Today we are delighted to introduce another key Code-N Advisory Board member: Dr. Paul Pearson. A veteran executive with Amgen, Merck, Pharmacia and Upjohn, Paul is now president of a consultancy advising biotechnology and pharmaceutical companies and the NIH on drug discovery, metabolism, drug interactions, toxicity, and safety. Be sure to read the profile of our other new board member, Dr. Christian Fibiger.
We began with our now-standard question about what the biggest challenge for the industry is.
For all the talk of unlocking innovation, the biggest issue in pharma today is risk aversion. Coming from Big Pharma to my work today, I’ve seen what small companies can do to move the needle on programs in ways that leave bigger companies trundling along with the rest of the herd. Larger companies are so bureaucratic and risk averse that they can’t see the big picture. We know that 99% of research today is not and will not be performed in one company. Success in drug discovery comes from accessing internal AND external data and following the science to a successful outcome.
How can Big Pharma keep up?
It’s going to require a paradigm shift, but I think access to big data and better information will enable them to start thinking independently. Information on intellectual property, competitive drugs, potential molecular targets, patents—all of this information is important, and being able to readily access and make sense of it will enable scientists to put together a compelling research strategy to present to their organization.
And this is where Code-N comes in, right?
Yes. The difference Code-N is pursuing is providing the ability to search broadly not just across a wider number of databases, but across information in a way that enables a more global evaluation of that information. It’s also removing the keyword based approach, so that even if you aren’t asking the right question semantically, you are still getting relevant information. In my current work, people contact me with a discovery problem they need solved. It’s important to be able to interrogate information sources efficiently in a way that lets me think about the problem, explore the literature, place the problem in the context of that literature, and emerge with a solution. And this capability has value to me, to scientists at my client companies, and even to VCs or entrepreneurs starting a new firm—being able to sort through all the information out there and use it to drive science-based decisions.
What inspired you to sign on as a Code-N advisor?
From the first demo I saw, I was interested in the ability to access data in an unbiased way that puts the science first. The ability to explore hypothesis by searching datasets presents possibilities. Some you might reject, some you might follow further—but these decisions are data driven, the hypotheses are data driven, and ultimately you’re following the science to the best solution. I see this technology being very useful in early drug development and the translational space, where you’re finding molecular targets and molecules that might interact with them. Navigating that space is chaotic and difficult. My perspective will help shape Code-N’s technology to design and deliver a product that will meet the needs of drug discovery scientists. I’m also very interested in blending proprietary and public data and using it as a big data source that can help scientists with problem solving, hypothesis creation, and thinking through new potential drug targets and applying them in unserved medical areas.