Preventing medication administration errors and improving efficiency during surgery enhances patient care.
- A mathematical model revealed that the optimal time to initiate immune-modulating therapy in COVID-19 differed according to patients’ medical history and risk factors. Different patients also required different types of immunomodulation for optimal therapy.
- Certain biological markers that differed based on patient characteristics determined optimal treatment initiation time, and these markers pointed to particular biologic programs or mechanisms that affected a patient’s outcome.
- Use of the model may help physicians tailor treatments to different patients and also indicate which patients are most likely to respond to certain drugs tested in clinical trials.
Lance Munn, PhD
Using a novel treatment efficacy scoring system, we found that older and hyperinflamed patients respond better to immunomodulation therapy than obese and diabetic patients.
Deputy Director, E.L. Steele Laboratories for Tumor Biology at MGH
BOSTON – 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 the model can be used to provide a better understanding of the complex interactions between illness and response and can help clinicians provide optimal care for diverse patients.
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. Significant efforts have been made to identify subtypes of ARDS defined by clinical features or biomarkers,” explains co–senior author Rakesh K. Jain, PhD, director of the E.L. Steele Laboratories for Tumor Biology at MGH and the Andrew Werk Cook Professor of Radiation Oncology at Harvard Medical School (HMS). “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.”
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,” says co–senior and corresponding author Lance Munn, PhD, who is the deputy director of the Steele Labs and an associate professor at HMS. “We also found that the optimal time to initiate immunomodulation therapy differs between patients and also depends on the drug itself.” Certain biological markers 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. The markers also matched clinically identified markers of disease severity.
For COVID-19 as well as 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,” says co–senior author Triantafyllos Stylianopoulos, PhD, an associate professor at the University of Cyprus.
Other co-authors include Sonu Subudhi, Chrysovalantis Voutouri, C. Corey Hardin, Mohammad Reza Nikmaneshi, Melin J. Khandekar and Sayon Dutta from MGH; and Ankit B. Patel and Ashish Verma from Brigham and Women’s Hospital.
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. Voutouri is a recipient of a Marie Skłodowska Curie Actions Individual Fellowship.
About the Massachusetts General Hospital
Massachusetts General Hospital, founded in 1811, is the original and largest teaching hospital of Harvard Medical School. The Mass General Research Institute conducts the largest hospital-based research program in the nation, with annual research operations of more than $1 billion and comprises more than 9,500 researchers working across more than 30 institutes, centers and departments. In August 2021, Mass General was named #5 in the U.S. News & World Report list of "America’s Best Hospitals."
About the University of Cyprus
The University of Cyprus (UCY) is the largest in the country academic institute established in 1989. Despite its brief history, UCY is internationally recognized for its active research community, and has been ranked 84th worldwide in the Times Higher Education Young University Rankings 2020. UCY has successfully implemented hundreds of research projects to date, including several funded through MSCA, FP7 and H2020 frameworks of European Commission. UCY is an Endorser of the “European Charter for Researchers” and accredited with the “HR Excellence in Research” logo, ensuring commitment to research and working conditions.
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