Computational Design and Biopharmaceutical Evaluation of a Chimeric Nanoparticle Vaccine for Targeted Delivery against BK Polyomavirus.

PURPOSE: To address the unmet need for a prophylactic therapy against BK polyomavirus (BKPV) in transplant recipients, this study employed a mechanism-based computational approach to design a multi-epitope chimeric nanoparticle (CNP) vaccine, predicting its biopharmaceutical and immunological profile.

METHODS

A comprehensive computational biopharmaceutics pipeline was implemented. Conserved and immunodominant B-cell, helper T-lymphocyte (HTL), and cytotoxic T-lymphocyte (CTL) epitopes from all major BKPV proteins were identified using immunoinformatic tools. Epitopes were rigorously validated for antigenicity, safety, global population coverage, and cross-reactivity potential against JC polyomavirus. A multi-epitope construct, fused with immune-potentiating peptides, was designed and displayed on an Encapsulin nanocage via the SpyTag/SpyCatcher system to create a targeted delivery platform.

RESULTS

Molecular docking and dynamics simulations demonstrated stable, high-affinity binding of the vaccine construct and the CNP with Toll-like receptors 2 and 4, predicting effective innate immune recognition. In silico immune simulation forecasted robust and durable humoral and cellular immune responses upon administration.

CONCLUSIONS

This integrated computational study provides a mechanistic rationale for a novel CNP vaccine candidate against BKPV. The predicted stability, immunogenicity, and targeted delivery potential establish a strong foundation for subsequent in vitro and in vivo development, aligning with the translation of computational design into biopharmaceutical products.