AI in the development of vaccines for emerging and re-emerging diseases
DOI:
https://doi.org/10.56294/saludcyt2024.1285Keywords:
Artificial intelligence, Vaccine development, Emerging diseases, Immunology, Genomic data ethicsAbstract
Introduction: The integration of artificial intelligence (AI) into vaccine development has revolutionized traditional methodologies, significantly enhancing the speed, precision, and scalability of immunological research. Emerging and re-emerging infectious diseases, driven by zoonotic spillovers, antimicrobial resistance, and global environmental changes, pose substantial challenges. Addressing these requires innovative approaches, with AI playing a pivotal role in advancing immunological solutions.
Development: AI applications in vaccinology include antigen detection, adjuvant optimization, and immune response simulation. Deep learning algorithms streamline the identification of immunogenic targets and conserved antigens, enabling vaccine development for highly mutable pathogens such as SARS-CoV-2, HIV, and influenza. Case studies demonstrate AI's transformative impact, including its role in the rapid creation of mRNA vaccines for COVID-19, identification of promising antigens for malaria, and enhanced efficacy of influenza vaccines through predictive modeling. However, challenges such as unequal access to technology, biases in data models, and ethical concerns regarding genomic data privacy persist. Recommendations to address these barriers include increasing data diversity, strengthening ethical frameworks, and investing in global infrastructure to democratize AI-driven innovations.
Conclusions: AI's ability to reduce time and cost, improve vaccine precision, and enable personalized immunization strategies positions it as a cornerstone of modern vaccinology. With continued advancements and equitable implementation, AI holds the potential to reshape vaccine development, improve pandemic preparedness, and address longstanding public health disparities globally.
References
1. Chernak ED, Influenza A, Coronavirus N. Evolving and emerging threats. Public Health Emergencies: Case Studies, Competencies, and Essential Services of Public Health. 2021;226.
2. Morens DM, Fauci AS. Emerging pandemic diseases: how we got to COVID-19. Cell. 2020;182(5):1077–92.
3. Ghattas M, Dwivedi G, Lavertu M, Alameh MG. Vaccine technologies and platforms for infectious diseases: Current progress, challenges, and opportunities. Vaccines (Basel). 2021;9(12):1490.
4. Gebre MS, Brito LA, Tostanoski LH, Edwards DK, Carfi A, Barouch DH. Novel approaches for vaccine development. Cell. 2021;184(6):1589–603.
5. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021;25:1315–60.
6. Katwaroo AR, Adesh VS, Lowtan A, Umakanthan S. The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine. Postgrad Med J. 2024;100(1183):289–96.
7. Farahani AF, Kasraei N. Evaluating the Impact of Artificial Intelligence on Vaccine Development: Lessons Learned from the COVID-19 Pandemic. medRxiv. 2024;2010–24.
8. Kannan S, Subbaram K, Faiyazuddin M. Artificial intelligence in vaccine development: Significance and challenges ahead. In: A Handbook of Artificial Intelligence in Drug Delivery. Elsevier; 2023. p. 467–86.
9. Thalange A V, Patil AR, Athavale VA. A Review of Artificial Intelligence and Machine Learning for Vaccine Research. In: The International Conference on Recent Innovations in Computing. Springer; 2023. p. 85–101.
10. Olawade DB, Teke J, Fapohunda O, Weerasinghe K, Usman SO, Ige AO, et al. Leveraging artificial intelligence in vaccine development: A narrative review. J Microbiol Methods. 2024;106998.
11. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.
12. Lawrence P, Heung M, Nave J, Henkel C, Escudero-Pérez B. The natural virome and pandemic potential: Disease X. Curr Opin Virol. 2023;63:101377.
13. Paul J. Introduction to Infectious Diseases. In: Disease Causing Microbes. Springer; 2024. p. 1–63.
14. Sánchez CA, Venkatachalam‐Vaz J, Drake JM. Spillover of zoonotic pathogens: A review of reviews. Zoonoses Public Health. 2021;68(6):563–77.
15. Rodriguez-Morales AJ, Paniz-Mondolfi AE, Faccini-Martínez ÁA, Henao-Martínez AF, Ruiz-Saenz J, Martinez-Gutierrez M, et al. The constant threat of zoonotic and vector-borne emerging tropical diseases: living on the edge. Vol. 2, Frontiers in tropical diseases. Frontiers Media SA; 2021. p. 676905.
16. Spencer JNH, Marasco D, Eichinger M. Planning for Emerging Infectious Disease Pandemics: Definitions, the role of planners, and learning from the avian influenza outbreak of 2004–2005. Journal of the American planning association. 2022;88(1):113–26.
