Columbia Data Science Institute recently covered EVQLV’s work towards developing therapeutic candidates against COVID-19 with antibodies.
Two graduates of the Data Science Institute (DSI) at Columbia University are using computational design to quickly discover treatments for the coronavirus.
Andrew Satz and Brett Averso are chief executive officer and chief technology officer, respectively, of EVQLV, a startup creating algorithms capable of computationally generating, screening, and optimizing hundreds of millions of therapeutic antibodies. They apply their technology to discover treatments most likely to help those infected by the virus responsible for COVID-19. The machine learning algorithms rapidly screen for therapeutic antibodies with a high probability of success.
Conducting antibody discovery in a laboratory typically takes years; it takes just a week for the algorithms to identify antibodies that can fight against the virus. Expediting the development of a treatment that could help infected people is critical says Satz, who is a 2018 DSI alumnus and 2015 graduate of Columbia’s School of General Studies.
“We are reducing the time it takes to identify promising antibody candidates,” he says. “Studies show it takes an average of five years and a half billion dollars to discover and optimize antibodies in a lab. Our algorithms can significantly reduce that time and cost.”
Speeding up the first stage of the process—antibody discovery—goes a long way toward expediting the discovery of a treatment for COVID-19. After EVQLV performs computational antibody discovery and optimization, it sends the promising antibody gene sequences to its laboratory partners. Laboratory technicians then engineer and test the antibodies, a process that takes a few months, as opposed to several years. Antibodies found to be successful will move onto animal studies and, finally, human studies.
Given the international urgency to combat the coronavirus, Satz says it may be possible to have a treatment ready for patients before the end of 2020.
“What our algorithms do is reduce the likelihood of drug-discovery failure in the lab,” he adds. “We fail in the computer as much as possible to reduce the possibility of downstream failure in the laboratory. And that shaves a significant amount of time from laborious and time-consuming work.”
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