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Optimizing treatment of cancer patients infected with COVID-19 and other preconditions using mathematical modelling

Descripción del proyecto

Un modelo para predecir la terapia óptima para enfermos de cáncer con COVID-19

Los enfermos de cáncer tienen un mayor riesgo de contraer el coronavirus del síndrome respiratorio agudo grave de tipo 2 (SARS-CoV-2) y padecerlo de forma grave. Para evitar la discontinuidad de su atención clínica, es urgente identificar la combinación correcta de fármacos. El proyecto CancerCOtreat, financiado con fondos europeos, propone desarrollar un sistema «in silico» para modelizar la progresión de la COVID-19 en el contexto del cáncer. El sistema combina la biología de la infección por SARS-CoV-2, la acción de los fármacos aprobados y la fisiopatología de los enfermos de cáncer. El equipo del proyecto ayudará a investigar la eficacia de diferentes tratamientos e identificará combinaciones sinérgicas que brinden una terapia óptima para enfermos de cáncer infectados con el SARS-CoV-2.

Objetivo

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.

Ámbito científico (EuroSciVoc)

Palabras clave

Programa(s)

Tema(s)

Régimen de financiación

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Convocatoria de propuestas

(se abrirá en una nueva ventana) H2020-MSCA-IF-2020

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Coordinador

UNIVERSITY OF CYPRUS
Aportación neta de la UEn
€ 256 236,48
Dirección
AVENUE PANEPISTIMIOU 2109 AGLANTZI
1678 Nicosia
Chipre
Región
Κύπρος Κύπρος Κύπρος
Tipo de actividad
Higher or Secondary Education Establishments
Enlaces
Coste total
€ 256 236,48

Socios (1)