The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
TLR Agonists as Vaccine Adjuvants Targeting Cancer and Infectious Diseases
Open accessAbstract
Modern vaccines have largely shifted from using whole, killed or attenuated pathogens to being based on subunit components. Since this diminishes immunogenicity, vaccine adjuvants that enhance the immune response to purified antigens are critically needed. Further advantages of adjuvants include dose sparing, increased vaccine efficacy in immunocompromised individuals and the potential to protect against highly variable pathogens by broadening the immune response. Due to their ability to link the innate with the adaptive immune response, Toll-like receptor (TLR) agonists are highly promising as adjuvants in vaccines against life-threatening and complex diseases such as cancer, AIDS and malaria. TLRs are transmembrane receptors, which are predominantly expressed by innate immune cells. They can be classified into cell surface (TLR1, TLR2, TLR4, TLR5, TLR6) and intracellular TLRs (TLR3, TLR7, TLR8, TLR9), expressed on endosomal membranes. Besides a transmembrane domain, each TLR possesses a leucine-rich repeat (LRR) segment that mediates PAMP/DAMP recognition and a TIR domain that delivers the downstream signal transduction and initiates an inflammatory response. Thus, TLRs are excellent targets for adjuvants to provide a “danger” signal to induce an effective immune response that leads to long-lasting protection. The present review will elaborate on applications of TLR ligands as vaccine adjuvants and immunotherapeutic agents, with a focus on clinically relevant adjuvants.
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Citations
Cites
Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine
Polack, F. P., Thomas, S. J., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., Perez, J. L., Pérez Marc, G., Moreira, E. D., Zerbini, C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2034577 · Cited by 14,592
Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine
Baden, L. R., El Sahly, H. M., Essink, B., Kotloff, K., Frey, S., Novak, R., Diemert, D., Spector, S. A., Rouphael, N., Creech, C. B., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2035389 · Cited by 10,119
Defective LPS Signaling in C3H/HeJ and C57BL/10ScCr Mice: Mutations in Tlr4 Gene
Poltorak, A., He, X., Smirnova, I., Liu, M.-Y., Huffel, C. V., Du, X., Birdwell, D., Alejos, E., Silva, M., Galanos, C., et al. (1998) · Science · doi:10.1126/science.282.5396.2085 · Cited by 7,631
A Toll-like receptor recognizes bacterial DNA
Hemmi, H., Takeuchi, O., Kawai, T., Kaisho, T., Sato, S., Sanjo, H., Matsumoto, M., Hoshino, K., Wagner, H., Takeda, K., et al. (2000) · Nature · doi:10.1038/35047123 · Cited by 6,491 · cited 5×
“MGN1703 is a novel, synthetic, covalently closed DNA molecule, which activates the TLR9 signalling pathway.”
BackgroundInfluentialmRNA vaccines — a new era in vaccinology
Pardi, N., Hogan, M. J., Porter, F. W., & Weissman, D. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.243 · Cited by 4,667 · cited 2×
“The immunostimulatory feature of mRNA vaccines may provide an adjuvant activity to drive DC maturation, thus inducing resilient T and B cell responses [80].”
BackgroundHoshino, K., Takeuchi, O., Kawai, T., Sanjo, H., Ogawa, T., Takeda, Y., Takeda, K., & Akira, S. (1999) · The Journal of Immunology · doi:10.4049/jimmunol.162.7.3749 · Cited by 3,374
Toll-Like Receptor Signaling Pathways
Kawasaki, T., & Kawai, T. (2014) · Frontiers in Immunology · doi:10.3389/fimmu.2014.00461 · Cited by 3,233
RNA-Based COVID-19 Vaccine BNT162b2 Selected for a Pivotal Efficacy Study
Walsh, E. E., Frenck, R., Falsey, A. R., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., Neuzil, K., Mulligan, M. J., Bailey, R., et al. (2020) · doi:10.1101/2020.08.17.20176651 · Cited by 2,615
Small anti-viral compounds activate immune cells via the TLR7 MyD88–dependent signaling pathway
Hemmi, H., Kaisho, T., Takeuchi, O., Sato, S., Sanjo, H., Hoshino, K., Horiuchi, T., Tomizawa, H., Takeda, K., & Akira, S. (2002) · Nature Immunology · doi:10.1038/ni758 · Cited by 2,522
COVID-19 vaccine BNT162b1 elicits human antibody and TH1 T cell responses
Sahin, U., Muik, A., Derhovanessian, E., Vogler, I., Kranz, L. M., Vormehr, M., Baum, A., Pascal, K., Quandt, J., Maurus, D., et al. (2020) · Nature · doi:10.1038/s41586-020-2814-7 · Cited by 1,977
Cited by
Vaccine adjuvants: mechanisms and platforms
Zhao, T., Cai, Y., Jiang, Y., He, X., Wei, Y., Yu, Y., & Tian, X. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01557-7 · Cited by 1,020
Cullin, N., Azevedo Antunes, C., Straussman, R., Stein-Thoeringer, C. K., & Elinav, E. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.08.006 · Cited by 578
Recent Advances in Studying Toll-like Receptors with the Use of Computational Methods
Bzówka, M., Bagrowska, W., & Góra, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00419 · Cited by 34
Honorato, L., Bonilla, J. J. A., Valdez, A. F., Frases, S., Araújo, G. R. D. S., Sabino, A. L. R. D. N., da Silva, N. M., Ribeiro, L., Ferreira, M. D. S., Kornetz, J., et al. (2024) · mSphere · doi:10.1128/msphere.00467-24 · Cited by 28
Glenn, J. D., Negash, H., Henry, W., Qian, R., Liu, Y., Danos, O., Bruder, J. T., & Karumuthil-Melethil, S. (2024) · Cellular Immunology · doi:10.1016/j.cellimm.2024.104823 · Cited by 6
Metadata
- Authors
- Luchner, M., Reinke, S., & Milicic, A.
- Venue
- Pharmaceutics
- Publisher
- MDPI AG
- Published
- 2021-01-22
- Volume
- 13
- Issue
- 2
- Pages
- 142
- ISSN
- 1999-4923 (ISSN-L)
- OpenAlex
- W3122174179
- Cited by
- 285
- References
- 88
- FWCI
- 13.76
- Type
- journal article
- Language
- en
- Open access
- gold
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