17. Bhanye AS, Bhanye JI. Urban health in the 21st century: Exploring innovative measures to reduce the spread of communicable diseases. Developments in Environmental Science. 2024;15:503–27.
18. Wainaina M, Wasonga J, Cook EAJ. Epidemiology of human and animal leptospirosis in Kenya: A systematic review and meta-analysis of disease occurrence, serogroup diversity and risk factors. PLoS Negl Trop Dis. 2024;18(9):e0012527.
19. Kaslow RA, Bell DM. Epidemiology and Control: From Principles to Pandemics. In: Viral Infections of Humans: Epidemiology and Control. Springer; 2022. p. 1–80.
20. Matić Z, Šantak M. Current view on novel vaccine technologies to combat human infectious diseases. Appl Microbiol Biotechnol. 2022;106:25–56.
21. Meganck RM, Baric RS. Developing therapeutic approaches for twenty-first-century emerging infectious viral diseases. Nat Med. 2021;27(3):401–10.
22. Lurie N, Saville M, Hatchett R, Halton J. Developing Covid-19 vaccines at pandemic speed. New England journal of medicine. 2020;382(21):1969–73.
23. Chowdhury K, Ahmad R, Sinha S, Dutta S, Haque M. Multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) among children: Where we stand now. Cureus. 2023;15(2).
24. Emane AKA, Guo X, Takiff HE, Liu S. Highly transmitted M. tuberculosis strains are more likely to evolve MDR/XDR and cause outbreaks, but what makes them highly transmitted? Tuberculosis. 2021;129:102092.
25. Sun-Waterhouse DX, Chen XY, Liu ZH, Waterhouse GI, Kang WY. Transformation from traditional medicine-food homology to modern food-medicine homology. Food & Medicine Homology. 2024;1(1):9420014.
26. Redford KH, Adams WM. Strange natures: Conservation in the era of synthetic biology. Yale University Press; 2021.
27. Khan M, Adil SF, Alkhathlan HZ, Tahir MN, Saif S, Khan M, et al. COVID-19: a global challenge with old history, epidemiology and progress so far. Molecules. 2020;26(1):39.
28. Vecchio R, Gentile L, Tafuri S, Costantino C, Odone A. Exploring future perspectives and pipeline progression in vaccine research and development. Ann Ig. 2024;36(4):446–61.
29. Sharma O, Sultan AA, Ding H, Triggle CR. A Review of the Progress and Challenges of Developing a Vaccine for COVID-19. Front Immunol. 2020;11:585354.
30. Zhuang L, Ye Z, Li L, Yang L, Gong W. Next-generation TB vaccines: progress, challenges, and prospects. Vaccines (Basel). 2023;11(8):1304.
31. Patel MN, Patel AJ, Nandpal MN, Raval MA, Patel RJ, Patel AA, et al. Advancing against drug-resistant tuberculosis: an extensive review, novel strategies and patent landscape. Naunyn Schmiedebergs Arch Pharmacol. 2024;1–24.
32. Hosseinian SA, Hajati MH. A comprehensive review of the zoonotic potential of avian influenza viruses: a globally circulating threat to pandemic influenza in human. Journal of Zoonotic Diseases. 2024;
33. Gangopadhayya A, Bhukya PL. Factors Contributing to the Emergence of Viral Diseases. In: Emerging Human Viral Diseases, Volume I: Respiratory and Haemorrhagic Fever. Springer; 2023. p. 3–69.
34. Callaway E. Delta coronavirus variant: scientists brace for impact. Nature. 2021;595(7865):17–8.
35. Rubinstein M, Makhon A, Losev Y, Valenci GZ, Gatt YE, Margalit H, et al. Prolonged survival of a patient with active MDR-TB HIV co-morbidity: insights from a Mycobacterium tuberculosis strain with a unique genomic deletion. Front Med (Lausanne). 2023;10:1292665.
36. Abhinand CS, Prabhakaran AA, Krishnamurthy A, Raju R, Keshava Prasad TS, Nair AS, et al. SARS-CoV-2 variants infectivity prediction and therapeutic peptide design using computational approaches. J Biomol Struct Dyn. 2023;41(20):11166–77.
37. Ghosh A, Larrondo-Petrie MM, Pavlovic M. Revolutionizing vaccine development for COVID-19: a review of AI-based approaches. Information. 2023;14(12):665.
38. ElSherif M, Halperin SA. Benefits of combining Molecular Biology and Controlled Human Infection Model methodologies in advancing Vaccine Development. J Mol Biol. 2023;168322.
