Researchers who recently developed a mathematical model showing why treatment responses differ significantly between individuals with COVID-19 used this model to identify biological markers associated with these different responses. A team led by scientists at Massachusetts General Hospital (MGH) and the University of Cyprus will use this model to better understand the complex interactions between illness and response, allowing clinicians to provide optimal care for diverse people. We can help you to provide. Patience.
Works published in EBioMedicineWas started because COVID-19 is very non-uniform. In short, post-SARS-CoV-2 infections range from asymptomatic to life-threatening conditions such as respiratory failure and acute respiratory distress syndrome (ARDS). “There is considerable heterogeneity even within a subset of severe COVID-19 patients who develop ARDS. Great efforts have been made to identify subtypes of ARDS as defined by clinical features or biomarkers. We’ve been, “explains co-chief author Rakesh K. Jain, Ph. .D. , Director of EL Steele Laboratories for Oncology Biology at MGH, and Professor of Radiation Oncology at Harvard University School of Medicine (HMS). “To predict disease progression and personalize treatment, we need to determine the association between clinical features, biomarkers, and underlying biology, which is achieved in the course of many clinical trials. Yes, but this process is time consuming and expensive. “
Alternatively, Jain and his colleagues used a model to analyze the impact of different patient characteristics on post-treatment outcomes with different treatments. This allowed the team to determine the optimal treatment for different categories of patients, identify the biological pathways responsible for various clinical reactions, and identify markers for these pathways.
The researchers simulated six patient types (defined by the presence or absence of different comorbidities) and three types of treatments that regulate the immune system. “Using the new therapeutic efficacy scoring system, we found that older, hyperinflamed patients responded better to immunomodulatory therapy than obese and diabetic patients,” said co-senior co-senior author Reims.・ Dr. Mann (Deputy Director) said. Associate Professor of Steele Labs and HMS. “We also found that the optimal time to start immunomodulatory therapy depends on the patient and the drug itself.” Biological markers Different ones based on the patient’s characteristics determined the optimal treatment start time, and these markers indicated a specific biological program or mechanism that affected the patient’s outcome. The markers were also consistent with clinically identified markers of disease severity.
For COVID-19 and other symptoms, the team’s approach allows researchers to enhance clinical trials in patients who are most likely to respond to a particular drug. “Such enrichment based on positively predicted biomarkers is a potential strategy for improving the accuracy of clinical trials and accelerating the development of treatments,” co-authored as an associate professor at the University of Cyprus. Said Dr. Triantafyllos Stylianopoulos.
Sonu Subudhi et al, Strategies for Minimizing Heterogeneity and Optimizing Clinical Trials in Acute Respiratory Distress Syndrome (ARDS): Insights from Mathematical Modeling, eBioMedicine (2022). DOI: 10.1016 / j.ebiom.2021.103809
Massachusetts General Hospital
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Mathematical model may help improve treatments and clinical trials of patients with COVID-19 and other illnesses Source link Mathematical model may help improve treatments and clinical trials of patients with COVID-19 and other illnesses