Authors: Sonu Subudhi, Chrysovalantis Voutouri, C. Corey Hardin, Mohammad Reza Nikmaneshi, Ankit B. Patel, Ashish Verma, Melin J. Khandekar, Sayon Dutta, Triantafyllos Stylianopoulos, Rakesh K. Jain, Lance L. Munn
Summary: Background: Mathematical modelling may aid in understanding the complex interactions between injury and immune response in critical illness.
Methods: We utilize a system biology model of COVID-19 to analyze the effect of altering baseline patient characteristics on the outcome of immunomodulatory therapies. We create example parameter sets meant to mimic diverse patient types. For each patient type, we define the optimal treatment, identify biologic programs responsible for clinical responses, and predict biomarkers of those programs.
Findings: Model states representing older and hyperinflamed patients respond better to immunomodulation than those representing obese and diabetic patients. The disparate clinical responses are driven by distinct biologic programs. Optimal treatment initiation time is determined by neutrophil recruitment, systemic cytokine expression, systemic microthrombosis and the renin-angiotensin system (RAS) in older patients, and by RAS, systemic microthrombosis and trans IL6 signalling for hyperinflamed patients. For older and hyperinflamed patients, IL6 modulating therapy is predicted to be optimal when initiated very early (<4th day of infection) and broad immunosuppression therapy (corticosteroids) is predicted to be optimally initiated later in the disease (7th – 9th day of infection). We show that markers of biologic programs identified by the model correspond to clinically identified markers of disease severity.
Interpretation: We demonstrate that modelling of COVID-19 pathobiology can suggest biomarkers that predict optimal response to a given immunomodulatory treatment. Mathematical modelling thus constitutes a novel adjunct to predictive enrichment and may aid in the reduction of heterogeneity in critical care trials.
Source: eBioMedicine, 2022; 75: 103809