39. Hodgson J. The pandemic pipeline. Nat Biotechnol. 2020;38(5):523–32.
40. Ng’uni T, Chasara C, Ndhlovu ZM. Major scientific hurdles in HIV vaccine development: historical perspective and future directions. Front Immunol. 2020;11:590780.
41. Feldmann-Jensen S, DPPD MPH, O’Sullivan T. Informing Adaptation with Lessons Learned from Key 21st Century Infectious Disease Outbreaks. Current and Emerging Trends in the Management of International Disasters. 2024;
42. Ding C, Liu X, Yang S. The value of infectious disease modeling and trend assessment: a public health perspective. Expert Rev Anti Infect Ther. 2021;19(9):1135–45.
43. Sarley D, Hwang A, Hall BF, Ford A, Giersing B, Kaslow DC, et al. Accelerating access for all through research and innovation in immunization: Recommendations from Strategic Priority 7 of the Immunization Agenda 2030. Vaccine. 2022;
44. Aribi M. Development: From Traditional to Modern Approaches. New Topics in Vaccine Development. 2024;3.
45. Van Tilbeurgh M, Lemdani K, Beignon AS, Chapon C, Tchitchek N, Cheraitia L, et al. Predictive markers of immunogenicity and efficacy for human vaccines. Vaccines (Basel). 2021;9(6):579.
46. Zhang WY, Zheng XL, Coghi PS, Chen JH, Dong BJ, Fan XX. Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines. Front Immunol. 2024;15:1438030.
47. Ananya, Panchariya DC, Karthic A, Singh SP, Mani A, Chawade A, et al. Vaccine design and development: Exploring the interface with computational biology and AI. Int Rev Immunol. 2024;1–20.
48. Zhao AP, Li S, Cao Z, Hu PJH, Wang J, Xiang Y, et al. AI for science: predicting infectious diseases. Journal of Safety Science and Resilience. 2024;
49. Vashisht V, Vashisht A, Mondal AK, Farmaha J, Alptekin A, Singh H, et al. Genomics for emerging pathogen identification and monitoring: Prospects and obstacles. BioMedInformatics. 2023;3(4):1145–77.
50. Farzan R. Artificial intelligence in Immuno-genetics. Bioinformation. 2024;20(1):29.
51. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol. 2020;9(2):14.
52. De Groot AS, Moise L, Terry F, Gutierrez AH, Hindocha P, Richard G, et al. Better epitope discovery, precision immune engineering, and accelerated vaccine design using immunoinformatics tools. Front Immunol. 2020;11:442.
53. Parvizpour S, Pourseif MM, Razmara J, Rafi MA, Omidi Y. Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches. Drug Discov Today. 2020;25(6):1034–42.
54. Khan MA, Amin A, Farid A, Ullah A, Waris A, Shinwari K, et al. Recent advances in genomics-based approaches for the development of intracellular bacterial pathogen vaccines. Pharmaceutics. 2022;15(1):152.
55. Bhattacharjee B, Bezbaruah R, Rynjah D, Newar A, Valu D, Ahmed N, et al. Proteogenomics and immunopeptidomics in the development of advanced vaccines. In: Advanced Vaccination Technologies for Infectious and Chronic Diseases. Elsevier; 2024. p. 455–75.
56. Angaitkar P, Janghel RR, Sahu TP. DL-TCNN: Deep Learning-based Temporal Convolutional Neural Network for prediction of conformational B-cell epitopes. 3 Biotech. 2023;13(9):297.
57. Dhanushkumar T, Santhosh ME, Selvam PK, Rambabu M, Dasegowda KR, Vasudevan K, et al. Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies. Life Sci. 2023;122360.
58. Liu Y, Ouyang X hui, Xiao ZX, Zhang L, Cao Y. A review on the methods of peptide-MHC binding prediction. Curr Bioinform. 2020;15(8):878–88.
59. Zhang Y, Mastouri M, Zhang Y. Accelerating drug discovery, development, and clinical trials by artificial intelligence. Med. 2024;
60. Bukhari SNH, Jain A, Haq E, Mehbodniya A, Webber J. Machine learning techniques for the prediction of B-cell and T-cell epitopes as potential vaccine targets with a specific focus on SARS-CoV-2 pathogen: A review. Pathogens. 2022;11(2):146.
61. Premkumar L, Segovia-Chumbez B, Jadi R, Martinez DR, Raut R, Markmann AJ, et al. The receptor-binding domain of the viral spike protein is an immunodominant and highly specific target of antibodies in SARS-CoV-2 patients. Sci Immunol. 2020;5(48):eabc8413.
