Identifying COVID-19 Therapy Candidates With Machine Learning
Killian Meara, assistant editor for ContagionLive, joined the MJH Life Sciences team in November 2020. He graduated from William Paterson University with a degree in liberal studies, and concentrations in history and psychology. He enjoys film, reading, and pretending he is a good cook. Follow him on Twitter @krmeara or email him at [email protected]
Study pinpoints the protein RIPK1 as a promising target for SARS-CoV-2 treatment.
Investigators from the Massachusetts Institute of Technology, in collaboration with Harvard University and ETH Zurich, have developed a machine learning-based approach that can identify therapies that are already on the market that have potential for repurposing to help fight the coronavirus disease 2019 (COVID-19). Results from the study were published in the journal Nature Communications.
As the COVID-19 pandemic continues to surge across the globe and investigators rush to find treatments, the information provided from the approach may have a significant impact.
The target population for the study is the elderly, as the virus impacts them more severely than younger populations. The approach accounts for gene expression changes in lung cells caused by COVID-19 as well as aging. The hope is that this would allow medical experts to find therapies for clinical testing faster.
"Earlier work by the Shivashankar lab showed that if you stimulate cells on a stiffer substrate with a cytokine, similar to what the virus does, they actually turn on different genes," Caroline Uhler, a computational biologist in MIT's Department of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society, and an associate member of the Broad Institute of MIT and Harvard said. "So, that motivated this hypothesis. We need to look at aging together with SARS-CoV-2 -- what are the genes at the intersection of these two pathways?"
The investigators took 3 steps to identify the most promising candidates for repurposing. They first generated a large list of possible candidates using the machine-learning technology and then mapped the genes and proteins involved in the aging process and in a SARS-CoV-2 infection. They then employed algorithms to pinpoint genes that caused cascading effects through the mapped network which narrowed the list of therapies. The overlap caused by the 2 maps is where the team found the precise gene expression network of therapies that would target COVID-19.
The team plans to share the findings with pharmaceutical companies to aid in finding more therapies that can be repurposed for COVID-19. However, they emphasize that any of the therapies identified must undergo clinical testing before they can be approved for use in elderly populations.
"Making new drugs takes forever," Uhler said. "Really, the only expedient option is to repurpose existing drugs."