Drug-resistant tuberculosis: a persistent global health concern

Farhat, Maha; Cox, Helen; Ghanem, Marwan; Denkinger, Claudia M.; Rodrigues, Camilla; Abd El Aziz, Mirna S.; Enkh-Amgalan, Handaa; Vambe, Debrah; Ugarte-Gil, Cesar; Furin, Jennifer; Pai, Madhukar

doi:10.1038/s41579-024-01025-1

Cited by 7 publications

(1 citation statement)

References 216 publications

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“…While treatment success rates for drug-sensitive TB reach 85%, managing multi-drug-resistant (MDR) strains presents formidable obstacles due to the limitations of existing therapies, marked by reduced efficacy, prolonged treatment duration, and severe side effects such as hepatotoxicity, ototoxicity, and neuropsychiatric manifestations. 2,3 Although TB primarily affects the lungs, its impact extends to various bodily organs, including the brain, kidneys, and spine. 4 Antibiotics serve as the cornerstone of TB treatment, yet the emergence of resistant strains underscores the pressing need for innovative therapeutic strategies to combat this global health menace.…”

Section: Introductionmentioning

confidence: 99%

Targeting Mycobacterial Polyketide Synthase Pks13-TE Domain with Small Organic Molecular Scaffolds: QSAR-Driven Modelling Studies to Identify Novel Inhibitors

Malhotra,

Bhowmick,

Okla

et al. 2024

Preprint

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This study presents a detailed computational investigation aimed at identifying and evaluating potential small organic molecular scaffolds as inhibitors targeting the Pks13 protein, a critical enzyme in tuberculosis (TB) drug discovery. Our approach integrates multiple computational techniques, including quantitative structure-activity relationship (QSAR) modelling, compound library generation, molecular docking, biological activity prediction, re-evaluation of screened compounds, molecular dynamics (MD) analysis, and binding free energy estimation using the MM-GBSA approach. We developed a robust QSAR model using multiple linear regression (MLR) techniques, specifically tailored to predict the minimum inhibitory concentration (MIC) values of potential anti-TB agents. This model demonstrated excellent predictive performance, validated through rigorous internal and external validation procedures. Through molecular docking simulations, we identified five promising inhibitors Ethyl (R,Z)-2-(2-((2-chlorobenzyl) oxy)benzylidene)-5-(4-chlorophenyl)-7-methyl-3-oxo-2,3-dihydro-5H-thiazolo [3,2-a] pyrimidine-6-carboxylate (PKD1), (7R,9aR)-7-(2-hydroxyphenyl)-4-(2-methoxyphenyl)-3-methyl-7,8,9,9a-tetrahydroisoxazolo[5,4-b]quinolin-5(6H)-one (PKD2), (7-(4-methoxy phenyl)-2-methyl-3-phenylpyrazolo[1,5-a]pyrimidin-5-yl)(4-(3-(trifluoromethyl)phenyl) piperazin-1-yl)methanone (PKD3), (8S,10S)-10-(((2R,4S,5S,6S)-4-amino-5-hydroxy-6-methyl tetrahydro-2H-pyran-2-yl)oxy)-6,8,11-trihydroxy-8-(2-hydroxyacetyl)-1-methoxy-7,8,9,10-tetra-hydrotetracene-5,12-dione (PKD4), 3-(3,4-dimethylphenyl)-5-((2-(4-fluorophenyl)-2-oxoethyl) thio)-6-phenyl-2-thioxo-2,3,5,7a-tetrahydrothiazolo[4,5-d]pyrimidin-7(6H)-one (PKD5)with high binding affinities, prioritizing them based on their binding energies. These selected compounds were further assessed for their biological activity predictions, guiding the selection of candidates for experimental validation. Subsequent re-evaluation using absolute binding free energy estimation and MD simulations provided insights into the stability and dynamics of the protein-ligand complexes over time. Our findings highlighted the potential of identified compounds as stable binders within the Pks13-TE domain binding pocket, underscoring their viability as drug candidates. Additionally, binding free energy analysis using MM-GBSA reaffirmed the strong affinity of the proposed compounds towards Pks13. Our integrated computational approach offers a systematic and detailed framework for the identification and characterization of potential Pks13 inhibitors with specific relevance to TB drug discovery.