62. Kowalzik F, Schreiner D, Jensen C, Teschner D, Gehring S, Zepp F. mRNA-based vaccines. Vaccines (Basel). 2021;9(4):390.
63. Bharadwaj KK, Srivastava A, Panda MK, Singh YD, Maharana R, Mandal K, et al. Computational intelligence in vaccine design against COVID-19. Computational intelligence methods in COVID-19: surveillance, prevention, prediction and diagnosis. 2021;311–29.
64. Alafif T, Tehame AM, Bajaba S, Barnawi A, Zia S. Machine and deep learning towards COVID-19 diagnosis and treatment: survey, challenges, and future directions. Int J Environ Res Public Health. 2021;18(3):1117.
65. Bastola R, Noh G, Keum T, Bashyal S, Seo JE, Choi J, et al. Vaccine adjuvants: smart components to boost the immune system. Arch Pharm Res. 2017;40:1238–48.
66. Zhao T, Cai Y, Jiang Y, He X, Wei Y, Yu Y, et al. Vaccine adjuvants: mechanisms and platforms. Signal Transduct Target Ther. 2023;8(1):283.
67. Elhassan Taha MM, Abdelwahab SI, Moni SS, Farasani A, Aljahdali IA, Oraibi B, et al. Nano-enhanced immunity: A bibliometric analysis of nanoparticles in vaccine adjuvant research. Hum Vaccin Immunother. 2024;20(1):2427464.
68. Kardani K, Bolhassani A, Namvar A. An overview of in silico vaccine design against different pathogens and cancer. Expert Rev Vaccines. 2020;19(8):699–726.
69. Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines. 2024;9(1):15.
70. Chowell D, Yoo SK, Valero C, Pastore A, Krishna C, Lee M, et al. Improved prediction of immune checkpoint blockade efficacy across multiple cancer types. Nat Biotechnol. 2022;40(4):499–506.
71. Moni SS, Abdelwahab SI, Jabeen A, Elmobark ME, Aqaili D, Gohal G, et al. Advancements in vaccine adjuvants: The journey from alum to nano formulations. Vaccines (Basel). 2023;11(11):1704.
72. Pulendran B, S. Arunachalam P, O’Hagan DT. Emerging concepts in the science of vaccine adjuvants. Nat Rev Drug Discov. 2021;20(6):454–75.
73. Xu J, Liu W, Fan F, Zhang B, Sun C, Hu Y. Advances in nano-immunotherapy for hematological malignancies. Exp Hematol Oncol. 2024;13(1):57.
74. Pasupuleti D, Bagwe P, Ferguson A, Uddin MN, D’Souza MJ, Zughaier SM. Evaluating Nanoparticulate Vaccine Formulations for Effective Antigen Presentation and T-Cell Proliferation Using an In Vitro Overlay Assay. Vaccines (Basel). 2024;12(9):1049.
75. Russo G, Reche P, Pennisi M, Pappalardo F. The combination of artificial intelligence and systems biology for intelligent vaccine design. Expert Opin Drug Discov. 2020;15(11):1267–81.
76. Shin B, An G, Cockrell RC. Examining B-cell dynamics and responsiveness in different inflammatory milieus using an agent-based model. PLoS Comput Biol. 2024;20(1):e1011776.
77. Jamali Y. Modeling the Immune System Through Agent-based Modeling: A Mini-review. Immunoregulation. 2024;6(1):3–12.
78. Handel A, La Gruta NL, Thomas PG. Simulation modelling for immunologists. Nat Rev Immunol. 2020;20(3):186–95.
79. Hayawi K, Shahriar S, Alashwal H, Serhani MA. Generative AI and Large Language Models: A New Frontier in Reverse Vaccinology. Inform Med Unlocked. 2024;101533.
80. Chavda VP, Ghali ENHK, Balar PC, Chauhan SC, Tiwari N, Shukla S, et al. Protein subunit vaccines: Promising frontiers against COVID-19. Journal of Controlled Release. 2024;366:761–82.
81. Bali A, Bali N. Role of artificial intelligence in fast-track drug discovery and vaccine development for COVID-19. In: Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19. Elsevier; 2022. p. 201–29.
82. Gao W, Liu J, Shtylla B, Venkatakrishnan K, Yin D, Shah M, et al. Realizing the promise of project optimus: challenges and emerging opportunities for dose optimization in oncology drug development. CPT Pharmacometrics Syst Pharmacol. 2024;13(5):691–709.
83. Poweleit EA, Vinks AA, Mizuno T. Artificial intelligence and machine learning approaches to facilitate therapeutic drug management and model-informed precision dosing. Ther Drug Monit. 2023;45(2):143–50.
