Landmark announcement at CHI’s MMTC signals open access initiative for drug development.
By Kevin Davies
March 24, 2009 | SAN FRANCISCO—Merck executives Stephen Friend and Eric Schadt unveiled their plans for Sage, an open access platform for sharing and disseminating complex data representing disease biology, at CHI’s Molecular Medicine Tri-Conference last month.* In a joint presentation, Friend, Merck senior vice president and former oncology chief, and Schadt reviewed the successes and outstanding challenges that prompted them, with Merck’s blessing (in the form of money and resources) to entertain a bold new approach to improving the expense, time, and productivity of drug development.
The benefits of analyzing complex bionetworks are very good, said Schadt, but “more expensive than any one company can afford.” The vision of Sage was “to create open access, integrative bionetworks, evolved by contributor scientists, to accelerate the elimination of human disease.” An all-star advisory team includes Nobelist Leland Hartwell, Sir David Lane (A*STAR Singapore), Navigenics co-founder Dietrich Stephan, Merck research chief Peter Kim, Yale’s Rick Lifton, and John Wilbanks (Science Commons).
Schadt said that Sage would be absolutely dependent on contributing scientists across the globe. “We need massive amounts of information appropriately integrated to build models that are predictive,” said Schadt. Aside from the scale and cost of such research, “scientists across the globe involved in different areas of research need to be actively engaged in accessing these networks and contributing information back.”
The transition from a linear to a network mindset would require the generation of coherent datasets, the development of predictive models to design novel therapeutic approaches, and the leveraging of social networks and other means to foster a contributor network. “Watching the trends of public data access, we anticipate a transition of disease biology into the precompetitive space,” said Schadt. Friend added that the transition is, “something that we feel in the long run has an opportunity [to succeed].” As for why scientists should include their own data, Friend said, “picture chemistry, picture physics. The people who were originally trying to mix compounds didn’t get very far until they found molecular structures… This is the analogy for what’s going to happen in biology.” New representations of disease allow for data to be shared and layered.
“The hardest part may not be the technology,” Friend concluded. “It’s either going to be ... our institutions … that have a certain culture about what we do with data. Or it’s going to be the clinicians,” who aren’t used to presenting clinical data using defined standards.
Decade of Discovery
Over the past decade, Friend said Merck has introduced numerous bold technologies that have been successful in limited capacities, including widescale RNA expression profiling in tumors, which led directly to the development of Mammaprint and Oncotype diagnostic tests; whole-genome RNA interference (RNAi) screening to select drugs and patient response. But system and sample heterogeneity made it almost impossible to put the results into context. “It’s like looking at a single frame in Slumdog Millionaire and going, Ah, that’s what that movie was about,” he said.
A third initiative, beginning around 2002, was to merge databases of clinical information and genetic information. Merck forged collaborations with institutes such as the Moffitt Cancer Center (see, “Merck-Moffitt Partnership Breaks Down Silos,” Bio•IT World, Aug 2008), which enables Merck researchers to direct patient selection in clinical trials based on molecular signatures. But Friend said that the volume of disease data amounted to “a clinical/genomic Tower of Babel” problem.
More recently, Merck has been riding the success of Schadt’s team in Seattle (see, “Eric Schadt’s Integrative Approach to Predictive Biology,” Bio•IT World, Oct 2008), which has taken major steps to harness the explosion of data and analyze biological networks to predict the physiological state of the system. “To be competitive in the future and to impact human health, we must become masters of information,” Schadt said, displaying a picture of Aria, the all-seeing master computer from the film Eagle Eye.
This article also appeared in the March-April 2009 issue of Bio-IT World Magazine.
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