Artificial intelligence in vaccine research and development: an umbrella review DOI Creative Commons
Rabie Adel El Arab,

Mansour Alkhunaizi,

Yousef N. Alhashem

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: May 8, 2025

Background The rapid development of COVID-19 vaccines highlighted the transformative potential artificial intelligence (AI) in modern vaccinology, accelerating timelines from years to months. Nevertheless, specific roles and effectiveness AI enhancing vaccine research, development, distribution, acceptance remain dispersed across various reviews, underscoring need for a unified synthesis. Methods We conducted an umbrella review consolidate evidence on AI’s contributions discovery, optimization, clinical testing, supply-chain logistics, public acceptance. Five databases were systematically searched up January 2025 systematic, scoping, narrative, as well meta-analyses explicitly focusing contexts. Quality assessments performed using ROBIS AMSTAR 2 tools evaluate risk bias methodological rigor. Results Among 27 traditional machine learning approaches—random forests, support vector machines, gradient boosting, logistic regression—dominated tasks antigen discovery epitope prediction supply‑chain optimization. Deep architectures, including convolutional recurrent neural networks, generative adversarial variational autoencoders, proved instrumental multiepitope design adaptive trial simulations. AI‑driven multi‑omic integration accelerated mapping, shrinking by months, while predictive analytics optimized manufacturing workflows operations (including temperature‑controlled, “cold‑chain” logistics). Sentiment analysis conversational demonstrated promising capabilities real‑time monitoring attitudes tailored communication address hesitancy. Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, ethical concerns over transparency equity. Discussion implications These findings illustrate lifecycle but underscore that translating promise into practice demands five targeted action areas: robust data governance multi‑omics consortia harmonize share high‑quality datasets; comprehensive frameworks featuring transparent model explainability, standardized performance metrics, interdisciplinary ethics committees ongoing oversight; adoption designs simulations enable safety silico process modeling; AI‑enhanced engagement strategies—such routinely audited chatbots, sentiment dashboards, culturally messaging—to mitigate hesitancy; concerted focus global equity pandemic preparedness through capacity building, digital infrastructure expansion, routine audits, sustained funding low‑resource settings. Conclusion This confirms pivotal role efficacy safety, bolstering Realizing these benefits requires not only investments stakeholder also documentation, oversight, audits. Moreover, bridging gap real‑world impact large‑scale validation studies methods can accommodate heterogeneous evidence, ensuring innovations deliver equitable health outcomes reinforce preparedness.

Language: Английский

Rational computational design and development of an immunogenic multiepitope vaccine incorporating transmembrane proteins of Fusobacterium necrophorum DOI Creative Commons
Muhammad Naveed,

Muhammad Toheed,

Tariq Aziz

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 4, 2025

Fusobacterium necrophorum is a Gram-negative, anaerobic pathogen responsible for Lemierre's syndrome, bovine foot rot, and other necrotizing infections. The rise in antimicrobial resistance the absence of effective vaccines underscore need alternative therapeutic strategies. This study employs computational biology to design multi-epitope vaccine targeting transmembrane proteins F. elicit strong immune responses. selected were evaluated toxicity, allergenicity, antigenicity, followed by epitope prediction screening. B T cell epitopes linked using immunogenic linkers, forming construct with VaxiJen score 0.7293 solubility 8.30 E. coli. Structural validation TrRosetta Ramachandran plots confirmed 97.4% residues favored regions, indicating high stability. Population coverage analysis indicated over 99% global applicability, further enhancing its potential impact. Docking studies revealed interactions receptors TLR7 TLR8. formed 12 hydrogen bonds, while TLR8(A) 9, TLR8(B) exhibited highest interaction, 13 bonds construct. Molecular dynamics simulations structural stability receptor engagement. RMSD stabilized around 4-5 Å, Vaccine-TLR8(B) complex. Radius Gyration remained 36 showing slight compaction time, RMSF peaked at 8-9 Å flexible lower fluctuations (1.5-2.5 Å) stable core regions. Principal component (PCA) identified elastic regions critical biological activity, energy levels (-5000 kJ/mol) reliability binding. Moreover, expression coli, as demonstrated SnapGene software pET-29a( +) vector. binding affinities predicted activation both humoral cellular responses, including increased IgM, IgG, cytokine levels. However, experimental necessary confirm safety efficacy, challenges manufacturing variable responses across populations must also be addressed.

Language: Английский

Citations

0
Artificial intelligence in vaccine research and development: an umbrella review DOI Creative Commons
Rabie Adel El Arab,

Mansour Alkhunaizi,

Yousef N. Alhashem

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: May 8, 2025

Background The rapid development of COVID-19 vaccines highlighted the transformative potential artificial intelligence (AI) in modern vaccinology, accelerating timelines from years to months. Nevertheless, specific roles and effectiveness AI enhancing vaccine research, development, distribution, acceptance remain dispersed across various reviews, underscoring need for a unified synthesis. Methods We conducted an umbrella review consolidate evidence on AI’s contributions discovery, optimization, clinical testing, supply-chain logistics, public acceptance. Five databases were systematically searched up January 2025 systematic, scoping, narrative, as well meta-analyses explicitly focusing contexts. Quality assessments performed using ROBIS AMSTAR 2 tools evaluate risk bias methodological rigor. Results Among 27 traditional machine learning approaches—random forests, support vector machines, gradient boosting, logistic regression—dominated tasks antigen discovery epitope prediction supply‑chain optimization. Deep architectures, including convolutional recurrent neural networks, generative adversarial variational autoencoders, proved instrumental multiepitope design adaptive trial simulations. AI‑driven multi‑omic integration accelerated mapping, shrinking by months, while predictive analytics optimized manufacturing workflows operations (including temperature‑controlled, “cold‑chain” logistics). Sentiment analysis conversational demonstrated promising capabilities real‑time monitoring attitudes tailored communication address hesitancy. Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, ethical concerns over transparency equity. Discussion implications These findings illustrate lifecycle but underscore that translating promise into practice demands five targeted action areas: robust data governance multi‑omics consortia harmonize share high‑quality datasets; comprehensive frameworks featuring transparent model explainability, standardized performance metrics, interdisciplinary ethics committees ongoing oversight; adoption designs simulations enable safety silico process modeling; AI‑enhanced engagement strategies—such routinely audited chatbots, sentiment dashboards, culturally messaging—to mitigate hesitancy; concerted focus global equity pandemic preparedness through capacity building, digital infrastructure expansion, routine audits, sustained funding low‑resource settings. Conclusion This confirms pivotal role efficacy safety, bolstering Realizing these benefits requires not only investments stakeholder also documentation, oversight, audits. Moreover, bridging gap real‑world impact large‑scale validation studies methods can accommodate heterogeneous evidence, ensuring innovations deliver equitable health outcomes reinforce preparedness.

Language: Английский

Citations

0