Pharmacological Strategies Against Mycobacterium Tuberculosis: An Overview of First-Line and Second-Line Treatments
Keywords:
Tuberculosis, drug resistance, vaccines, artificial intelligence, global healthAbstract
Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health threat, with millions of new cases annually and increasing drug-resistant strains like MDR and XDR-TB. This review examines current first-line and second-line treatments, their mechanisms, targets, and resistance issues, noting that lengthy regimens, side effects, and poor adherence continue to drive resistance despite new drugs like bedaquiline, pretomanid, and delamanid showing promise yet facing accessibility challenges. Vaccine development remains crucial, as the BCG vaccine's limited protection highlights the need for innovative solutions such as mRNA and nanoparticle-based vaccines. Meanwhile, artificial intelligence (AI) is enhancing TB diagnosis and treatment monitoring, though data security and infrastructure limitations must be resolved. Achieving the WHO's End-TB goals requires a comprehensive strategy—combining better diagnostics, affordable treatments, and effective vaccines—supported by stronger healthcare systems and global cooperation to reduce TB's burden, especially in high-risk regions.
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Gill CM, Dolan L, Piggott LM, et al. New developments in tuberculosis diagnosis and treatment. Breathe. 2022;18(1):210149.
Global Tuberculosis Report 2024. 1st ed. Geneva: World Health Organization; 2024.
Getahun H, Matteelli A, Chaisson RE, et al. Latent Mycobacterium tuberculosis Infection. Campion EW, editor. N Engl J Med [Internet]. 2015 [cited 2025 Mar 18];372(22):2127–2135.
Comstock GW, Livesay VT, Woolpert SF. THE PROGNOSIS OF A POSITIVE TUBERCULIN REACTION IN CHILDHOOD AND ADOLESCENCE. Am J Epidemiol [Internet]. 1974 [cited 2025 Mar 18];99(2):131–138.
Lienhardt C. From Exposure to Disease: The Role of Environmental Factors in Susceptibility to and Development of Tuberculosis. Epidemiol Rev [Internet]. 2001 [cited 2025 Mar 18];23(2):288–301.
Uplekar M, Weil D, Lonnroth K, et al. WHO’s new End TB Strategy. The Lancet [Internet]. 2015 [cited 2025 Mar 18];385(9979):1799–1801.
World Health Organization. WHO guidelines on tuberculosis infection prevention and control: 2019 update [Internet]. Geneva: World Health Organization; 2019 [cited 2025 Feb 11]. Available from: https://iris.who.int/handle/10665/311259.
Natarajan A, Beena PM, Devnikar AV, et al. A systemic review on tuberculosis. Indian J Tuberc [Internet]. 2020 [cited 2025 Mar 18];67(3):295–311.
Comas I, Coscolla M, Luo T, et al. Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat Genet [Internet]. 2013 [cited 2025 Mar 18];45(10):1176–1182.
Gagneux S. Host–pathogen coevolution in human tuberculosis. Philos Trans R Soc B Biol Sci [Internet]. 2012 [cited 2025 Mar 18];367(1590):850–859.
Leung AN. Pulmonary Tuberculosis: The Essentials. Radiology [Internet]. 1999 [cited 2025 Mar 18];210(2):307–322.
Hilda JN, Das S, Tripathy SP, et al. Role of neutrophils in tuberculosis: A bird’s eye view. Innate Immun [Internet]. 2020 [cited 2025 Mar 18];26(4):240–247.
Winslow GM, Cooper A, Reiley W, et al. Early T‐cell responses in tuberculosis immunity. Immunol Rev [Internet]. 2008 [cited 2025 Mar 18];225(1):284–299.
[14] Cooper AM. Cell-Mediated Immune Responses in Tuberculosis. Annu Rev Immunol [Internet]. 2009 [cited 2025 Mar 18];27(1):393–422.
Schluger NW. The Pathogenesis of Tuberculosis: The First One Hundred (and Twenty-Three) Years. Am J Respir Cell Mol Biol [Internet]. 2005 [cited 2025 Mar 18];32(4):251–256.
Bermudez LE, Goodman J. Mycobacterium tuberculosis invades and replicates within type II alveolar cells. Infect Immun [Internet]. 1996 [cited 2025 Mar 18];64(4):1400–1406.
Danelishvili L, McGarvey J, Li Y, et al. Mycobacterium tuberculosis infection causes different levels of apoptosis and necrosis in human macrophages and alveolar epithelial cells. Cell Microbiol [Internet]. 2003 [cited 2025 Mar 18];5(9):649–660.
Torrelles JB, Schlesinger LS. Integrating Lung Physiology, Immunology, and Tuberculosis. Trends Microbiol [Internet]. 2017 [cited 2025 Mar 18];25(8):688–697.
