Big drug companies are not what they used to be. It is harder to find new drug candidates, to test them, and to get them approved than ever before. Drugs that are “mere chemicals” are becoming more and more complex. Frequently, new drugs require DNA interaction, which requires them to be manufactured through a mostly automated cellular process rather than just mixing the right components in the right order. Just the changes to the refrigeration requirements for these new drugs represents a challenge to drug manufacturers, pharmacies and hospitals.
Combined, these difficulties create a combustible business environment that can ignited by the pressure of expiring patents. Experts estimate that the approval process ensures that a drug company actually gets only about 12 years of exclusivity before a 20-year patent wears off. So in pharma-land, the march of popular medications to generic status forces the original developers into the famous Innovators Dilemma. Most companies face competition from the generic versions of their own previous work.
Almost everyone seems to agree that Big Pharma is in crisis. Almost all of the pharma executives that I know and respect understand that the writing is on the wall: Pharma must change or die. In order to avert this crisis, Pharma must and does embrace a series of insights about how to refactor their business.
And they are changing. Modern pharma is already using Big Data to not only search for new drugs, but to stream line and slim down bulky internal processes. From marketing, to education efforts, to investment decisions, Bigger Data is leading the way to Leaner Pharma.
To survive, Big Pharma must learn to externalize and share redundant costs. That means learning to leverage Open Source, both the software and the community, more effectively. Take a careful look at how many sponsors for the Strata RX conference are touting Open Source Big Data solutions, or how many sponsors of O’Reilly’s Open Source convention have healthcare applications. The new Lean Pharma asks one question again and again, “Is this cost creating a competitive advantage? If not, can we share it?”
But don’t take my word for it. Lilly has created a new center called Lilly Clinical Open Innovation (LCOI). Their mission blurb reads:
We believe that open innovation can transform clinical research and development. We look to engage in the open for insight, innovation, talent and wisdom to drive new capabilities to fight disease and meet patient needs
Almost every insight that I will highlight is either explicitly in this statement or it is implied by it. But this might just be marketing fluff… does Lilly really mean it?
They do. You can imagine my excitement when I discovered at OSCON the informal collaboration that LCOI had begun with a Merck employee, Janakiraman Gopinath. Gopinath is using a LCOI API to make an internal clinical trials application available to Merck employees. Gopinath found a significant bug in the Lilly API (the kind of issue that you would need to have deep familiarity with clinical trial data to even notice). He reported it to Lilly and they fixed the bug.
Consider, for a moment, the value of this interaction. First, LCOI is offering an simplified API to clinical trial data, which is a space that could use some innovation. Gopinath not only leveraged that value inside Merck, but provided insights back to Lilly that only another clinical trial expert could provide. Lilly fixes the bug, improving the value it provides internally to Lilly and enabling Gopinath to leverage the improved data for Merck. Most importantly, now the Lilly API is more valuable to Health IT innovators at large (who might not have been able to spot the problem that Gopinath found), creating new opportunities for lowered costs for all drug companies. Which brings us our next insight: With the right open collaboration, everybody wins.
Both Lilly and Merck have practical “clinical trial problems” but there is no benefit for either company to solve those problems alone. Clinical trials are a sunk cost for both companies, so the trials need to be run as close to perfectly as possible, and at as low a cost as possible. As a society we should hope that there is no competitive advantage available here, only a shared standard of excellence.
But how did this innovation actually happen? It happened because of the serendipity of an open collaboration. LCOI offered an API under standard open data terms that were reasonable enough that Gopinath did not have to ask his CEO at Merck to approve their use. The total cost of this collaboration was the cost of starting LCOI at Lilly (which is minimal dollars in the world of Pharma) and the cost of the salary of whatever boss at Merck was smart enough to hire Janakiraman Gopinath, and of course the cost of Gopinath’s salary.
The informal collaboration occurred because Lilly was smart enough to let go of control and embrace the Open Source developer community, and because Merck was smart enough to join that developer community. But it produced a unique and exemplary result. I suspect that an equivalent goal might also have been achieved if Merck and Lilly had each signed an agreement to collaborate by donating a billion dollars to improving clinical trials research. But that is exactly the kind of “throw money at this problem” attitude that Big Pharma can no longer afford.
I pressured O’Reilly Media to create the Strata RX conference for exactly this reason. We need a space where the top healthcare data scientists in the world could come together and start sharing methods, data, and software in order to achieve the holy grail in healthcare: The Triple Aim. We need the DNA guys to be talking to the Clinical Trials guys to be talking to the Patient community hackers and the Health IT standards guys. Big Data, along with the community of healthcare data scientists, will ultimately achieve the triple aim. The only question is whether the benefits of this will help our generation, or only our grandchildren. If we want to see Big Data impact healthcare in our life times we need to start breaking down the walls. Exactly the kind of walls that used to separate Lilly and Merck.
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