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

Información del proyecto

CancerCOtreat

Identificador del acuerdo de subvención: 101028945

DOI

10.3030/101028945

Proyecto cerrado

Fecha de la firma de la CE 13 Abril 2021

Fecha de inicio 1 Mayo 2021

Fecha de finalización 30 Abril 2024

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 256 236,48

Aportación de la UE

€ 256 236,48

Coordinado por

UNIVERSITY OF CYPRUS
Cyprus

Periodic Reporting for period 2 - CancerCOtreat (Optimizing treatment of cancer patients infected with COVID-19 and other preconditions using mathematical modelling)

Período documentado: 2023-05-01 hasta 2024-04-30

Resumen del contexto y de los objetivos generales del proyecto

The project aims to address the negative impact of COVID-19 on cancer patients, who are at a higher risk of developing severe symptoms due to preexisting conditions such as obesity, diabetes, hypertension, advanced age, and gender. The objective is to repurpose existing drugs to treat COVID-19 in cancer patients and identify optimal treatment regimens using an in silico systems biology approach.

The proposed mathematical framework will model the known biology of SARS-CoV-2 infection and the action of approved drugs in the context of cancer patients with different disease states, preconditions, and gender. The model will 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. The model will be validated using data from the Massachusetts General Hospital patient database.

The project is timely because it will reveal novel strategies to optimally combine current and emerging treatments for COVID-19 in cancer patients. Moreover, the proposed model will set a mathematical framework for the optimal treatment of cancer patients contracted by any infectious diseases. The fellowship will allow the applicant to expand his knowledge in tumor/virus biology, cancer research and clinical translation, mathematical modelling, and the analysis of complex biological systems involving multiple medical conditions.

In conclusion, the project proposes an innovative approach to optimize the treatment of COVID-19 in cancer patients using existing drugs and mathematical modelling. The findings from this project will have important implications for the clinical management of cancer patients and may be applicable to the treatment of other infectious diseases as well.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

January 2022:
1. The first mathematical model has been developed and has led to a publication in EBiomedicine entitled: “Strategies to minimize heterogeneity and optimize clinical trials in Acute Respiratory Distress Syndrome (ARDS):Insights from mathematical modelling”
https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(21)00603-4/fulltext

January 2023:
2. The second mathematical model has been developed and has led to a publication in Proceedings of the National Academy of Sciences (PNAS), entitled: “Mechanistic model for booster doses effectiveness in healthy, cancer, and immunosuppressed patients infected with SARS-CoV-2”
https://www.pnas.org/doi/abs/10.1073/pnas.2211132120

March 2024
3. The third mathematical model has been developed and has led to a publication in Cell Reports Medicine, entitled: "In silico clinical studies for optimal COVID-19 vaccination schedules in patients with cancer"
https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(24)00059-4

4.Voutouri C., Hardin, C. C., Naranbhai, V., Nikmaneshi, M. R., Khandekar, M. J., Gainor, J. F., ... & Jain, R. K. Dynamic heterogeneity in COVID-19: Insights from a mathematical model. PLOSONE [DOI: 10.1371/journal.pone.0301780].


Attended the conferences:
1. SACB2022 "Mechanistic model for booster doses effectiveness in healthy,cancer and immunosuppressed patients infected with SARS-CoV-2Chrysovalantis Voutouri, C. Corey Hardin, Vivek Naranbhai, Mohammad R.Nikmaneshi Melin J. Khandekar, Justin FGainor, Triantafyllos Stylianopoulos, Lance L. Munn and Rakesh K. Jain. 19-22 October 2022 in Woods Hole, MA.
2. AACR2022 "Requirement for booster doses in healthy, cancer and immunosuppressed patients infected with the ancestralorvariantSARS-CoV-2Chrysovalantis Voutouri, C. Corey Hardin,Vivek Naranbhai,Melin J. Khandekar,Justin F Gainor, Triantafyllos Stylianopoulos, Lance L. Munn and Rakesh K. Jain. April 8-13, 2022. New Orleans, Louisiana.
3. BMES2022 "Requirement for booster doses in healthy, cancer and immunosuppressed patients infected with the ancestral or variant SARS-CoV-2: Insights from a mechanistic mathematical model" ChrysovalantisVoutouri, C. Corey Hardin, Vivek Naranbhai, Mohammad R.Nikmaneshi Melin J. Khandekar, Justin F Gainor, Triantafyllos Stylianopoulos, Lance L. Munn and Rakesh K. Jain. 12-15 October San Antonio, TX.
4. MGH Clinical Research Day 2021 "Strategies to minimize heterogeneity and optimize clinical trials in Acute Respiratory Distress Syndrome: Insights from mathematical modelingSonu Subudhi, Chrysovalantis Voutouri, C. Corey Hardin, Mohammad Reza Nikmaneshi, Ankit B. Patel, Ashish Verma, Melin J. Khandekar, Sayon Dutta, Triantafyllos Stylianopoulos, Rakesh K. Jain and Lance L. Munn. October 13, 2021, Boston, MA.
5. SAC2023 "Unraveling the complexity and heterogeneity of COVID-19 treatment responses using amechanistic mode" ChrysovalantisVoutouri, C. Corey Hardin, Vivek Naranbhai, Mohammad R.Nikmaneshi Melin J. Khandekar, Justin F Gainor, Triantafyllos Stylianopoulos, Lance L. Munn and Rakesh K. Jain. January 23, 2023, Boston, MA.
6. BMES2021 "Strategies to minimize heterogeneity and optimize clinical trials in Acute Respiratory Distress Syndrome(ARDS): Insights from mathematical modelingSonu Subudhi*, Chrysovalantis Voutouri, C. Corey Hardin, Mohammad Reza Nikmaneshi, Ankit B. Patel,Ashish Verma, Melin J. Khadekar, Sayon Dutta, Triantafyllos Stylianopoulos, Rakesh K. Jain and Lance L.Munn". October 6-9, 2021, Orlando.
7. AACR2020 "Optimizing treatmentforCOVID-19 using computational modelling: Implications forcancer patients". Chrysovalantis Voutouri, Mohammad Reza Nikmaneshi, Sayon Dutta, Melin Khandekar, Ankit B. Patel, Ashish Verma, Triantafyllos Stylianopoulos, LanceL. Munn and Rakesh K.Jain. 22-24 June, 2020 Virtual.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

The project is expected to progress beyond the state of the art by developing an in silico systems biology approach to model the known biology of SARS-CoV-2 infection and the action of approved drugs in the context of cancer patients with different disease states and preconditions. Specifically, the project aims to develop a model that mechanistically models the Renin Angiotensin System and coagulation pathways in the context of cancer, simulates the effect of COVID-19 in this patient population, and explores the efficacy of various treatment regimens to identify synergistic combinations as well as optimal schedules for therapy.

The project's expected results until the end of the project include the development of a mechanism-based model that reveals novel strategies to optimally combine current and emerging treatments for COVID-19 in cancer patients. This will potentially lead to clinical trials and identify areas for future investigations. The results of this research will have a significant impact on the socio-economic and wider societal implications by reducing the number of needed experimental animals overall. Moreover, the proposed model will not be limited to COVID-19 but will set a mathematical framework for the optimal treatment of cancer patients contracted by any other infectious diseases.

In conclusion, the project's expected results are significant, as it will develop a mechanism-based model to reveal novel strategies for optimal treatment of cancer patients infected by COVID-19 and other infectious diseases. The project's potential impacts are wide-ranging, including reducing the number of needed experimental animals overall, potentially leading to clinical trials and identifying areas for future investigations.

Schematic representation of the systems biology approach to predict COVID-19 disease outcome.