84. Liu M, Li Q, Lin J, Lin Y, Hoffman E. Innovative trial designs and analyses for vaccine clinical development. Contemp Clin Trials. 2021;100:106225.
85. Chen X, He R, Chen X, Jiang L, Wang F. Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges. Front Pharmacol. 2023;14:1261312.
86. Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput. 2023;19(16):5315–33.
87. Ortiz JPH, Osorio JE. One Health and Engineering: using engineering to further pave the roadmap towards global health security, pandemic preparedness, and personalized medicine. DYNA: revista de la Facultad de Minas Universidad Nacional de Colombia Sede Medellín. 2023;90(230):22–8.
88. Danchin A. Artificial intelligence‐based prediction of pathogen emergence and evolution in the world of synthetic biology. Microb Biotechnol. 2024;17(10):e70014.
89. Domingo E, García-Crespo C, Lobo-Vega R, Perales C. Mutation rates, mutation frequencies, and proofreading-repair activities in RNA virus genetics. Viruses. 2021;13(9):1882.
90. Kwok AJ, Mentzer A, Knight JC. Host genetics and infectious disease: new tools, insights and translational opportunities. Nat Rev Genet. 2021;22(3):137–53.
91. Al-Amran FG, Hezam AM, Rawaf S, Yousif MG. Genomic Analysis and Artificial Intelligence: Predicting Viral Mutations and Future Pandemics. arXiv preprint arXiv:230915936. 2023;
92. Nawaz MS, Fournier-Viger P, Shojaee A, Fujita H. Using artificial intelligence techniques for COVID-19 genome analysis. Applied Intelligence. 2021;51:3086–103.
93. Mohanty E, Mohanty A. Role of artificial intelligence in peptide vaccine design against RNA viruses. Inform Med Unlocked. 2021;26:100768.
94. Bernasconi A. The opportunity of data-driven services for viral genomic surveillance. In: 2023 IEEE International Conference on Service-Oriented System Engineering (SOSE). IEEE; 2023. p. 172–81.
95. Wei CJ, Crank MC, Shiver J, Graham BS, Mascola JR, Nabel GJ. Next-generation influenza vaccines: opportunities and challenges. Nat Rev Drug Discov. 2020;19(4):239–52.
96. Dănăilă VR, Avram S, Buiu C. The applications of machine learning in HIV neutralizing antibodies research—A systematic review. Artif Intell Med. 2022;134:102429.
97. Hederman AP, Ackerman ME. Leveraging deep learning to improve vaccine design. Trends Immunol. 2023;44(5):333–44.
98. Irvine EB, Reddy ST. Advancing antibody engineering through synthetic evolution and machine learning. The Journal of Immunology. 2024;212(2):235–43.
99. Elste J, Saini A, Mejia-Alvarez R, Mejía A, Millán-Pacheco C, Swanson-Mungerson M, et al. Significance of Artificial Intelligence in the Study of Virus–Host Cell Interactions. Biomolecules. 2024;14(8):911.
100. Ghosh A, Pavlovic M, Larrondo-Petrie MM. Rational Vaccine Design for SARS-CoV-2 Virus—A Systematic Review. 2023;
101. Taft JM, Weber CR, Gao B, Ehling RA, Han J, Frei L, et al. Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. Cell. 2022;185(21):4008–22.
102. Scarpa F, Casu M. Genomics and Bioinformatics in One Health: Transdisciplinary Approaches for Health Promotion and Disease Prevention. Int J Environ Res Public Health. 2024;21(10):1337.
103. Ling-Hu T, Rios-Guzman E, Lorenzo-Redondo R, Ozer EA, Hultquist JF. Challenges and opportunities for global genomic surveillance strategies in the COVID-19 era. Viruses. 2022;14(11):2532.
104. Padhi A, Agarwal A, Saxena SK, Katoch CDS. Transforming clinical virology with AI, machine learning and deep learning: a comprehensive review and outlook. Virusdisease. 2023;34(3):345–55.
105. Bedi R, Bayless NL, Glanville J. Challenges and progress in designing broad-spectrum vaccines against rapidly mutating viruses. Annu Rev Biomed Data Sci. 2023;6(1):419–41.
106. Aminu RF, Emurotu MO, Mofolorunsho KC. Applications of Molecular Phylogeny in Disease Diagnosis. Int J Res Virol. 2024;1(1):1–8.
