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Tuberculosis Vaccines and Prevention of Infection
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SUMMARY: Tuberculosis (TB) is a leading cause of death worldwide despite the availability of effective chemotherapy for over 60 years. Although Mycobacterium bovis bacillus Calmette-Guérin (BCG) vaccination protects against active TB disease in some populations, its efficacy is suboptimal. Development of an effective TB vaccine is a top global priority that has been hampered by an incomplete understanding of protective immunity to TB. Thus far, preventing TB disease, rather than infection, has been the primary target for vaccine development. Several areas of research highlight the importance of including preinfection vaccines in the development pipeline. First, epidemiology and mathematical modeling studies indicate that a preinfection vaccine would have a high population-level impact for control of TB disease. Second, immunology studies support the rationale for targeting prevention of infection, with evidence that host responses may be more effective during acute infection than during chronic infection. Third, natural history studies indicate that resistance to TB infection occurs in a small percentage of the population. Fourth, case-control studies of BCG indicate that it may provide protection from infection. Fifth, prevention-of-infection trials would have smaller sample sizes and a shorter duration than disease prevention trials and would enable opportunities to search for correlates of immunity as well as serve as a criterion for selecting a vaccine product for testing in a larger TB disease prevention trial. Together, these points support expanding the focus of TB vaccine development efforts to include prevention of infection as a primary goal along with vaccines or other interventions that reduce the rate of transmission and reactivation.
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“Bacillus Calmette-Guerin (BCG), which was introduced in 1921, is the sole approved TB vaccine, but it offers only limited protection 132 .”
From Nanotechnology approaches for global infectious diseases (2021) · cites this paper as [132]
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Metadata
- Authors
- Hawn, T. R., Day, T. A., Scriba, T. J., Hatherill, M., Hanekom, W. A., Evans, T. G., Churchyard, G. J., Kublin, J. G., Bekker, L.-G., & Self, S. G.
- Affiliations
- University of Washington (US)
- Fred Hutch Cancer Center (US)
- University of Cape Town (ZA)
- Gates Foundation (US)
- Aeras (US)
- University of the Witwatersrand (ZA)
- ORCID
- Venue
- Microbiology and Molecular Biology Reviews
- Publisher
- American Society for Microbiology
- Published
- 2014-12
- Volume
- 78
- Issue
- 4
- Pages
- 650-671
- ISSN
- 1092-2172 (ISSN-L)
- 1098-5557
- PMC
- PMC4248657
- OpenAlex
- W2137780892
- Cited by
- 164
- References
- 209
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 8 · 2023: 8 · 2022: 16 · 2021: 14 · 2020: 15 · 2019: 21 · 2018: 22 · 2017: 23 · 2016: 20 · 2015: 6
- FWCI
- 2.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Microbiology and Molecular Biology Reviews (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (84323)
- National Institute of Allergy and Infectious Diseases (UM1AI068614, UM1AI068635)
- Topics
- Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
- Life Sciences › Immunology and Microbiology › Immunology › Immune responses and vaccinations
- Health Sciences › Medicine › Infectious Diseases › Infectious Diseases and Tuberculosis
- Keywords
- tuberculosis vaccine development, BCG vaccine, protective immunity, natural resistance to infection, correlates of immunity, tuberculosis transmission, tuberculosis reactivation, tuberculosis epidemiology
- MeSH terms
- Animals, Clinical Trials as Topic, Humans, Models, Theoretical, Mycobacterium bovis, Mycobacterium tuberculosis, Tuberculosis, Vaccination, HIV Infections, Models, Animal, Tuberculosis Vaccines, Coinfection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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