For the last couple years, big technology giants have invested heavily into healthcare for business solutions, cloud applications, etc., but we have yet to see a major big data and big pharma partnership that really creates reduced research and development spending and more efficient drugs.
All that may be changing soon.
Big Data and Big Pharma Come Together
Schwan makes a great point in saying, “(top technology) companies are experts at digitizing and analyzing data; they have the tools and algorithms necessary to make sense of mountains of information.”
He then added, “But what they miss is the medical knowledge, the understanding of biology. They can’t ask the right questions. They can program, but they don’t know what to program.“
These comments by Schwan make perfect sense, and sound very much like Tibco Software CEO and founder Vivek Ranadive when I spoke to him about the future of big data last year. He explained that big data could be a massive opportunity in healthcare if big data and healthcare can get on the same page and work together towards a common goal, identifying candidates with the highest likelihood of market success.
In essence, a company like Tibco or Tableau (DATA) can collect data on clinical trials, molecules and drugs that are currently FDA approved to find which combination and single agents can be most effective at treating diseases.
For a company like Roche, such data could be extremely useful.
How Big Data Helps Big Pharma
Specifically, Roche is one of the largest pharmaceutical companies in the world. It has more products than one can count, over 30 ongoing Phase 3 trials, and recently identified 66 new molecule entities. While such drug development and Roche’s $17.6 billion in operating profit is great, its near $9 billion commitment to R&D and the seven million hour plus commitment it takes to produce and develop just one drug leaves room for major improvement.
What the incorporation of big data can do is give RHHBY a better idea of what candidates and compounds work without so much testing. This is possible by pooling all of Roche’s past data on every molecular entity ever tested to identify what works best and how.
For a company that has performed as many tests and has as much data as Roche, there’s a good chance that Roche could even find significant value in tested molecules that otherwise seem lost, much like Johnson & Johnson‘s (JNJ) Zytiga.
The blockbuster cancer drug Zytiga was passed on by just about every big pharma company before it was purchased by Cougar for just $1 million. Essentially, it was tested and then considered worthless.
However, further collaboration and additional testing unveiled a major opportunity in prostate cancer, with Zytiga increasing life in patients with the disease upwards of 40% versus standard of care. JNJ then turned around and bought Cougar for a near $1 billion.
The key takeaway is that by incorporating all pooled data and visually seeing what molecular entities work and how, Roche can better identify not only obvious opportunities, but also those under the radar.
In doing so, Roche and any big pharma company with loads of clinical data can reduce R&D costs while increasing clinical trial success rates. This in turn leads to higher revenue, lower operating costs and higher stock prices.
Therefore, the start of big data and big pharma’s relationship could very well be the start of something very special for the healthcare industry.
As of this writing, Brian Nichols owns no stock in any company mentioned.