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Personalizing drug development using big data

A new computer program, developed by CIBS’ Rui Chang and his colleague Eric Schadt, dean for precision medicine at the Icahn School of Medicine at Mt. Sinai, is helping medical professionals predict how diseases will progress in new patients and how they will respond to therapies.

Chang and Schadt created the algorithm, which brings together big data from a variety of sources such as DNA and RNA sequencing, proteomics, metabolomics and epigenetics.

“With this technology, I’m excited to build an atlas of disease models to create a holistic way to swipe through the disease data, and then, within each disease section, find the targets for that disease,” Chang said.

The team worked with Tech Launch Arizona, the UA office that works with university inventors to commercialize inventions, to protect the intellectual property and license it to their startup, INTelico Therapeutics, LLC.

With guidance from mentors-in-residence Mike Sember and Kevin McLaughlin, Chang gained valuable business advice through TLA’s I-Corps program, a six-week intensive course that teaches academic entrepreneurs about lean launch methodology and customer discovery.

“Rui has great energy and passion for his vision around harnessing computational power for improved drug application and patient care. It was a pleasure working with him and I wish him success,” McLaughlin said.

The company plans to use the program to provide services to researchers at academic institutions and pharmaceutical companies, allowing them to run bioinformatics on their proprietary patient data to help them develop new, more effective drugs more quickly and cost effectively.

The Center for Innovation in Brain Science (CIBS) at the University of Arizona is addressing the challenge that, in the 21st century, there is not a single cure for a single neurodegenerative disease and is focused on four age-associated neurodegenerative diseases: Alzheimer’s, Parkinson’s, Multiple Sclerosis and ALS.

© 2024 Center for Innovation Brain Science. University of Arizona Health Sciences.

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