Roger Ulrich recently joined Code-N’s advisory board after completing the merger of the company he founded, Calistoga Pharmaceuticals, with Gilead Sciences. Be sure to also read commentaries by Dr. Christian Fibiger and Dr. Paul Pearson.
As when we talked to Christian and Paul, we began by asking Roger what he views as the primary challenges in drug discovery today.
The single biggest problem plaguing everyone is keeping a robust pipeline, and with concerns about patent expirations, what large companies particularly want is an early asset—something easy to approve without a high price tag. This reflects a general trend toward risk aversion in our industry. Large companies have primarily suffered from it, but even now you see it with VCs funding small companies. Concerns about early-stage molecules failing have led the industry to de-risk the drug discovery process, which ultimately stifles innovation.
What has led the industry to this risk aversion?
Obviously the pipeline concerns play a role, but when you think about risk aversion and human nature, it usually comes down to unknowns caused by a lack of information. We all know that the simple act of getting into a car is risky endeavor, however we do it because we have done it before, it’s familiar, and therefore we aren’t scared of it. But when the unknowns start to pile up and you don’t have the information you need to make a decision—that’s when you’re less likely to take the chance.
What’s ironic is that we in discovery wanted more information to help us find answers, but we confused having more data with having more information. We’ve gotten really good at generating data. But we aren’t good at synthesizing it so that we can make a decision. I’ve seen so many presentations and draft reports from developmental studies that are all over the map—and that’s within a program. Imagine trying to look across programs to determine results that might impact your effort? It’s chaos. I’ve watched large companies cancel programs because they don’t want to put effort necessary to gather, analyze, and understand the information it would take to make the program successful.
How can Code-N’s technology help?
Code-N has hit on a way to take all the various data resources relevant to a project and combine them to provide actual knowledge rather than an unrelated, relentless stream of facts. The sources Code-N is accessing are the public sources we are familiar with today, but while today we tap into them one at a time, Code-N has developed technology that sorts through all the information at once and interprets it. Using computers to analyze all possible pathways instantly takes the guesswork out of searching keywords in multiple passes and has the potential to turn up connections a human wouldn’t even consider. In my career, I’ve seen how making decisions requires scientists to sit with an incredible stack of information and try to make sense of it. My goal is to provide Code-N with industry feedback on its systems to ensure they will be easy for scientists to use so that they can open up new avenues for innovation.