– Researchers at the Center for Cytokine Storm Treatment and Laboratory at the Perelman School of Medicine at the University of Pennsylvania have received a $1 million award to enhance their COVID-19 treatment discovery database.
The award, given by the Parker Institute for Cancer Immunotherapy (PICI), will hep the Penn Medicine team expand the scope of the COvid19 Registry of Off-label and New Agents (CORONA) project.
“For the last year, over 100 volunteers and members of my lab have worked on nights and weekends to extract and centralize data for CORONA which has been used to identify and advance the most promising treatments for COVID-19,” said David C. Fajgenbaum, MD, MBA, MSc, assistant professor of Translational Medicine & Human Genetics and director of the Center for Cytokine Storm Treatment & Laboratory at the Perelman School of Medicine.
“With this grant from PICI, we can build out our team to integrate and analyze data with the effort and urgency that this global pandemic warrants.”
CORONA is the world’s largest database of COVID-19 treatments, covering over 400 treatments that have been reported to be administered to more than 340,000 patients. The database helps researchers identify and prioritize promising treatments for well-designed clinical trials and inform patient care.
With the award from PICI, the Penn Medicine team has already built several new tools and will continue to develop more. These tools include an open-access dashboard that harmonizes data between studies and presents vital data points for prioritizing promising treatments, such as the number of randomized control trials that have been completed, the number that are open, the number that achieved their primary endpoint, and others.
“All of the really relevant and important data is listed right next to each COVID-19 drug and kept up to date,” Fajgenbaum said.
“Given the hundreds of drugs that have been tested in the last year, the tens of thousands of published studies about them, and the global importance of finding truly effective treatments, we had to build a central tool like this. We can’t afford to let a promising treatment fall through the cracks.”
Over 20,000 users have accessed CORONA, and the platform has served as a critical dataset for the FDA and NIH. Additionally, Fajgenbaum was recently chosen to serve on NIH’s ACTIV-6 team to select the most promising COVID-19 treatments for a large randomized controlled trial.
Fajgenbaum is also leading a similar effort for the CURE Drug Repurposing Collaboratory, a public-private partnership between FDA, NIH, and Critical Path Institute.
Fajgenbaum and his team used a method similar to CORONA to discover multiple promising treatment approaches for Castleman disease. While the CORONA project currently has seed funding for one year, researchers are actively seeking additional financial support.
Within that timeframe, researchers expect that they can contribute to accelerating the end of the pandemic, but they also have plans to turn the platform into a tool for drug discovery and repurposing beyond COVID-19.
Throughout the COVID-19 pandemic, researchers have sought to develop ways to accelerate promising treatments and drug discovery. In May 2020, a team from Northwestern University developed an artificial intelligence platform that can quickly identify research that has the most potential to produce COVID-19 treatments and solutions.
The tool has the ability to scale up to review a larger number of papers in minutes instead of months.
“The standard process is too expensive, both financially and in terms of opportunity costs. First, it takes take too long to move on to the second phase of testing and second, when experts are spending their time reviewing other people’s work, it means they are not in the lab conducting their own research,” said Brian Uzzi, Richard L. Thomas Professor of Leadership at Kellogg and co-director of the Northwestern Institute on Complex Systems.
“This tool is particularly useful in this crisis situation where we can’t act fast enough,” Uzzi said. “It can give us an accurate estimate of what’s going to work and not work very quickly. We’re behind the ball, and this can help us catch up.”
Credit: Source link