Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine

The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led to the development of personalized neoantigen cancer vaccines that are tailored to the unique variants found in a patients cancer. These vaccines can provide significant clinical benefit by leveraging the patients immune response to eliminate malignant cells. However, determining the optimal vaccine dose for each patient is a challenge due to the heterogeneity of tumors. To address this challenge, we formulated a mathematical dose optimization problem that aims to find the optimal personalized vaccine doses for a given fixed vaccination schedule, based on a previous mathematical model that encompasses the immune response cascade produced by the vaccine in a patient. To validate our approach, we performed in silico experiments on six patients with advanced melanoma. We compared the results of applying an optimal vaccine dose to those of a suboptimal dose (dose used in the clinical trial and its deviations). Our simulations revealed that an optimal vaccine may lead to a reduction in tumor size for certain patients, with higher initial doses and lower final doses. Our mathematical dose optimization offers a promising approach to determining the optimal vaccine dose for each patient and improving clinical outcomes.