107. Ochsenreiter RW. Algorithmic Approaches on Structural RNA Evolution in Viruses. 2023;
108. Kushwaha SK, Kesarwani V, Choudhury S, Gandhi S, Sharma S. SARS-CoV-2 transcriptome analysis and molecular cataloguing of immunodominant epitopes for multi-epitope based vaccine design. Genomics. 2020;112(6):5044–54.
109. Rastogi A, Gautam S, Kumar M. Bioinformatic elucidation of conserved epitopes to design a potential vaccine candidate against existing and emerging SARS-CoV-2 variants of concern. Heliyon. 2024;10(15).
110. Giurgea LT, Morens DM, Taubenberger JK, Memoli MJ. Influenza neuraminidase: a neglected protein and its potential for a better influenza vaccine. Vaccines (Basel). 2020;8(3):409.
111. Biswas A, Chakrabarti AK, Dutta S. Current challenges: from the path of “original antigenic sin” towards the development of universal flu vaccines: flu vaccine efficacy encounters significant hurdles from pre-existing immunity of the host suggesting assessment of host immunity before vaccination. Int Rev Immunol. 2020;39(1):21–36.
112. Mahomed S. Broadly neutralizing antibodies for HIV prevention: a comprehensive review and future perspectives. Clin Microbiol Rev. 2024;e00152-22.
113. Brisse M, Vrba SM, Kirk N, Liang Y, Ly H. Emerging concepts and technologies in vaccine development. Front Immunol. 2020;11:583077.
114. Prabhod KJ. The Role of Machine Learning in Genomic Medicine: Advancements in Disease Prediction and Treatment. Journal of Deep Learning in Genomic Data Analysis. 2022;2(1):1–52.
115. Montesinos-López OA, Montesinos-López A, Pérez-Rodríguez P, Barrón-López JA, Martini JWR, Fajardo-Flores SB, et al. A review of deep learning applications for genomic selection. BMC Genomics. 2021;22:1–23.
116. Loewa A, Feng JJ, Hedtrich S. Human disease models in drug development. Nature reviews bioengineering. 2023;1(8):545–59.
117. Baquero F, Martinez JL, F. Lanza V, Rodríguez-Beltrán J, Galán JC, San Millán A, et al. Evolutionary pathways and trajectories in antibiotic resistance. Clin Microbiol Rev. 2021;34(4):e00050-19.
118. Williams D, Hornung H, Nadimpalli A, Peery A. Deep learning and its application for healthcare delivery in low and middle income countries. Front Artif Intell. 2021;4:553987.
119. Naseem M, Akhund R, Arshad H, Ibrahim MT. Exploring the potential of artificial intelligence and machine learning to combat COVID-19 and existing opportunities for LMIC: a scoping review. J Prim Care Community Health. 2020;11:2150132720963634.
120. Ebulue CC, Ekkeh OV, Ebulue OR, Ekesiobi CS. Leveraging machine learning for vaccine distribution in resource-limited settings: A synthesis of approaches. International Medical Science Research Journal. 2024;4(5):544–57.
121. Srivastava V, Kumar R, Wani MY, Robinson K, Ahmad A. Role of artificial intelligence in early diagnosis and treatment of infectious diseases. Infect Dis. 2024;1–26.
122. Abbasi N, Nizamullah FNU, Zeb S. AI IN HEALTHCARE: USING CUTTING-EDGE TECHNOLOGIES TO REVOLUTIONIZE VACCINE DEVELOPMENT AND DISTRIBUTION. JURIHUM: Jurnal Inovasi dan Humaniora. 2023;1(1):17–29.
123. Prajapati RN, Bhushan B, Singh K, Chopra H, Kumar S, Agrawal M, et al. Recent Advances in Pharmaceutical Design: Unleashing the Potential of Novel Therapeutics. Curr Pharm Biotechnol. 2024;25(16):2060–77.
124. Khuat TT, Bassett R, Otte E, Grevis-James A, Gabrys B. Applications of machine learning in biopharmaceutical process development and manufacturing: Current trends, challenges, and opportunities. arXiv preprint arXiv:231009991. 2023;
125. Sharma A, Virmani T, Pathak V, Sharma A, Pathak K, Kumar G, et al. Artificial intelligence‐based data‐driven strategy to accelerate research, development, and clinical trials of COVID vaccine. Biomed Res Int. 2022;2022(1):7205241.
126. Rappuoli R, Alter G, Pulendran B. Transforming vaccinology. Cell. 2024;187(19):5171–94.
127. Pertseva M, Gao B, Neumeier D, Yermanos A, Reddy ST. Applications of machine and deep learning in adaptive immunity. Annu Rev Chem Biomol Eng. 2021;12(1):39–62.
