Ramesh J Chougule, associate vice president of the life sciences industry unit at global IT firm Infosys, talks about how big data is shaping the pharmaceutical industry.
For pharmaceutical businesses the past decade has been marked by turbulence and upheaval. The boom days when ‘blockbuster’ drugs earned their creators untold billions are now long past; the most lucrative patents have expired or are expiring. And in an industry where just one failed R&D project has the power to rock a company to its core, there will be no easy fixes or quick wins to replace lost revenue. But all is not doom and gloom however, and pharma companies can expect to find salvation in what has become one of their most important assets: their data estate.
Success in this tougher era will come down to the following: the ability of pharmaceutical companies to speed up the R&D process and to make it significantly more cost effective. When one considers that out of every 5,000 R&D products only one completes the drug development process and, of those that do only one in five actually returns its R&D investment, it becomes apparent just how urgent a requirement this is.
To meet this challenge, manufacturers will need to transform themselves away from current siloed operational structures towards more unified and inclusive models. This shift will require greater openness within R&D organisations’ various departments, but it will also mean manufacturers will need to be prepared to work more effectively with third party organisations – and even competitors – to create marketable products.
This new era of collaboration will not be without its challenges. Pharma companies will need to make collaboration and inter-agency research as smooth and secure as possible. Collaboration with clinical chains, research institutes, Pharma companies, CROs, CMOs and distributors will be key for efficient drug development. The good news is that new technologies, such as big data analytics and cloud computing mean that pharmaceutical companies will be able to do just that.
Take big data analytics. These tools, which comprise advanced analytics engines, simulation tools and machine-based discovery technologies, allow R&D organisations to mine data in order to uncover new opportunities and predict the most profitable research outcomes. Crucially this advance is coinciding with new openness in research data, helping to improve the accuracy of findings and leading to new insights.
In the era of personalised medicines, co-relating the clinical trial outcomes with the patient characteristics and bringing out the patient relevant doses and formulations in short time frame will be an art rather than a science. Big data will help identify the right subsets of relevant data element, run the analytics and provide the insights will act a key to this process. Manufacturers will need to build the supply chain and operations as flexible as car or computer manufacturers to respond to dynamic demand quickly at reasonable cost.
To capitalise on this trend, pharma companies should look to invest in data standardisation, integration, and interoperability. The reason for this is simple. Through the course of the clinical development process a huge amount of data is created, much of which is not exploited to the extent it should be. Rather than being held by the clinical team in silo, it should be integrated with data from the discovery phase, so that the company as a whole can garner insights that could result in new drugs or help avoid costly failures.
Drug re-purposing will be a more compelling business case as big data technology evolves. The insight which a company is able to capture will help to shape the pharmaceutical R&D process for years to come. However in the short-term (over the 3-5 years), these companies will need to create a roadmap to implement this and do so in a phased manner.
It is through such seamless integration of data assets that the pharmaceutical industry will be able to thrive in spite of the challenging market conditions of aging population and shift in emerging economies. To make the most of this opportunity, pharma businesses must put in place the right technology while also reorienting their businesses around a more collaborative model.
