As physicians working on the front lines of the COVID-19 pandemic can attest, timing is everything when treating a viral infection. Josua Aponte-Serrano, a researcher at the Biocomplexity Institute at Indiana University (IU), is part of an international research team which has developed a modular framework for modeling drug effectiveness in treating viral diseases like COVID-19.
The researchers used an Indiana University supercomputer, Carbonate, to execute multiple simulation replications to help define virus and patient-specific model parameters. Apponte-Serrano’s research, which contributed to a “model which can predict distinctive clinical outcomes depending on critical factors such as initial exposure, affinity of the virus for cell receptors and timing of the immune response,” explained Aponte-Serrano. TJ Sego, co-author and Biocomplexity Institute at IU colleague, ran the simulations of this model using COVID-19 data. The findings, published in PLOS Computational Biology in December 2020, show a robust, “[m]odular framework for complex modeling of acute viral infections like COVID-19 to better understand immune response to drug therapy timing and effectiveness.”
Josua Aponte-Serrano, graduate student researcher, Biocomplexity Institute at Indiana University.
Increased access to powerful computing technologies like Carbonate has broadened the use of complex biomedical models to understand the mechanisms driving the progression of these diseases.
Josua Aponte-Serrano
The lack of a drug’s effectiveness after significant viral replication within the body poses real-life questions for doctors considering whether to provide antiviral treatment. “We studied the effects on progression of treatment potency and time-of-first treatment after infection,” the group of researchers from Indiana University, Technische Universitat in Dresden Germany, The University of Melbourne, and Georgia State University wrote in the PLOS research article.
The group’s modeling suggests that though an antiviral drug’s effectiveness falls after a certain time period, drugs are still potentially effective in preventing excessive tissue damage. “In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection.” In addition, the complex modeling indicates “while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective.”
TJ Sego, co-author and researcher at the Biocomplexity Institute at Indiana University.
In the study, mathematical modeling methods integrate available host- and pathogen-level data on disease dynamics as a way of decoding the biology of infection and immune response, explained Aponte-Serrano. A graduate student researcher in the Biocomplexity Institute at IU, directed by Intelligent Systems Engineering professor James Glazier, Aponte-Serrano’s work applies computational methods to virus interactions to provide scientists a dynamic representation of relevant biological processes.
By constructing a modular framework for studying viruses, researchers are able to pivot resources amidst the ever-changing needs of global health. “A modular framework is necessary to facilitate rapid collaborative development that is necessary to address challenges posed by critical diseases,” said Aponte-Serrano. “Increased access to powerful computing technologies like Carbonate has broadened the use of complex biomedical models to understand the mechanisms driving the progression of these diseases,” he said.
You can learn more about Carbonate by visiting the IU Knowledge Base article, About Carbonate at Indiana University.
