Suzan Farhang-Sardroodi, Department of Mathematics, University of Manitoba, Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Modelling Infection and Immunity Lab, Mathematics Statistics, York University
Machanistic mathematical modelling of the within-host response: from chemotherapy to COVID-19
Mechanistic modelling approaches and pharmacological considerations play a decisive role in exploring novel treatment modalities in cancer and infectious diseases. The recent expansion of within-host mathematical models addresses many questions regarding effective cancer treatments as well as the complex interplay of virus replication and the behaviour of host immune response. Mathematical models have become an integral part of systems biology, which can describe known physiology and fill in the gaps in our understanding of which complex interactions drive host-pathogen responses.
In this seminar, I will first present our mathematical model developed in the context of cancer chemotherapy to define an optimal treatment schedule that reduces tumour burden while also maintaining lean muscle mass. Although chemotherapy is the most common cancer treatment, it has significant side effects, including muscle atrophy. Preservation of body composition, in addition to consideration of inflammation and immune interactions, the gut microbiome, and other systemic health measures, may lead to improved patient-specific treatment plans that improve patient quality of life. Next, I will discuss recent work focused on using mathematical modelling to identify biological and COVID-19 vaccine characteristics that may allow for heightened and longer-lasting immune responses. Here, we focused on the optimal timing of second doses as supply chain logistics hampered initial global vaccine delivery, which is impacted mass vaccination strategies during the early phases of mass vaccination in the COVID-19 pandemic. I will present our recent study which aimed to predict the impact of different prime-boost schedules by quantifying their effects on immunological outcomes based on a simple system of ordinary differential equations.