128. Bansal H, Aggarwal N. 19 Artificial Techniques Intelligenceto Design Epitope-Mapped. Handbook of AI-Based Models in Healthcare and Medicine: Approaches, Theories, and Applications. 2024;378.
129. Bansal H, Aggarwal N. Artificial Intelligence Techniques to Design Epitope-Mapped Vaccines and Diagnostics for Emerging Pathogens. In: Handbook of AI-Based Models in Healthcare and Medicine. CRC Press; p. 378–96.
130. Malone B, Simovski B, Moliné C, Cheng J, Gheorghe M, Fontenelle H, et al. Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs. Sci Rep. 2020;10(1):22375.
131. Slieker RC, Warmerdam DO, Vermeer MH, van Doorn R, Heemskerk MHM, Scheeren FA. Reassessing human MHC-I genetic diversity in T cell studies. Sci Rep. 2024;14(1):7966.
132. Deng Z. Pandemic-Resilient Investment: Sustainable Knowledge Infrastructure for Medical AI. Journal of the Knowledge Economy. 2024;1–24.
133. Pillai AS. Artificial Intelligence in Healthcare Systems of Low-and Middle-Income Countries: Requirements, Gaps, Challenges, and Potential Strategies. International Journal of Applied Health Care Analytics. 2023;8(3):19–33.
134. Gleeson D, Townsend B, Tenni BF, Phillips T. Global inequities in access to COVID-19 health products and technologies: a political economy analysis. Health Place. 2023;83:103051.
135. Ritoré Á, Jiménez CM, González JL, Rejón-Parrilla JC, Hervás P, Toro E, et al. The role of Open Access Data in democratizing healthcare AI: A pathway to research enhancement, patient well-being and treatment equity in Andalusia, Spain. PLOS Digital Health. 2024;3(9):e0000599.
136. Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: A call for open science. Patterns. 2021;2(10).
137. Schleiss MR, Crooks CM, Karthigeyan KP, Kruc RM, Otero CE, Wang HY, et al. Proceedings of the Conference “CMV Vaccine Development—How Close Are We?”(27–28 September 2023). MDPI; 2024.
138. Jean-Jacques M, Bauchner H. Vaccine distribution—equity left behind? JAMA. 2021;325(9):829–30.
139. Topol E. Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK; 2019.
140. Balagurunathan Y, Sethuraman RR. An Analysis of Ethics-Based Foundation and Regulatory Issues for Genomic Data Privacy. Journal of The Institution of Engineers (India): Series B. 2024;1–11.
141. Adekugbe AP, Ibeh CV. Navigating ethical challenges in data management for US program development: best practices and recommendations. International Journal of Management & Entrepreneurship Research. 2024;6(4):1023–33.
142. Kaushik R, Kant R, Christodoulides M. Artificial intelligence in accelerating vaccine development-current and future perspectives. Frontiers in Bacteriology. 2023;2:1258159.
143. Oyeniran CO, Adewusi AO, Adeleke AG, Akwawa LA, Azubuko CF. Ethical AI: Addressing bias in machine learning models and software applications. Computer Science & IT Research Journal. 2022;3(3):115–26.
144. Agampodi S, Mogeni OD, Chandler R, Pansuriya M, Kim JH, Excler JL. Global pandemic preparedness: learning from the COVID-19 vaccine development and distribution. Expert Rev Vaccines. 2024;23(1):761–72.
145. Chen J, Li K, Zhang Z, Li K, Yu PS. A survey on applications of artificial intelligence in fighting against COVID-19. ACM Computing Surveys (CSUR). 2021;54(8):1–32.
146. Casalino L, Dommer AC, Gaieb Z, Barros EP, Sztain T, Ahn SH, et al. AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. Int J High Perform Comput Appl. 2021;35(5):432–51.
147. Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial intelligence in surveillance, diagnosis, drug discovery and vaccine development against COVID-19. Pathogens. 2021;10(8):1048.
148. Nejat R, Torshizi MF, Najafi DJ. S protein, ACE2 and host cell proteases in SARS-CoV-2 cell entry and infectivity; is soluble ACE2 a two blade sword? A narrative review. Vaccines (Basel). 2023;11(2):204.
149. Sanyal D, Banerjee S, Bej A, Chowdhury VR, Uversky VN, Chowdhury S, et al. An integrated understanding of the evolutionary and structural features of the SARS-CoV-2 spike receptor binding domain (RBD). Int J Biol Macromol. 2022;217:492–505.
150. Gote V, Bolla PK, Kommineni N, Butreddy A, Nukala PK, Palakurthi SS, et al. A comprehensive review of mRNA vaccines. Int J Mol Sci. 2023;24(3):2700.