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Section: Introductionmentioning

confidence: 99%

Targeting Mycobacterial Polyketide Synthase Pks13-TE Domain with Small Organic Molecular Scaffolds: QSAR-Driven Modelling Studies to Identify Novel Inhibitors

Malhotra,

Bhowmick,

Okla

et al. 2024

Preprint

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Stimuli‐Responsive NO Delivery Platforms for Bacterial Infection Treatment

Hu,

Ding,

et al. 2024

Adv Healthcare Materials

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The prevalence of drug‐resistant bacterial infections has emerged as a grave threat to clinical treatment and global human health, presenting one of the foremost challenges in medical care. Thus, there is an urgent imperative to develop safe and efficacious novel antimicrobial strategies. Nitric oxide (NO) is a recognized endogenous signaling molecule, which plays a pivotal role in numerous pathological processes. Currently, NO has garnered significant interest as an antibacterial agent due to its capability to eradicate bacteria, disrupt biofilms, and facilitate wound healing, all while circumventing the emergence of drug resistance. However, the inherently unstable characteristic of NO therapeutic gas renders the controlled administration of NO gases exceedingly challenging. Hence, in this review, the current challenge of bacterial infection is discussed; then it is briefly elucidated the antibacterial mechanism of NO and comprehensively delineate the recent advancements in stimulus‐responsive NO delivery platforms, along with their merits, obstacles, and prospective avenues for clinical application. This review offers guidance for future advancements in NO‐medicated anti‐infection therapy is hoped.

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Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints

Liu,

Wang,

Zhang

et al. 2024

Advanced Science

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Tuberculosis (TB) stands as the second most fatal infectious disease after COVID‐19, the effective treatment of which depends on accurate diagnosis and phenotyping. Metabolomics provides valuable insights into the identification of differential metabolites for disease diagnosis and phenotyping. However, TB diagnosis and phenotyping remain great challenges due to the lack of a satisfactory metabolic approach. Here, a metabolomics‐based diagnostic method for rapid TB detection is reported. Serum metabolic fingerprints are examined via an automated nanoparticle‐enhanced laser desorption/ionization mass spectrometry platform outstanding by its rapid detection speed (measured in seconds), minimal sample consumption (in nanoliters), and cost‐effectiveness (approximately $3). A panel of 14 m z−1 features is identified as biomarkers for TB diagnosis and a panel of 4 m z−1 features for TB phenotyping. Based on the acquired biomarkers, TB metabolic models are constructed through advanced machine learning algorithms. The robust metabolic model yields a 97.8% (95% confidence interval (CI), 0.964‐0.986) area under the curve (AUC) in TB diagnosis and an 85.7% (95% CI, 0.806‐0.891) AUC in phenotyping. In this study, serum metabolic biomarker panels are revealed and develop an accurate metabolic tool with desirable diagnostic performance for TB diagnosis and phenotyping, which may expedite the effective implementation of the end‐TB strategy.

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Drug-resistant tuberculosis: a persistent global health concern

Cited by 7 publications

References 216 publications

Targeting Mycobacterial Polyketide Synthase Pks13-TE Domain with Small Organic Molecular Scaffolds: QSAR-Driven Modelling Studies to Identify Novel Inhibitors

Targeting Mycobacterial Polyketide Synthase Pks13-TE Domain with Small Organic Molecular Scaffolds: QSAR-Driven Modelling Studies to Identify Novel Inhibitors

Stimuli‐Responsive NO Delivery Platforms for Bacterial Infection Treatment

Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints

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