Finding a Drug to Treat Liver Cancer for $50K

Bringing a new, lifesaving drug to market can take 15 years and cost hundreds of millions of dollars. But what if we could accomplish the same thing in significantly less time – and for as little as $50,000? 

That’s the audacious approach of Atul Butte, MD, PhD, and his colleagues at UCSF’s Bakar Computational Health Sciences Institute (BCHSI). The key to this revolutionary idea is to compile publicly available data on tens of thousands of drugs already approved for human use and match them to diseased cells, in a search for agents that were made for one disease but also can treat another. 

“We’ve invested millions in electronic medical records (EMR) systems and cool gizmos like wearables to generate more and more data,” Butte says. “But it’s all just sitting there, waiting for someone to ask the right questions.” Mining all that biomedical data can also lead to other solutions, like designing blood tests with greater specificity or eliminating unnecessary blood transfusions. 

If we want to change the world, Butte says, we need to get all our biomedical data into one place. Then information scientists and biomedical researchers can see what care each person with a particular disease received and what was or was not effective, and apply all that knowledge to other patients suffering from the same disease. 

To start on this daunting task, the BCHSI is working with the five other University of California medical centers at UC Davis, UC Irvine, UCLA, UC Riverside, and UC San Diego to aggregate their patient information. That will result in a whopping 15 million patient records from an extraordinarily diverse population in one secure, reliable data repository that’s strictly regulated to safeguard individual privacy. No other medical system in the nation can match UC for its critical mass of patient records and its computing power to analyze them, Butte says. 

The ultimate goal? Being able to predict what might happen to a specific patient with a specific disease or find the treatment most likely to succeed, and ultimately to use that data to provide every patient on the planet with the best health care possible. 

Atul Butte is the Priscilla Chan and Mark Zuckerberg Distinguished Professor. 

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