151. Macarayan E, Papanicolas I, Jha A. The quality of malaria care in 25 low-income and middle-income countries. BMJ Glob Health. 2020;5(2):e002023.
152. Rajneesh, Tiwari R, Singh VK, Kumar A, Gupta RP, Singh AK, et al. Advancements and challenges in developing malaria vaccines: Targeting multiple stages of the parasite life cycle. ACS Infect Dis. 2023;9(10):1795–814.
153. Wong F, de la Fuente-Nunez C, Collins JJ. Leveraging artificial intelligence in the fight against infectious diseases. Science (1979). 2023;381(6654):164–70.
154. Musundi SD, Gitaka J, Kanoi BN. Identification of conserved cross-species B-cell linear epitopes in human malaria: A subtractive proteomics and immuno-informatics approach targeting merozoite stage proteins. Front Immunol. 2024;15:1352618.
155. Taddese S. Status in Malaria Vaccine Development: Basic aspects of Vaccine, Mechanism of actions, Vaccine pipelines, Stage oriented immune response ‘Challenges and Opportunities.’ Microbial journal. 2023;3(1).
156. Malik S, Waheed Y. Recent advances on vaccines against malaria: A review. Asian Pac J Trop Med. 2024;17(4):143–59.
157. Duffy PE. Current approaches to malaria vaccines. Curr Opin Microbiol. 2022;70:102227.
158. Ali ST, Cowling BJ. Influenza virus: tracking, predicting, and forecasting. Annu Rev Public Health. 2021;42(1):43–57.
159. Luczo JM, Spackman E. Epitopes in the HA and NA of H5 and H7 avian influenza viruses that are important for antigenic drift. FEMS Microbiol Rev. 2024;48(3).
160. Russell CA, Fouchier RAM, Ghaswalla P, Park Y, Vicic N, Ananworanich J, et al. Seasonal influenza vaccine performance and the potential benefits of mRNA vaccines. Hum Vaccin Immunother. 2024;20(1):2336357.
161. Rcheulishvili N, Mao J, Papukashvili D, Liu C, Wang Z, Zhao J, et al. Designing multi-epitope mRNA construct as a universal influenza vaccine candidate for future epidemic/pandemic preparedness. Int J Biol Macromol. 2023;226:885–99.
162. Sparrow E, Wood JG, Chadwick C, Newall AT, Torvaldsen S, Moen A, et al. Global production capacity of seasonal and pandemic influenza vaccines in 2019. Vaccine. 2021;39(3):512–20.
163. Montero DA, Vidal RM, Velasco J, Carreño LJ, Torres JP, Benachi O MA, et al. Two centuries of vaccination: historical and conceptual approach and future perspectives. Front Public Health. 2024;11:1326154.
164. Lu G, Shan S, Zainab B, Ayaz Z, He J, Xie Z, et al. Novel vaccine design based on genomics data analysis: A review. Scand J Immunol. 2021;93(3):e12986.
165. Creytens S, Pascha MN, Ballegeer M, Saelens X, de Haan CAM. Influenza neuraminidase characteristics and potential as a vaccine target. Front Immunol. 2021;12:786617.
166. Mandala WL, Harawa V, Dzinjalamala F, Tembo D. The role of different components of the immune system against Plasmodium falciparum malaria: Possible contribution towards malaria vaccine development. Mol Biochem Parasitol. 2021;246:111425.
167. Matsuzaka Y, Yashiro R. Understanding and Therapeutic Application of Immune Response in Major Histocompatibility Complex (MHC) Diversity Using Multimodal Artificial Intelligence. BioMedInformatics. 2024;4(3):1835–64.
168. Munagandla VB, Dandyala SSV, Vadde BC. AI-Powered Cloud-Based Epidemic Surveillance System: A Framework for Early Detection. Revista de Inteligencia Artificial en Medicina. 2024;15(1):673–90.
169. Bag AK, Sengupta D. Computational frameworks for zoonotic disease control in Society 5.0: opportunities, challenges and future research directions. AI Soc. 2024;1–30.
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Copyright (c) 2024 Diana Catalina Velastegui-Hernandez, Luis Felipe Contreras-Vásquez, Gabriela Sandoval, Andrea Alexandra Tufiño-Aguilar, Andrea Carolina Cevallos-Teneda, Estefania Araceli Reyes-Rosero, Verónica Gabriela Salinas-Velastegui, Rita Elizabeth Velastegui-Hernández, Fabricio Alejandro Vasquez de la Bandera, Luis Fabián Salazar-Garcés (Author)
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