Luies L, Du Preez I. The Echo of Pulmonary Tuberculosis: Mechanisms of Clinical Symptoms and Other Disease-Induced Systemic Complications. Clin Microbiol Rev [Internet]. 2020 [cited 2025 Mar 18];33(4):e00036-20.
Chee CBE, Reves R, Zhang Y, et al. Latent tuberculosis infection: Opportunities and challenges. Respirol Carlton Vic. 2018;23(10):893–900.
Cohen A, Mathiasen VD, Schön T, et al. The global prevalence of latent tuberculosis: a systematic review and meta-analysis. Eur Respir J. 2019;54(3):1900655.
Huszár S, Chibale K, Singh V. The quest for the holy grail: new antitubercular chemical entities, targets and strategies. Drug Discov Today [Internet]. 2020 [cited 2025 Mar 18];25(4):772–780.
Grosset J. Mycobacterium tuberculosis in the extracellular compartment: an underestimated adversary. Antimicrob Agents Chemother. 2003;47(3):833–836.
Smith DT, Abernathy RS, Smith GB, et al. The apical localization of reinfection pulmonary tuberculosis. I. The stream flow theory. Am Rev Tuberc. 1954;70(4):547–556.
Lerner TR, Queval CJ, Lai RP, et al. Mycobacterium tuberculosis cords within lymphatic endothelial cells to evade host immunity. JCI Insight. 2020;5(10):e136937, 136937.
Heye T, Stoijkovic M, Kauczor H-U, et al. [Extrapulmonary tuberculosis: radiological imaging of an almost forgotten transformation artist]. ROFO Fortschr Geb Rontgenstr Nuklearmed. 2011;183(11):1019–1029.
Bagcchi S. WHO’s Global Tuberculosis Report 2022. Lancet Microbe. 2023;4(1):e20.
Ritacco V, Di Lonardo M, Reniero A, et al. Nosocomial spread of human immunodeficiency virus-related multidrug-resistant tuberculosis in Buenos Aires. J Infect Dis. 1997;176(3):637–642.
Wells CD, Cegielski JP, Nelson LJ, et al. HIV infection and multidrug-resistant tuberculosis: the perfect storm. J Infect Dis. 2007;196 Suppl 1:S86-107.
McQuaid CF, Vassall A, Cohen T, et al. The impact of COVID-19 on TB: a review of the data. Int J Tuberc Lung Dis Off J Int Union Tuberc Lung Dis. 2021;25(6):436–446.
Alemu A, Bitew ZW, Seid G, et al. Tuberculosis in individuals who recovered from COVID-19: A systematic review of case reports. PloS One. 2022;17(11):e0277807.
Fatima S, Kumari A, Das G, et al. Tuberculosis vaccine: A journey from BCG to present. Life Sci [Internet]. 2020 [cited 2025 Mar 18];252:117594.
Darrah PA, Zeppa JJ, Maiello P, et al. Prevention of tuberculosis in macaques after intravenous BCG immunization. Nature. 2020;577(7788):95–102.
Negi K, Bhaskar A, Dwivedi VP. Progressive Host-Directed Strategies to Potentiate BCG Vaccination Against Tuberculosis. Front Immunol. 2022;13:944183.
Gagneux S. Ecology and evolution of Mycobacterium tuberculosis. Nat Rev Microbiol. 2018;16(4):202–213.
Zumla A, Nahid P, Cole ST. Advances in the development of new tuberculosis drugs and treatment regimens. Nat Rev Drug Discov. 2013;12(5):388–404.
World Health Organization. WHO guidelines on tuberculosis infection prevention and control: 2019 update [Internet]. Geneva: World Health Organization; 2019 [cited 2025 Mar 19]. Available from: https://iris.who.int/handle/10665/311259.
Migliori GB, Tiberi S, Zumla A, et al. MDR/XDR-TB management of patients and contacts: Challenges facing the new decade. The 2020 clinical update by the Global Tuberculosis Network. Int J Infect Dis IJID Off Publ Int Soc Infect Dis. 2020;92S:S15–S25.
WHO Consolidated Guidelines on Tuberculosis. Module 4: Treatment - Drug-Resistant Tuberculosis Treatment, 2022 Update. 1st ed. Geneva: World Health Organization; 2022.
Zumla A, Chakaya J, Centis R, et al. Tuberculosis treatment and management--an update on treatment regimens, trials, new drugs, and adjunct therapies. Lancet Respir Med. 2015;3(3):220–234.
Sensi P. History of the development of rifampin. Rev Infect Dis. 1983;5 Suppl 3:S402-406.
Nahid P, Mase SR, Migliori GB, et al. Treatment of Drug-Resistant Tuberculosis. An Official ATS/CDC/ERS/IDSA Clinical Practice Guideline. Am J Respir Crit Care Med. 2019;200(10):e93–e142.
Koul A, Arnoult E, Lounis N, et al. The challenge of new drug discovery for tuberculosis. Nature [Internet]. 2011 [cited 2025 Mar 19];469(7331):483–490.
