Evaluating neoantigen-vaccine responses through mechanistic and model-based frameworks

Abstract

Therapeutic cancer vaccines activate tumor-specific T-cells to reduce tumor burden, yet face challenges when integrating and translating multi-level complex preclinical data into actionable insights. We present a model-based framework to integrate diverse preclinical data, comparing synthetic long peptide vaccines with or without adjuvant stimuli to a vaccine-drug conjugate approach by Adaptable Drug Affinity Conjugate technology. This technology provides modular and rapid conjugate formation via high-affinity binding between a peptide-tagged vaccine antigen and a CD40-targeting antibody, acting as an adjuvant and delivery vehicle. Our developed semi-mechanistic modelling framework successfully linked dosing to tumor dynamics, incorporating four sub-models: pharmacokinetic, peptide uptake by antigen-presenting cells, T-cell response, and tumor growth in TC-1 and MC38 tumors. Model-based simulations highlighted the importance of affinity conjugation on pharmacokinetics and efficacy. Effector T-cells mediated tumor shrinkage. Antibody dose-dependent effects were identified and quantified in the immune-responsive MC38 model. This framework supports rational vaccine optimization and translational decision-making. The graphical abstract was created in https://BioRender.com .

Abstract of "Evaluating neoantigen-vaccine responses through mechanistic and model-based frameworks" by Ibrahim EIK, Laurén I, Veerman RE et al., https://doi.org/10.1038/s41698-026-01579-8, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.