Big data’s existence is nothing new. The changing digital landscape, especially the rise of mobile and tablet platforms, has made big data even bigger, exponentially. Research and information is pouring in from so many different sources that it’s easy to become a bit overserved. With so much on tap, it’s likely some are experiencing a Big Data hangover.
Social media represents only a small segment of the big data pie, and even those stats are overwhelming: On a daily basis, there are 4.75 billion pieces of content being shared on Facebook, and 400 million Twitter tweets. More specific to pharma, the National Institutes of Health, per the ClinicalTrials.gov site, reports that in 2012, there were a total of 138,893 studies registered on the site; that averages out to approximately 380 clinical trials registered daily.
When it comes to pharmaceutical research, companies are definitely facing big data overload. Perhaps the area in which drug makers are under the most is pressure is drug innovation. Speeding drug development could clearly help with the innovation crisis, but the traditional clinical trial process is slow and costly. (Statistics indicate that the average drug developed by a major pharmaceutical company costs anywhere from $4 billion to $11 billion. In the event you were wondering, $4 billion will buy you over 220 million cases of good beer, which, true to my headline, would give you quite the hangover.)
I recently watched a TED talk by Dr. Bertalan Mesko — Ph.D, author, lecturer and “Medical Futurist.” In his talk, Mesko discusses medical information overload from the Internet, and the importance of filtering it. Mesko recognized that he had to build specific medical communities, thereby crowdsourcing medical questions and curating information from the most relevant group of individuals. The right people can be amazing filters. But if you don’t know your community, your efforts are ineffective.
The same idea can be applied to the pursuit of innovation in the pharma industry. By using new ways of analyzing and organizing “big data,” companies like AstraZeneca are utilizing a different kind of philosophy to understand drugs better in the early stages of clinical development and tailor this to the right patients. This “translational science-based” approach utilizes technology to better analyze data from the beginning, creating a more customized drug development process.
Berg Biosystems, a systems biology company, has developed software platforms to analyze pre-existing data sets and illuminate the full use of that data. This enables researchers to leverage big data analytics to create an assortment of drugs targeting very specific diseases. While this new era of medicine has brought with it even more data, it also brings the tools to better utilize this data. In terms of drug innovation, researchers now have the power to decrease the time it takes to bring drugs to market because they can focus on specific patients who are more likely to benefit from certain kinds of drugs and treatment options.
Big data is getting bigger, but Big Pharma is getting smarter. I’ll drink to that.
