Advancing poultry health: A meta-analysis of epitope-based and peptide-based vaccines against Avian Pathogenic E. coli with machine learning insights.

INTRODUCTION

Avian Pathogenic Escherichia coli (APEC) causes colibacillosis in poultry, which leads to tremendous economic losses. Traditional control methods, including antibiotics and conventional vaccines, are less effective due to the genetic diversity of APEC and developing antimicrobial resistance (AMR). Novel epitope- and peptide-based vaccines, supported by machine learning (ML), hold high promise.

OBJECTIVES

This meta-analysis and systematic review evaluated the effectiveness of epitope- and peptide-vaccine-based candidates against APEC-related morbidity and mortality, production factors, and AMR, and the use of ML in vaccine development.

MATERIALS AND

METHODS

Ten studies were included. Outcomes assessed were prevention of mortality, morbidity, immunogenicity, production performance, and reduction in AMR. The random-effects model was applied for meta-analysis, and the use of ML was summarized descriptively.

RESULTS

Vaccines prevent mortality (RR = 1.49; 95% CI: 1.30-1.68, p < 0.001, I2 = 5.55%) and morbidity (RR = 1.50; 95% CI: 0.64-2.35, p < 0.001, I2 = 49.55%) significantly. More sophisticated formulations, such as outer membrane vesicles (OMVs) and nanoparticle-conjugated platforms, induced substantial immune responses and cross-serotype protection. The available evidence showed variability, which needs further validation. The interventions may reduce bacterial load and, potentially, antibiotic consumption. ML provided exciting potential that may improve epitope prediction and delivery strategies.

CONCLUSION

Epitope- and peptide-vaccines showed significant but variable efficacy, while ML demonstrated promising potential in improving the control of APEC. Their utility needs to be established through large field trials and economic analysis.