Title : Integrating next-generation vaccine technologies, immunoinformatic, and public health policy for rapid disease control: Insights from India
Abstract:
The rapid rise of emerging pathogens highlights the critical need for accelerated vaccine design approaches that integrate computational vaccinology, advanced immunoinformatic and scalable production platforms. Drawing from India’s successful COVID-19 response and polio eradication programs, we propose an integrated framework that leverages reverse vaccinology, AI-driven epitope prediction, and in silico immunogenicity modeling to streamline the preclinical development process. In a case study using a hypothetical zoonotic virus, the complete proteome was analyzed using the IEDB and NetMHCpan pipelines, yielding candidate B- and T-cell epitopes with 87% predicted HLA population coverage. Epitope selection was narrowed down from over 150 peptides to 12 high-confidence epitopes, each demonstrating antigenicity scores above 0.8 in the VaxiJen analysis. Machine learning–based clustering and immune escape prediction eliminated redundant sequences, reducing the wet-lab validation requirements by 60%. These epitopes were subsequently mapped onto mRNA and nanoparticle vaccine platforms, with in silico simulations forecasting a 2.5-fold increase in IFN-γ responses compared to the baseline viral antigens. Simulated bioprocessing models further projected scalable yields of 5×10¹³ particles per 200-L fermenter run, with predicted thermostability exceeding 18 months at 2–8 °C. Policy integration scenarios, modeled on India’s polio campaign, emphasize cold-chain logistics and equitable distribution in resource-limited settings. Together, this AI-enhanced immunoinformatic framework demonstrates the potential to compress vaccine development timelines, maximize population coverage, and ensure scalable and equitable deployment, contributing to global pandemic preparedness and neglected tropical disease (NTD) vaccine innovation.
Keywords: next-generation vaccines, reverse vaccinology, immunoinformatic, AI/ML in vaccinology, scalable bioprocessing, population coverage, pandemic preparedness
