This article is part of Harvard Medical School’s continuing coverage of COVID-19.
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 the different responses.
The team, led by Harvard Medical School scientists at Massachusetts General Hospital and researchers at the University of Cyprus, noted that the model can be used to provide a better understanding of the complex interactions between injury and immune response and can help clinicians provide optimal care for a range of patients.
The work, 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, with conditions such as respiratory failure or acute respiratory distress syndrome (ARDS), where fluid collects in the lungs.
“Even within the subset of critically ill COVID-19 patients who develop ARDS, there exists substantial heterogeneity. Significant efforts have been made to identify subtypes of ARDS defined by clinical features or biomarkers,” explained co-senior author Rakesh Jain, the A. Werk Cook Professor of Radiation Oncology at HMS and director of the E.L. Steele Laboratories for Tumor Biology at Mass General.
“To predict disease progression and personalize treatment, it is necessary to determine the associations among clinical features, biomarkers, and underlying biology. Although this can be achieved over the course of numerous clinical trials, this process is time-consuming and extremely expensive,” Jain said.
As an alternative, Jain and his colleagues used their model to analyze the effects that different patient characteristics yield on outcomes following treatment with different therapies. This allowed the team to determine the optimal treatment for distinct categories of patients, reveal biologic pathways responsible for different clinical responses, and identify markers of these pathways.
The researchers simulated six patient types defined by the presence or absence of different comorbidities and three types of therapies that modulate the immune system.
“Using a novel treatment efficacy scoring system, we found that older and hyperinflamed patients respond better to immunomodulation therapy than obese and diabetic patients,” said co-senior and corresponding author Lance Munn, deputy director of the Steele Labs and an associate professor of radiation oncology at HMS.
“We also found that the optimal time to initiate immunomodulation therapy differs between patients and also depends on the drug itself,” Munn said.
Certain biological markers that differed based on patient characteristics determined optimal treatment initiation time and pointed to particular biologic programs or mechanisms that impacted a patient’s outcome. These markers also matched clinically identified markers of disease severity.
For COVID-19 and other conditions, the team’s approach could enable investigators to enrich a clinical trial with patients most likely to respond to a given drug.
“Such enrichment based on prospectively predicted biomarkers is a potential strategy for increasing precision of clinical trials and accelerating therapy development,” said co-senior author Triantafyllos Stylianopoulos, an associate professor at the University of Cyprus.
Funding for the study was provided by the National Institutes of Health, Harvard Ludwig Cancer Center, Niles Albright Research Foundation, and Jane's Trust Foundation. Co-author Chrysovalantis Voutouri is a recipient of a Marie Skłodowska Curie Actions Individual Fellowship.