One reason why COVID-19 is so difficult to treat is that its symptoms run the whole gamut, from none whatsoever to organ failure and even death. This is why understanding COVID-19’s heterogeneity and identifying the best therapy in each case is crucial in the battle against the SARS-CoV-2 virus. Researchers from Cyprus, Iran and the United States have taken significant steps in achieving this goal. The scientists have developed a comprehensive mathematical model that shows why COVID-19 outcomes are so varied and how treatment can be tailored to the needs of specific patient groups. The biology-based model is the outcome of research supported in part by the Immuno-Predictor and CancerFingerPrints projects for which researcher Triantafyllos Stylianopoulos of the University of Cyprus received funding from the EU. The research is discussed in a paper published in the journal ‘Proceedings of the National Academy of Sciences of the United States of America’. The mathematical model is based on known mechanisms of SARS-CoV-2 and incorporates possible mechanisms of action of various treatments that have been tested in COVID-19 patients. In addition, it shows how aspects such as age and other medical conditions a patient may have can affect their response to treatment and clinical outcomes. “Our model predicts that antiviral and anti-inflammatory drugs that were first employed to treat COVID-19 might have limited efficacy, depending on the stage of the disease progression,” stated corresponding author Rakesh K. Jain of Harvard Medical School and Massachusetts General Hospital in a news item posted on the ‘Scienmag’ website.
In the course of their research, the scientists found that the viral load – the amount of the virus contained in the blood – increases during early lung infection. However, after the fifth day, the trajectory of the viral infection changes depending on the levels of activated T-cells, white blood cells that are part of the adaptive immune system. In patients younger than 35 with healthy immune systems, the scientists observed a sustained recruitment of T-cells. This was accompanied by a decrease in viral load and reduced inflammation, as well as lower levels of innate immune cells called neutrophils and macrophages. These processes resulted in significantly reduced blood clot formation and in restored oxygen levels in the lungs. However, a poor outcome was found to be common in cases where patients already had high levels of inflammation when infected with SARS-CoV-2. It was also found in people with a more active innate immune response combined with a less effective adaptive immune system. Such cases included older patients and people with diabetes, obesity, high blood pressure or dysregulated immune response. According to the model, effective treatments for diabetic patients as well as older patients with some inflammation and impaired adaptive immunity include the blood thinner heparin or an immune checkpoint inhibitor combined with the corticosteroid drug dexamethasone. Additionally, the combination of heparin and dexamethasone has proved to be beneficial for patients with obesity or high blood pressure. The biology-based model was originally developed for use in cancer research conducted with support from the Immuno-Predictor (Mechanical Biomarkers for Prediction of Cancer Immunotherapy) and CancerFingerPrints (Identification of nano-mechanical fingerprints as a biomarker for cancer treatment prognosis) projects. The researchers plan to develop the model further to study the immune system’s response to various COVID-19 vaccines. For more information, please see: Immuno-Predictor project CancerFingerPrints project
Immuno-Predictor, CancerFingerPrints, COVID-19, SARS-CoV-2, mathematical model, cancer, treatment