ARIZONA NEWS

NAU biologist receives grant to build software for cancer drug efficiency

Jan 6, 2021, 4:25 AM

FILE - In this Sept. 5, 2013 file photo, an infusion drug to treat cancer is administered to a pati...

FILE - In this Sept. 5, 2013 file photo, an infusion drug to treat cancer is administered to a patient via intravenous drip at a cancer center hospital in Durham, N.C. Insurers can no longer reject customers with expensive medical conditions thanks to the health care overhaul, but patient advocates still see wiggle room for companies to avoid covering the sickest and costliest patients.

PHOENIX — A biologist with Northern Arizona University has received a $1.4 million grant from the National Institutes of Health to continue building software tools aimed at predicting the efficiency of drugs to treat specific cancers.

Richard Posner, a professor in NAU’s Department of Biological Sciences, will use the grant money to more accurately predict how certain drugs will stop the growth of cancer cells through mathematical models.

Researchers can then use the predictions to develop the most effective combinations of drugs to treat specific cancers.

“In future work, we will focus on new tools for learning model parameters from data, which is challenging because practical problems require scalable algorithms that aren’t available in existing toolboxes,” Posner said in a press release. “We’re trying to provide solutions.”

Posner and computational systems biologist William Hlavacek from the Theoretical Division at Los Alamos National Laboratory are the principal investigators on their research project, “Hardening Software for Rule-based Modeling,” which began in 2014.

The project was recently renewed through April 2024 due to the funding.

The funding will provide enhancements to the current software package that was developed for the rule-based modeling.

The modeling tools developed through the project have been used by groups in the United States and across the world.

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NAU biologist receives grant to build software for cancer drug efficiency