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By John T. Aquino
March 22 — Representatives from GlaxoSmithKline, 23andMe Inc., Eisai Inc. and academia said March 22 at a workshop that genetic-based research is transforming drug development but that those involved should avoid overpromising.
The workshop session ended daylong discussions on deriving drug discovery from large-scale genetic resources.
“There are challenges ahead, but there are also unprecedented opportunities to accelerate the delivery of medicines to patients,” said Nadeem Sarwar, workshop chairman and president of Andover Product Creation Innovation Systems at the pharmaceutical company Eisai. “Patients should, accordingly, have unprecedented hopes.”
Lon Cardon, senior vice president of alternative discovery and development and head of target sciences at health-care company GlaxoSmithKline, agreed, saying that genetic research is offering “an opportunity that's once in a lifetime. We don't have to make a leap of faith to a breakthrough. It's all already here.”
But Cardon added, “We lost a lot of ground 10 years ago by overpromising and under-delivering, and that's possible now.”
Sarwar suggested that the most likely avenue for success, fueled by incentives to collaborate, is to work on fewer things and concentrate on those most likely to succeed.
The session, titled “Where Do We Go From Here?” was part of Deriving Drug Discovery Value from Large-Scale Genetic Bioresources: A Workshop, sponsored by the National Academies of Science, Engineering and Medicine's Board on Health Sciences Policy's Roundtable on Translating Genomic-Based Research for Health. The background for the workshop was that several large cohort studies in both public and private sectors in the U.S. and around the world have incorporated or begun to include genetic data collection as part of the study design.
The session included those who had made presentations discussing how progress can be made in discovering and validating promising targets and medicines for those targets by using data collected from large-scale genetic studies. They shared best practices for study design and data collection.
In one presentation, Chas Bountra, professor of translational medicine and head of the Structural Genomics Consortium (SGC) at the University of Oxford, discussed the SGC's approach to drug discovery through its consortium of 10 Big Pharmas and labs in Canada, the U.S., Brazil, Sweden and Germany. The pharmas are GlaxoSmithKline, Pfizer, Merck, Bayer Healthcare, Boehringer-Ingelheim, Janssen, Novartis, Takeda Pharmaceuticals, Eli Lilly Canada and AbbVie.
The approach pools resources, freely shares data, knowledge and reagents with academia and the biopharmaceutical industry in a form of crowdsourcing science, and immediately releases everything to the world.
Bountra said, “The way I work in the open is quick, synergistic and impactful.”
Russ Altman, professor of bioengineering, genetics and medicine at Stanford University, proposed a variant on ClinicalTrials.gov that could be called “geneticcohorts.gov” that would list all cohort studies and the data they collect. “It would give the person you're supposed to call concerning data, and you'd call and say ‘I'm interested in this type of data.' It's a way to facilitate sharing by instituting a metadata repository.”
In a cohort study, an investigator selects a group of nondiseased people and follows them over time to determine if they develop a disease, proceeding from cause to effect.
Referring to the presentations by Bountra and others, Sarwar said they had made it clear that “we can't do this alone.”
He said another key takeaway from the day was that making medicines on the average takes a long time. “So it's become clear that what we have to do is to reduce the denominator, to work on fewer things and only on those that are more likely to succeed.”
Cardon suggested that part of the problem is differing perspectives between partners, which he characterized by defining a difference between large pharmaceutical companies and biotech companies.
“A few years ago, Big Pharmas decided they needed to become more like biotechs. They wanted to become more flexible and leaner. And they did make some changes to do this, and they did some streamlining,” Cardon said. “But at a Big Pharma, a project defines you. If it's not going the way it should, you don't want to let go, you don't want to leave it. In contrast, at a biotech, if a project doesn't work, you run out of money, so it's easier to move on because you have to. We have to learn to let go.”
Mark Daly, co-director of the program in medical and population genetics for the Broad Institute, the biomedical research center of MIT and Harvard University, said that what is needed is a business model that doesn't give incentives to academics for hoarding data and gives incentives to Big Pharma to collect data and then share it.
“We need to look beyond genetics. Genetic discovery is relatively simple and relatively transient. We need to get into interpreting the genetic variants, to get to actual therapeutic hypothesis,” Daly said.
Altman said, “It's important to come to grips with the idea that genetics won't get you all the way by itself. Genetics is part of a portfolio that must be combined with other things.”
Cardon said that it is important to think innovatively and collaboratively. He cited a colleague as saying, “We're a little like a taxi driver trying to design Uber,” referring to the online transportation network.
“Given where the cab driver is coming from, that's not going to happen. It is incumbent on us to work together to make it happen. We are still focusing too much on raw data rather than on interpreting data,” Cardon said.
Bountra said that another obstacle to moving forward is an insistence on labeling data projects as focused on cardiology or oncology or gastrointestinal disease. He noted that this has hampered some project funding. “Terminology is blinkering our thinking. We need to be more data driven.”
Bountra added that his goal is to take molecules his group has developed and to do small proof-of-medicine studies in patients.
“History has shown that many drug candidates have exhibited efficacy in animal models, but few of these have translated into benefits in early phase patient studies,” Bountra said. “This is a risky business. But target validation happens in patients.”
There were exchanges between the panelists about data sharing and the general impression that everyone hoards their data.
Stanford's Altman said that his conclusion from the day's presentations was that data sharing isn't a rare thing. “But sometimes it's data sharing in the sense of ‘Tell me what you need and I'll call the lawyer and get back to you.' ”
Richard Scheller, chief science officer for 23andMe, agreed that data sharing isn't uncommon. “23andMe collaborates all the time,” he said.
To contact the reporter on this story: John T. Aquino in Washington at email@example.com
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