Calling for better diagnostics, prognostics and therapeutics, Saturday’s Ramon Guiteras lecturer said the solutions can be found in academia and big data.
“If we don’t do this in academia, I don’t really think the pharma industry is going to figure this thing out. It’s going to come down to each one of us to solve this problem one by one,” said Atul Butte, MD, PhD, the Priscilla Chan and Mark Zuckerberg Distinguished Professor and inaugural Director of the Institute for Computational Health Sciences at the University of California, San Francisco. Dr. Butte presented the annual Ramon Guiteras Lecture, which he titled “Translating a Trillion Points of Data into Therapies, Diagnostics, and New Insights into Disease.”
Biomedical data have grown exponentially, and Dr. Butte said it is upending the scientific method.
“We were all taught this in second and third grade: You first come up with a question called a hypothesis, then you go gather data to answer the question,” he said. “But in today’s world, we already have the data. That’s 99 percent of the hard part.”
Citing an example of a high school student who developed a diagnostic for breast cancer using publicly available data and then did the same for leukemia, Dr. Butte said anyone who wants to study a disease doesn’t have to start with an institutional review board.
“You start by typing those diseases in and downloading data,” he said. “If a high school kid can do this, every one of our researchers, fellows and postdocs can do it.”
The precision medicine initiative has highlighted the need for improved diagnostics, therapeutics and outcomes. Big data hold solutions for all of these, Dr. Butte said.
He provided an example of using available data on preeclampsia and preterm birth as the first step to finding a better diagnostic test for preeclampsia. The research, which started with publicly available data, resulted in several papers about a new blood diagnostic test, the formation of a company, a $2 million seed investment and then the sale of the company.
“Let me be crystal clear for this academic audience,” Dr. Butte said. “I’m not showing you $2 million to brag about it. I am showing you the $2 million to maybe convince you this is a new way science has to continue out of academia.”
New therapeutics are also a target for big data usage. The cost of developing a new drug is estimated at $4 billion to $12 billion. Dr. Butte suggested that big data are the answer to reducing these costs.
“Instead of just using this for new drugs, we realized we could do this to supply new uses for old drugs,” he said.
Dr. Butte cited examples of using niclosamide, a medication for tapeworm infestation, for liver cancer and a diuretic from the 1960s for psoriasis. He presented unpublished data about the new use for the old diuretic and its affect on keratinocytes in psoriasis in a mouse model.
“Nobody has a molecule like this, and it was sitting there in public data — a 1960s diuretic. How many other molecules are sitting there waiting for us to use?” he said.
Among all the data assets available, Dr. Butte said he is most excited about electronic health records. At UCSF, he is working to build a single data warehouse containing clinical data across the entire University of California system and approximately 15 million patients.
Dr. Butte said he’s trying to build maps of death and disease in California, adding that he also wants to show where patients are on those maps and then predict what will happen in the next 90 days and in the next year.
“That, to me, is going to be the new definition of an accountable care organization, one that knows how to account for the care of all 15 million of its patients,” he said.