Abstract S01-02: Optimizing treatment for COVID-19 using computational modeling: Implications for cancer patients
Authors: Chrysovalantis Voutouri, Mohammadreza Nikmaneshi, Melin Khandekar, Ankit B. Patel, Ashish Verma, Sayon Dutta, Triantafyllos Stylianopoulos, Lance L. Munn, Rakesh K. Jain
Journal: Clinical Cancer Research (2020)

Abstract Emerging retrospective analyses show that cancer patients are more likely to develop severe COVID-19. The causes for these worse outcomes are unclear, but data suggest that cancer therapies, which can suppress the immune system, are not responsible for increased COVID-19 severity. An alternative hypothesis is that common molecular pathways are altered in cancer and COVID-19, resulting in worsened disease outcomes. Our previous work demonstrated that activated renin angiotensin signaling (RAS) modulates the tumor microenvironment, resulting in worse outcomes and therapy resistance. Inhibition of this pathway using angiotensin receptor blockers (ARBs) or angiotensin converting enzyme inhibitors (ACEIs) can improve the outcomes of cancer therapies. Similarly, there is great interest in understanding the implications of RAS in COVID-19 progression because a key component of this system, ACE2, is also the docking site for the SARS-CoV-2 virus. Indeed, multiple clinical trials are currently evaluating whether ARBs/ACEIs benefit or harm COVID-19 patients. To help guide administration of these drugs, we adapted our existing computational modeling framework of the cancer microenvironment using available data to simulate COVID-19 progression in patients. Using a systems biology approach, we mechanistically modeled the interaction of the RAS and coagulation pathways with COVID-19 infection. We further explored the efficacy of various antiviral, antithrombotic, and RAS-targeted treatment regimens to identify synergistic combinations as well as optimal schedules for therapy. The system is complex, given that viral binding of ACE2 interferes with its antiinflammatory signaling. When ACE2 is bound by the virus, its local activity decreases, leading to immune dysregulation and risk of coagulopathy, predictors of COVID-19 severity and mortality. To optimize combination treatments for cancer patients who contract COVID-19, multiple simulations were run by combining different therapeutics currently in clinical trials to predict their effects on viral load, thrombosis, oxygen saturation, and cytokine levels. These include ARBs, ACEIs, antiviral drugs, antithrombotic agents, and anti-inflammatory drugs (e.g., anti-IL6/6R). Our simulations predict that i) there is an optimal timing for treatment with antiviral drugs such as remdesivir, related to immune activation; ii) combinations of antiviral and antithrombotic drugs are able to prevent lung damage, increase blood oxygen levels, and inhibit thromboembolic events; and iii) RAS modulators can have a positive effect when added to the treatment regimen. Effective strategies for COVID-19 treatment identified by this in silico analysis will be further analyzed in combination with cancer therapeutics (e.g., immune checkpoint blockers, chemotherapy) to provide guidelines for optimal clinical management of both cancer and COVID-19. Citation Format: Chrysovalantis Voutouri, Mohammadreza Nikmaneshi, Melin Khandekar, Ankit B. Patel, Ashish Verma, Sayon Dutta, Triantafyllos Stylianopoulos, Lance L. Munn, Rakesh K. Jain. Optimizing treatment for COVID-19 using computational modeling: Implications for cancer patients [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr S01-02.