Personalized approaches to treatments will continue to drive biopharma in 2016. The current belief is that personalized medicine allows us to better match patients to therapeutics based on their genetic profile. This needs to evolve, especially in the context of complex disease outside of cancer (e.g., autoimmune disease). Some have broadened the definition and taken a systems approach, using a large array of datasets mined by network analytics to identify targeted treatments. Many nonpharma companies such as GE and Dell have entered this space using cloud computing, pattern-matching, and Big-Data science. The fundamental issue with the current approaches is the belief that static data from patients coupled with state-of-the-art computing and analytics engines will generate predictive models to match a patient with a therapeutic. This assumes an individual’s capacity to respond to a therapeutic stays constant over time, which we know is not the case. The key to unlocking the potential of precision medicine is the trifecta: therapeutics, diagnostics, and analytics.
President, CEO, and founder of ImmusanT, Inc., an early-stage company focused on peptide treatments for autoimmune diseases. She has more than 20 years of industry experience.