mycobacterium tuberculosis (Mtb) is drug resistant has been increasing worldwide, whereas latent tuberculosis infection (LTBI) may develop into active TB. Thus it is important to understand the mechanism of drug resistance, find new drugs, and find biomarkers for TB diagnosis. The rapid progress of metabolomics has enabled quantitative metabolite profiling of both the host and the pathogen. In this context, we provide recent progress in the application of metabolomics toward biomarker discovery for tuberculosis. In particular, we first focus on biomarkers based on blood or other body fluids for diagnosing active TB, identifying LTBI and predicting the risk of developing active TB, as well as monitoring the effectiveness of anti-TB drugs. Then we discuss the pathogen-based biomarker research for identifying drug resistant TB. While there have been many reports of potential candidate biomarkers, validations and clinical testing as well as improved bioinformatics analysis are needed to further substantiate and select key biomarkers before they can be made clinically applicable.

Tuberculosis Drug Discovery: Challenges and New Horizons

Over the past 2000 years, tuberculosis (TB) has claimed more lives than any other infectious disease. In 2020 alone, TB was responsible for 1.5 million deaths worldwide, comparable to the 1.8 million deaths caused by COVID-19. The World Health Organization has stated that new TB drugs must be developed to end this pandemic. After decades of neglect in this field, a renaissance era of TB drug discovery has arrived, in which many novel candidates have entered clinical trials. However, while hundreds of molecules are reported annually as promising anti-TB agents, very few successfully progress to clinical development. In this Perspective, we critically review those anti-TB compounds published in the last 6 years that demonstrate good in vivo efficacy against Mycobacterium tuberculosis. Additionally, we highlight the main challenges and strategies for developing new TB drugs and the current global pipeline of drug candidates in clinical studies to foment fresh research perspectives.

Development of Predictive Classification Models for Whole Cell Antimycobacterial Activity of Benzothiazinones

Nitrobenzothiazinones (BTZs) are a very potent class of antibiotics against Mycobacterium tuberculosis. However, relationships between their structural properties and whole cell activity remain poorly predictable. Herein, we present the synthesis and antimycobacterial evaluation of a diverse set of BTZs. High potency was predominantly achieved by piperidine and piperazine substitutions, whereupon three compounds were identified as promising candidates, showing preferable metabolic stability. Lack of correlation between potency and calculated binding energies suggested that target inhibition is not the only requirement to obtain suitable antimycobacterial agents. In contrast, prediction of whole cell activity class was successfully accomplished by extensively validated machine learning models. The performance of the superior model was further verified by >70% correct class predictions for a large set of reported BTZs. Our generated model is thus a key prerequisite to streamline lead optimization endeavors, particularly regarding the improvement of overall hit rates in whole cell antimycobacterial assays.

Protein Integrated Network Analysis to Reveal Potential Drug Targets Against Extended Drug-Resistant Mycobacterium tuberculosis XDR1219

The reconstruction and analysis of the protein-protein interaction (PPI) network is a powerful approach to understand the complex biological and molecular functions in normal and disease states of the cell. The interactome of most organisms is largely unidentified except some model organisms. The current study focused on the construction of PPI network for the human pathogen Mycobacterium tuberculosis (MTB)-resistant strain XDR1219 using computational methods. In this work, a bioinformatics approach was employed to reveal potential drug targets. The pipeline adopted the combination of an extensive integrated network analysis that led to identify 22 key proteins involved in drug resistance, resistant metabolic pathways, virulence, pathogenesis and persistency of the infection. The MTB XDR1219 interactome consists of 11,383 non-redundant PPIs among 1499 proteins covering 38% of the entire MTB XDR1219 proteome. The overall quality of the network was assessed and topological parameters of the PPI were calculated. The predicted interactions were functionally annotated and their relevance was assessed with the functional similarity. The study attempts to present the interactome of previously unidentified MTB XDR1219 and revealed potential drug targets that can be further explored by scientific community.

Enzymatic β-Oxidation of the Cholesterol Side Chain in Mycobacterium tuberculosis Bifurcates Stereospecifically at Hydration of 3-Oxo-cholest-4,22-dien-24-oyl-CoA

The unique ability of Mycobacterium tuberculosis (Mtb) to utilize host lipids such as cholesterol for survival, persistence, and virulence has made the metabolic pathway of cholesterol an area of great interest for therapeutics development. Herein, we identify and characterize two genes from the Cho-region (genomic locus responsible for cholesterol catabolism) of the Mtb genome, chsH3 (Rv3538) and chsB1 (Rv3502c). Their protein products catalyze two sequential stereospecific hydration and dehydrogenation steps in the β-oxidation of the cholesterol side chain. ChsH3 favors the 22S hydration of 3-oxo-cholest-4,22-dien-24-oyl-CoA in contrast to the previously reported EchA19 (Rv3516), which catalyzes formation of the (22R)-hydroxy-3-oxo-cholest-4-en-24-oyl-CoA from the same enoyl-CoA substrate. ChsB1 is stereospecific and catalyzes dehydrogenation of the ChsH3 product but not the EchA19 product. The X-ray crystallographic structure of the ChsB1 apo-protein was determined at a resolution of 2.03 Å, and the holo-enzyme with bound NAD⁺ cofactor was determined at a resolution of 2.21 Å. The homodimeric structure is representative of a classical NAD⁺-utilizing short-chain type alcohol dehydrogenase/reductase, including a Rossmann-fold motif, but exhibits a unique substrate binding site architecture that is of greater length and width than its homologous counterparts, likely to accommodate the bulky steroid substrate. Intriguingly, Mtb utilizes hydratases from the MaoC-like family in sterol side-chain catabolism in contrast to fatty acid β-oxidation in other species that utilize the evolutionarily distinct crotonase family of hydratases.