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Pharmaceutical Big Data Analytics Promises a Healthier Future

June 5, 2014


“Big data is transforming the way drugs are being developed and prescribed,” asserts Gail Dutton. “Among the innovations: drugs that already have been approved are reformulated or repurposed for other ailments.” [“How big data is reshaping the drug industry,” Tech Page One, 21 March 2014] Real estate billionaire Carl Berg also sees an opportunity to leverage Big Data in the pharmaceutical industry to make money. He has demonstrated his commitment by backing a new startup called Berg Pharma. Nick Paul Taylor reports, “The features of Berg Pharma’s discovery platform read like a bingo card of hyped approaches, with Big Data sitting alongside genomics and artificial intelligence.” [“Billionaire-backed startup puts Big Data, AI at the heart of drug discovery,” FierceBiotechIT, 6 April 2014] One of the reasons that healthcare costs have skyrocketed is that drug costs continue to soar. The pharmaceutical industry argues that researching and developing a new drug, testing it, and getting approval to sell it is a long and costly process. What they charge for medication needs to cover all of the company’s overhead costs, including the cost of research and development, as well as production, advertising, and distribution.


Berg Pharma believes it can help bring down those R&D costs by using Big Data analytics to speed up the drug discovery process. “Berg has tried to push expectations,” reports Taylor, “by claiming it can halve drug development time and costs, but to date the claims — like some of the technologies on which they are based — remain unproven.” To be fair, in order for Big Data analytics to play a significant role in drug discovery the best data must be available. Dutton reports, “Historically, drug development has been relatively isolated, with little information sharing among researchers.” Several recent initiatives could change that. For example, Dutton notes, “Now … scientists have the ability to tap information from public and controlled-access databases, mine real-world data from insurance claims and crowdsource additional information. Consequently, much of the research work shifts from data generation to data analysis.” Dan Munro adds, “In the course of one short week, no less than 3 different models have emerged for sharing big data in the pharmaceutical industry. The highest profile of these — called Project Data Sphere (PDS here) — was announced … with the official opening of an online resource to share clinical trial data for use in cancer research.” [“,” Forbes, 8 April 2014] Below is a short video about Project Data Sphere that clearly explains why sharing data is important.



Munro reports, “The number of available data sets available today is fairly small (9), but the list of companies committed to providing data is impressive and includes AstraZeneca, Bayer, Celgene, Janssen Research and Development (an affiliate of Johnson & Johnson), Pfizer, Memorial Sloan Kettering Cancer Center and Sanofi U.S.” He continues:

“The primary objective, of course, is to accelerate drug discovery and development. From the website, the underlying objective is posed as a question this way: ‘What if we could share, integrate and analyze our collective historical cancer research data in a single location?’ Formal registration for professional researchers will be required, but there will be no charge for accessing the research, uploading data sets and access will be available online globally.”

Data sharing in the healthcare field is not an easy thing to do. Pharmaceutical companies have good reasons to guard their proprietary research and protect their profits. Healthcare providers have good reasons to guard personal data gathered during diagnosis and treatment of patients. At the same time, everyone in the medical field understands that data sharing offers the best opportunities for finding life-changing breakthroughs. Shu Zhang notes that “McKinsey & Co. Inc., a global consultancy, has predicted big data could reduce research and development costs for pharmaceutical makers by $40 billion to $70 billion.” [Medill Reports – Chicago, 12 March 2014] She indicates that McKinsey analysts are optimistic that “an era of open information in health care is now under way,” She continues:

“There are plenty of data sources for big pharma to tap into. Thousands of online patient communities grouped by various diseases now exist online. PatientsLikeMe.com connects more than 220,000 patients in more than 2,000 condition groups. The Association of Cancer Online Resources links more than 200 cancer support groups. … The volume of data is exploding at a speed no one has ever imagined before, industry experts say, and that is challenging drugmakers’ ability to transform the information into additional revenue. In general, big pharma is a risk-averse industry, analysts say, and it may take 10 to 20 years for a new idea to become a reality. Big pharma’s big data efforts are still in their early days.”

Drug discovery is not the way that Big Data analytics can play an important role in the pharmaceutical industry. As Dutton noted at the beginning of this post, Big Data analytics can also help drug companies find new uses for old drugs. She explains:

“NuMedii is mining disparate databases to identify existing drugs that can be repurposed or even refined to be safer and more effective. NuMedii’s big data approach already has predicted the efficacy of drug repurposing for treating colitis — a form of inflammatory bowel disease — small-cell lung cancer and other conditions, according to Scott Saywell, vice president, corporate development. One recent finding, for instance, suggests the epilepsy drug topiramate also may be effective in treating inflammatory bowel disease.”

Big Data analytics can also help companies in the pharmaceutical industry lower costs by helping them reduce operating costs. Rich Sherman, Principal Essentialist at Trissential, writes, “The industry finds itself in the eye of a perfect storm. Profits are falling from the patent cliff, pressuring companies to adopt new supply chain value propositions. Regulatory compliance and the Affordable Healthcare Act create process and cost pressure. The cold chain manages the Cs: challenges from complexity, connectivity, compliance and continuum of care. And, above all is the shift from fee-based services to outcome-based care and payment.” [“‘Perfect Storm’ in Pharma Vertical Raises Value of Supply Chain Management,” SupplyChainBrain, 11 March 2014] In response to this perfect storm, Sherman predicts, “Senior management will take new interest in supply chain management as a strategic lever to adapt to the market transformation. Companies proactively initiating enterprise transformation programs will capture the opportunity while companies that react to the new market may risk falling off an operations cliff.”


Brian Hudock, a partner at Tompkins International, agrees with Sherman that pharmaceutical supply chain management is going to play a significant role in helping companies keep costs in check. “Supply chain leadership will be critical,” he writes, “to leading optimization, to control costs and to maintain a safe, secure market delivery system.” [“What Is Driving the Pharmaceutical and Healthcare Supply Chain?SupplyChainBrain, 11 March 2014] Hudock indicates that efficiencies will be sought in areas such as network configuration, logistics, manufacturing, and inventory management. He notes, “Many of these decisions and subsequent changes will impact the entire supply chain, and must be made as part of a cohesive strategy to generate sustainable improvements.” That is why Big Data analytics needs to play a significant role in providing actionable insights that achieve desired cost savings. “Organizations must take the first step,” Hudock insists, “by establishing the current condition of the supply chain and developing a road map to overcome gaps and capture opportunities.” Big Data analytics can help. Dan Munro concludes, “Ultimately, the big promise of big data is much more signal — and a lot less noise.”


In many ways, the pharmaceutical industry is just beginning to understand how Big Data can be used to lower costs and increase profits. And there are skeptics who believe that Big Data analytics are not going to match the hype about increasing the speed and efficiency of drug discovery. Hopefully, the skeptics will be proven wrong and Big Data analytics in the pharmaceutical industry will provide us a healthier (and more affordable) future.

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