Project description
A model to predict optimal therapy for cancer patients with COVID-19
Patients with cancer are at increased risk of contracting SARS-CoV-2 and becoming severely ill. To avoid discontinuity of their clinical care, there is an imminent need to identify the right combination of drugs. The EU-funded CancerCOtreat project proposes to develop an in silico system to model COVID-19 progression in the context of cancer. The system combines the biology of SARS-CoV-2 infection, the action of approved drugs and the pathophysiology of cancer patients. It will help explore the efficacy of various treatments and identify synergistic combinations that provide optimal therapy for cancer patients infected with SARS-CoV-2.
Objective
Cancer patients are of high risk to develop severe COVID-19, which has a negative impact on their clinical management. Cancer therapy and COVID-19 severity can be also affected negatively by preconditions, including obesity, diabetes, hypertension and advanced age, as well as by the gender. To help cancer patients suffering from COVID-19 and the other preconditions as soon as possible, it will be necessary to repurpose existing and well tolerated drugs - alone or in combination. To accelerate this process, we propose to develop an in silico systems biology approach to model the known biology of SARS-CoV-2 infection and the action of approved drugs overlaid on the underlying pathophysiology of cancer patients with different disease states, preconditions and gender. The proposed mathematical framework will mechanistically model the COVID-19 progression in the context of cancer. We will also simulate the effect of COVID-19 in this patient population and explore the efficacy of various treatment regimens to identify synergistic combinations as well as optimal schedules for therapy. Robust model validation will be performed using data from the Massachusetts General Hospital patient database (host of outgoing phase). This is a very timely research because the proposed mechanism-based model will reveal novel strategies to optimally combine current and emerging treatments for COVID-19 in cancer. Importantly, the proposed model will not be limited to COVID-19 but it will set a mathematical framework for the optimal treatment of cancer patients contracted by any infectious diseases. The fellowship will allow the applicant to substantially built upon his previous experience and strengthen his overall scientific abilities. In particular, he will expand his knowledge in tumor/virus biology, cancer research and clinical translation, will enrich his mathematical modelling capabilities and the analysis of complex biological systems that involve more than one medical conditions.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- medical and health sciencesbasic medicinephysiologypathophysiology
- medical and health scienceshealth sciencesinfectious diseasesRNA virusescoronaviruses
- medical and health sciencesclinical medicineoncology
- medical and health scienceshealth sciencesnutritionobesity
- natural sciencesmathematicsapplied mathematicsmathematical model
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Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
1678 Nicosia
Cyprus