A new mathematical model from MIT researchers suggests optimal treatment strategies for COVID-19.
Researchers at Massachusetts General Hospital (MGH), in collaboration with researchers at Brigham Hospital, Women’s Hospital, and the University of Cyprus, have created a mathematical model that incorporates information about the known infectious mechanisms SARS-CoV-2.
Our model predicts that the antiviral and anti-inflammatory drugs that were first used to treat COVID-19 may have limited efficacy depending on the stage of disease progression.
Rakesh K. Jain, Ph.D., of Edwin L. Steele Labs in MGH’s Radiation Oncology Department and Harvard Medical School (HMS).
Jane and colleagues found that the viral load (the level of SARS-CoV-2 particles in the bloodstream) increases during early lung infection in all patients. Still, later no pattern could be identified: starting from day 5, the disease course depends on the levels of key immunity.
Patients younger than 35 years of age who have healthy immune systems experience sustained T cell recruitment, accompanied by a decrease in viral load. All these processes lead to a decrease in blood clots’ risk and the restoration of oxygen levels in the lung tissues: these patients tend to recover.
But people with a higher level of inflammatory processes during infection can be patients with diabetes, obesity, or high blood pressure and carry the infection harder.
Based on their findings, Jain and colleagues propose using heparin, a blood clot-preventing drug, for optimal treatment in elderly patients. Also, to use a modifying drug (checkpoint inhibitor) in the early stages of the disease and the anti-inflammatory drug dexamethasone in the later stages.
In patients with obesity, diabetes, or high blood pressure, it is suggested to use drugs that specifically target substances that promote inflammation (cytokines such as interleukin-6) and drugs that can suppress the renin-angiotensin system (the main mechanism for controlling blood pressure in the organism).