Pharma R&D Today
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Driving R&D Success: Deeper Science, Greater Collaboration
Posted on May 25th, 2016 by Tamas Szarvas in Pharma R&D
As a senior strategic marketing professional at Elsevier, I often find myself telling our potential clients: to unlock your organization’s productivity, you need to strengthen your science—and we can help you do that. I know that’s the case because we’ve worked hard with our business partners to enhance the rigor of their research, whether they’re in the academic, nonprofit, government or industry sector.
Nevertheless, it was thrilling to hear that very same theme emphasized in a presentation at a recent Chief Strategy Officer Summit in New York City. Devyn Smith, Head of Strategy for Pharmatherapeutics at Pfizer Worldwide R&D, described the drug discovery “journey” over the past 15 years. He noted that although the biopharmaceutical industry spends significantly more on R&D per employee than most others—including the chemical, computer and electronic, and aerospace industries, to name a few—tighter regulations, the long road to approval, and the high level of attrition during the R&D process continue to depress productivity while the costs and complexity of producing a novel drug continue to rise.
But the root cause of this situation—the “fundamental challenge,” as Devyn said—has been scientific. He cited NIH Director Francis Collins, who wrote in Nature, “We should remember that genomics obeys the First Law of Technology: we invariably overestimate the short-term impacts of new technologies and underestimate their longer-term effects.” That applies to all areas of science, not just genomics.
While understanding the science that underlies disease takes time, when the pieces start coming together, the effect can be cataclysmic. As Devyn pointed out, that understanding is now “revolutionizing cancer therapeutics.” For example, in 1987, we knew that mutations in the KRAS oncogene could set the stage for lung and other cancers, but not much else was known about genes and cancer. By 2004, the science had progressed, and mutations in the epidermal growth factor receptor (EGFR) were also implicated.
By 2012, the science and our understanding had significantly expanded; with the identification of many additional relevant mutations, tumors could be classified based on genetic mutations rather than tissue type, and therapies began to be based on the genotype of a patient’s tumor.
“Success is occurring after a decade of struggles, as the science revolution proceeds,” Devyn said. Once the underlying science was ready, various models of innovation could kick into gear. The first four innovation models that Devyn described—namely, increased R&D spend, industry consolidation, biotech in-licensing or acquisition, and R&D reorganization—have already been tried by the industry in an effort to curb declining productivity.
Emerging models, he said, include outsourcing, cooperative technology development, and open source. At Elsevier, we’re particularly excited about open source, and we’re currently involved in a number of initiatives and partnerships that enable researchers to more easily store, share, discover and use data. Industry, in particular, is learning that openly partnering during the pre-competitive phases of discovery and development strengthens the science and yields a much stronger understanding than holding everything back.
Elsevier already has the capability of mining the literature, conference input, regulatory documents, social media (and whatever comes next, such as electronic health record data or mobile health data) in the service of problem solving and decision making.
We know how to harness that content to move from data to knowledge, and from knowledge to understanding. The more content we can gather and analyze from diverse sources, the better prepared we are to help our business partners find the nuggets of information that could enable them identify and develop novel therapeutics.
By enabling our partners to gain a deeper scientific understanding of the compounds they want to move into the clinic, we’re also enabling them to more effectively choose which candidate compounds to move into clinical trials and, using precision medicine strategies, which patients are more likely to benefit by participating in those trials.
I am grateful to Devyn for (unwittingly, to be sure) reinforcing the two critical themes that Elsevier talks about for pharma R&D—understanding the science and openly collaborating. I’m also inspired and energized by the larger possibilities of this approach, and the power of our technologies, ultimately, to make a difference in people’s lives.
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