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Mathematical Model

A mathematical model could help patients with COVID-19 or other diseases

Credit: Pixabay/CC0 Public Domain Investigators who recently developed a mathematical model that indicated why treatment responses vary widely among individuals with COVID-19 have now used the model to identify biological markers related to these different responses. The team, which was led by scientists at Massachusetts General Hospital (MGH) and the University of Cyprus, notes that…

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Credit: Pixabay/CC0 public domain

Investigators who recently developed a mathematical model that indicated why treatment responses vary widely among individuals with COVID-19 have now used the model to identify biological markers related to these different responses. Researchers at Massachusetts General Hospital (MGH), and University of Cyprus led the team. They noted that the model could be used to better understand the complex interactions between illness, response, and help clinicians provide optimal care for patients with diverse conditions.

The work, which is published in EBioMedicine, was initiated because COVID-19 is extremely heterogeneous, meaning that illness following SARS-CoV-2 infection ranges from asymptomatic to life-threatening conditions such as respiratory failure or acute respiratory distress syndrome (ARDS), in which fluid collects in the lungs. “Even within the subset of critically ill COVID-19 patients who develop ARDS, there exists substantial heterogeneity. Co-author Rakesh K. Jain (Ph.D.), director of E.L. Steele Laboratories to Tumor Biology at MGH, and the Andrew Werk Cook Professor Radiation Oncology, Harvard Medical School (HMS), has made significant efforts to identify subtypes ARDS. It is important to identify the relationships between clinical features, biomarkers, and underlying biology in order to predict disease progression and tailor treatment. This can be done over many clinical trials but it is very time-consuming. “

Jain and his coworkers used their model to examine the impact of different patient characteristics on treatment outcomes. The team was able to identify the markers and biologic pathways that were responsible for the different clinical responses of each patient, determine the best treatment, and then decide the best treatment.

The researchers created six types of patients, each one based on the presence or absence different comorbidities. They also tested three types of immunomodulation therapies. “Using a novel treatment effectiveness scoring system, we discovered that older, hyperinflamed patients respond more to immunomodulation therapy” says Lance Munn, Ph.D. who is also the Steele Labs deputy director and associate professor at HMS. We also discovered that the best time to start immunomodulation therapy varies between patients, and it also depends on the drug. Certain that differed based on patient characteristics determined optimal treatment initiation time, and these markers pointed to particular biologic programs or mechanisms that impacted a patient’s outcome.

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