A Minimal Framework for Optimizing Vaccination Protocols Targeting Highly Mutable Pathogens

TL;DR

This paper presents a minimal theoretical framework to optimize vaccination protocols against highly mutable pathogens by modeling affinity maturation and guiding the design of selection forces to maximize broadly neutralizing antibody production.

Contribution

It introduces a simplified, analytically tractable model of affinity maturation to determine optimal vaccination strategies for inducing broad immunity.

Findings

Analytical mean-field results align with stochastic simulation outcomes.

Guidelines for designing time-dependent vaccination protocols.

Insights into maximizing broadly neutralizing antibody generation.

Abstract

A persistent public health challenge is finding immunization schemes that are effective in combating highly mutable pathogens such as HIV and influenza viruses. To address this, we analyze a simplified model of affinity maturation, the Darwinian evolutionary process B cells undergo during immunization. The vaccination protocol dictates selection forces that steer affinity maturation to generate antibodies. We focus on determining the optimal selection forces exerted by a generic time-dependent vaccination protocol to maximize production of broadly neutralizing antibodies (bnAbs) that can protect against a broad spectrum of pathogen strains. The model lends itself to a path integral representation and operator approximations within a mean-field limit, providing guiding principles for optimizing time-dependent vaccine-induced selection forces to enhance bnAb generation. We compare our analytical mean-field results with the outcomes of stochastic simulations and discuss their similarities and differences.