Posted by Nitin Mittal on October 10, 2016
Pharmaceutical companies have operated the same way for decades. Conduct R&D, run clinical trials, endure the regulatory gauntlet, and swing for a home run. This blockbuster-driven business model has produced miracle drugs and flops, winners and losers. And now, it’s going away.
Instead of investing in drugs whose success hinges on risky bets with their inevitable boom or bust cycles, pharma companies are pivoting to a precision medicine-based model–developing drugs to impact a specific patient’s malady or condition.
Creating such drugs will be expensive in both data and dollars. Historically, data for drug development has come directly from clinical trials or through datasets of individual patients’ insurance claims. Both offer only a limited view of someone’s medical history, one that excludes the person’s physiology, wellbeing, lifestyle, clinical profile, and genetic makeup. Can you imagine if we captured data across these dimensions to create a foundation that underpins the entire drug lifecycle?
Just having the data is not enough, of course. If we want to learn what’s hidden in the data, we’ll have to analyze and interpret it, exploring correlations between genetic, clinical, lifestyle, and other data types. Yet the scale of that analysis will quickly bury researchers since developing a new drug will involve collecting data from tens of thousands of patients–down to an individual gene–and creating enormous data stores. This leads to a lack of sharing among researchers and too much replicated work. Plus, having humans analyze such massive amounts of data will require a brute-force approach that is time-, resource-, and cost-prohibitive.
The good news is that cognitive technologies are already changing that dynamic, helping researchers extend the power of informatics and artificial intelligence to tasks traditionally performed by humans. We are starting to see it being used to generate new insights by analyzing lifestyle data and its correlation to the progression of a particular patient’s ailment. In fact, this is one of the biggest areas of investment today, wherein cognitive techniques are being used for mass personalization and engaging patients not only based on clinical and genomic data, but also behavioral characteristics. Cognitive approaches are also accelerating the pace of R&D in life science companies by analyzing scientific literature and compound libraries to identify potential drug targets. By being trained on specific data sets and then learning on their own, cognitive machines are poised to mimic and augment human-like abilities to uncover extraordinary insights.
In this context, cognitive technology is not a nice-to-have: It’s a prerequisite to the business model transformation taking place as pharma companies pivot from blockbuster-driven drug research to precision medicine. To put the three drivers of precision medicine–data, analytics, and cognition–in business terms: data is the new currency, analytics extracts the value from the currency, and AI-driven cognitive and machine learning are the disruptive force needed to harness and complete the business model transformation.
Is a precision medicine business model in your company’s future? I’d like to hear from you.
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