In silico clinical studies for the design of optimal COVID-19 vaccination schedules in patients with cancer

Abstract

Abstract As we approach an endemic phase of COVID-19, there is an urgent need for the development of novel and flexible tools to predict the effectiveness of COVID-19 vaccines over the long term. This is particularly evident for patients with significant comorbidities, such as cancer, who may be underrepresented in general vaccine cohorts. More rigorous and scientifically grounded guidelines may help reduce the now prevalent "vaccine fatigue" (Stamm et al., Nature Medicine 2023). We propose that in silico clinical studies, i.e., use of computer simulations for the evaluation of a medicinal product or intervention, is a feasible solution. We have developed a mechanistic mathematical model of SARS-CoV-2 infection to better understand the mechanisms of COVID-19, that account for the specific characteristics of novel variants, including immune evasion and replicative potential. Previously, we used this modeling framework to predict the long-term effectiveness of COVID-19 vaccines in healthy individuals and those who have cancer or suppressed immune responses and performed in silico studies to predict vaccines effectiveness (Voutouri, et al, PNAS 2023). Here we present a comparison of our model predictions with data on bivalent vaccines. Our modeling framework provides a useful tool for predicting the effectiveness of booster doses for different vaccine variants, and our findings suggest that bivalent boosters are particularly effective for patients with cancer We hope that our study will contribute to the development of effective vaccination strategies for vulnerable populations.