Cytocybernetics, the UB spinoff founded by Randall L. Rasmusson, PhD, left, and Glenna C. Bett, PhD, is working to speed up COVID-19 drug screening.

Cytocybernetics Accelerates COVID-19 Drug Screening

Published December 2, 2020

story based on news release by jessica szklany

Cytocybernetics, the UB spinoff co-founded by two Jacobs School of Medicine and Biomedical Sciences faculty members, is aiding in the effort to clear candidate drug therapies for COVID-19 in a fast, effective and safe manner.

“The COVID situation requires quick answers to drug risk, but skipping or paying inadequate attention to this safety test can lead to serious problems, which may only become apparent when the drug is widely used. ”
Cytocybernetics CEO and vice chair for research and associate professor of obstetrics and gynecology
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Addresses Critical Step for USDA Approval

Cytocybernetics has developed a high-tech tool called CyberQ to rapidly assess whether or not investigational COVID-19 drugs have arrhythmogenic properties that can result in sudden cardiac death — a critical step in the U.S. Food and Drug Administration’s drug approval process.

For this work, the company has been awarded $44,990 in supplemental COVID-19 funds through the National Science Foundation’s (NSF) Small Business Technology Transfer (STTR) program. The funds are an amendment to an initial Phase I STTR award received by the company in June 2019, bringing Cytocybernetics’ total award sum for the project to $269,990.

“Since the late 1990s, all new drugs must demonstrate that they are safe and do not unreasonably increase the risk of sudden cardiac death. The FDA mandates cardiac risk information from all drug candidates, and most companies use a slow, laborious approach that was developed over 20 years ago,” says Glenna C. Bett, PhD, Cytocybernetics CEO and vice chair for research and associate professor of obstetrics and gynecology.

CyberQ Uses Computer Models to Analyze Data

To replace this outdated process, CyberQ utilizes advanced computer models and machine learning to quickly analyze electrophysiological data from a variety of in-vitro assays and accurately determine if new drugs may produce cardiac arrhythmias that could lead to patient deaths.

“The COVID situation requires quick answers to drug risk, but skipping or paying inadequate attention to this safety test can lead to serious problems, which may only become apparent when the drug is widely used,” Bett explains. “We are partnering with the FDA to produce a simple yet powerful analysis of drug data to determine the clinical risk of a drug candidate.”

“This new process is designed to reduce the time to bring a drug to market, and to increase the clinical relevance of the preclinical drug safety testing, resulting in fewer post-market adverse events,” Bett adds. “Shortening the time and increasing the accuracy of this testing will bring down the cost of commercializing a candidate compound.”

Promising Drugs Can Now Be ‘Resurrected’

According to Bett, the older FDA process for determining the cardiac safety of drug candidates often resulted in large errors in early stages of drug development, resulting in premature abandonment of promising drugs. Along with safety tests for new potential therapies, Cytocybernetics’ software uses artificial intelligence to re-examine old data to recognize such drugs.

“The net result is that drugs that were well along in development that have the potential to be part of the solution to COVID-19 can be ‘resurrected’ to fight the disease much more quickly than starting from scratch,” says Randall L. Rasmusson, PhD, Cytocybernetics president and professor of physiology and biophysics.

“We’re proud to see UB spinoff companies like Cytocybernetics making progress in developing technologies that solve an urgent market challenge and can impact millions of lives,” says Christina P. Orsi, UB’s associate vice president for economic development.

Cytocybernetics senior software engineer Leigh Korbel, who graduated from UB with a master's degree in physics and advanced certification in computational science, is the lead investigator on the NSF-funded project.