Porcine reproductive and respiratory syndrome virus (PRRSV) remains one of the most complex disease challenges facing the swine industry. Because the virus evolves rapidly, producers and veterinarians must constantly evaluate how well vaccines match circulating strains. Traditionally, assessing vaccine immunogenicity against emerging PRRSV strains has been a time-consuming and resource-intensive process, often requiring months of laboratory and live-animal studies.
New research explores how artificial intelligence can help accelerate this process.1 Using AI-powered immunoinformatics tools, researchers analyzed how well a PRRSV vaccine aligns with the T cell epitopes of several circulating PRRSV challenge strains. These epitopes are small protein fragments that help trigger protective immune responses in pigs. By comparing viral protein sequences with vaccine components, the model can estimate how effectively a vaccine may stimulate cellular immunity against different strains.
The study combined computational modeling with live-animal research to evaluate predicted immune responses in vaccinated pigs. Results demonstrated that the AI-based modeling approach can provide valuable insight into potential cross-protection between vaccines and circulating PRRSV strains. In fact, this type of analysis can be completed in a matter of days or even hours—significantly faster than traditional methods that may take nine to twelve months.
Beyond improving speed, AI-driven immunoinformatics can help identify key immune “hotspots,” or T cell epitopes, that are most likely to trigger protective immune responses. This information can guide vaccine development toward regions of the virus that provide broader and more durable protection across multiple strains. As more viral sequences and field strains are incorporated into the model, its predictive capability is expected to improve further.
While vaccines remain an important component of PRRS management, they are only one piece of a broader herd health strategy that includes biosecurity, nutrition and overall management practices. Data-driven tools like AI modeling may help producers and veterinarians better understand how vaccines interact with evolving viral strains and make more informed decisions about disease control.
Ultimately, this research highlights the potential for innovative technologies to strengthen PRRSV management. In the future, AI-based tools may allow veterinarians and producers to analyze new PRRSV sequences quickly, helping predict vaccine coverage and supporting more responsive approaches to protecting swine herd health.
1J. M. Hammer, J. Risser, et al. 2025. “Estimating PRRSV immunogenicity to four challenge strains with an in-silico T cell model.” American Assoc. Of Swine Veterinarians. AASV. 56th Annual Meeting of the American Assoc. of Swine Veterinarians. 141-144. https://doi.org/10.54846/am2025/45