Mudde SE, Upton AM, Lenaerts A, et al. Delamanid or pretomanid? A Solomonic judgement! J Antimicrob Chemother. 2022;77(4):880–902.
Ginsberg AM, Spigelman M. Challenges in tuberculosis drug research and development. Nat Med [Internet]. 2007 [cited 2025 Mar 19];13(3):290–294.
Khawbung JL, Nath D, Chakraborty S. Drug resistant Tuberculosis: A review. Comp Immunol Microbiol Infect Dis [Internet]. 2021 [cited 2025 Feb 23];74:101574.
de Jager VR, Dawson R, van Niekerk C, et al. Telacebec (Q203), a New Antituberculosis Agent. N Engl J Med. 2020;382(13):1280–1281.
Lu LL, Smith MT, Yu KKQ, et al. IFN-γ-independent immune markers of Mycobacterium tuberculosis exposure. Nat Med [Internet]. 2019 [cited 2025 Mar 20];25(6):977–987.
Martin C, Aguilo N, Marinova D, et al. Update on TB Vaccine Pipeline. Appl Sci. 2020;10(7):2632.
Lewinsohn DA, Swarbrick GM, Park B, et al. Comprehensive definition of human immunodominant CD8 antigens in tuberculosis. Npj Vaccines [Internet]. 2017 [cited 2025 Mar 20];2(1):8.
Romano M, Squeglia F, Kramarska E, et al. A Structural View at Vaccine Development against M. tuberculosis. Cells [Internet]. 2023 [cited 2025 Mar 20];12(2):317.
Ali A, Waris A, Khan MA, et al. Recent advancement, immune responses, and mechanism of action of various vaccines against intracellular bacterial infections. Life Sci [Internet]. 2023 [cited 2025 Mar 20];314:121332.
Hart P, Copland A, Diogo GR, et al. Nanoparticle-Fusion Protein Complexes Protect against Mycobacterium tuberculosis Infection. Mol Ther [Internet]. 2018 [cited 2025 Mar 20];26(3):822–833.
Xue T, Stavropoulos E, Yang M, et al. RNA Encoding the MPT83 Antigen Induces Protective Immune Responses against Mycobacterium tuberculosis Infection. Infect Immun [Internet]. 2004 [cited 2025 Mar 20];72(11):6324–6329.
Al Tbeishat H. Novel In Silico mRNA vaccine design exploiting proteins of M. tuberculosis that modulates host immune responses by inducing epigenetic modifications. Sci Rep [Internet]. 2022 [cited 2025 Mar 20];12(1):4645.
Larsen SE, Erasmus JH, Reese VA, et al. An RNA-Based Vaccine Platform for Use against Mycobacterium tuberculosis. Vaccines [Internet]. 2023 [cited 2025 Mar 20];11(1):130.
Lopes UK, Valiati JF. Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Comput Biol Med [Internet]. 2017 [cited 2025 Mar 20];89:135–143.
Aguiar FS, Torres RC, Pinto JVF, et al. Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil. Med Biol Eng Comput [Internet]. 2016 [cited 2025 Mar 20];54(11):1751–1759.
Jaeger S, Juarez-Espinosa OH, Candemir S, et al. Detecting drug-resistant tuberculosis in chest radiographs. Int J Comput Assist Radiol Surg [Internet]. 2018 [cited 2025 Mar 20];13(12):1915–1925.
Khan S, Ullah R, Shahzad S, et al. Analysis of tuberculosis disease through Raman spectroscopy and machine learning. Photodiagnosis Photodyn Ther [Internet]. 2018 [cited 2025 Mar 20];24:286–291.
Mohamed EI, Mohamed MA, Moustafa MH, et al. Qualitative analysis of biological tuberculosis samples by an electronic nose-based artificial neural network. Int J Tuberc Lung Dis [Internet]. 2017 [cited 2025 Mar 20];21(7):810–817.
Kuok C, Horng M, Liao Y, et al. An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks. Microsc Res Tech [Internet]. 2019 [cited 2025 Mar 20];82(6):709–719.
Elveren E, Yumuşak N. Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm. J Med Syst [Internet]. 2011 [cited 2025 Mar 20];35(3):329–332.
Osamor VC, Azeta AA, Ajulo OO. Tuberculosis–Diagnostic Expert System: An architecture for translating patients information from the web for use in tuberculosis diagnosis. Health Informatics J [Internet]. 2014 [cited 2025 Mar 20];20(4):275–287.
Luo Y, Xue Y, Song H, et al. Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection. J Infect [Internet]. 2022 [cited 2025 Mar 20];84(5):648–657.
Hussain OA, Junejo KN. Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models. Inform Health Soc Care. 2019;44(2):135–151.
Working Group on New TB Drugs. Clinical Pipeline. www.newtbdrugs.org/pipeline/clinical Date last accessed 4 March (2025)
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