Big Data: Harnessing Its Personalized Medicine Power | Diaceutics

Big Data: Harnessing Its Personalized Medicine Power

June 5th, 2016

Peter Keeling

Everyone is talking about the possibilities and promises offered by big data and attempts are being made from many directions to harness the power it contains. Peter Keeling, CEO of Diaceutics, assesses where big data currently stands in relation to personalized medicine and where we need to be in order to benefit the patient.

Big data as a talking point is almost synonymous with personalized medicine at the minute. Partnerships are being forged seemingly every month between pharma and companies like Foundation Medicine, Illumina and IBM Watson, as it seeks to evaluate and integrate large scale biomarker, epidemiology and patient outcomes data into its targeted therapy programs.  Big data healthcare conferences are sold out and playing to packed audiences, highlighting the appetite that development, commercial and policy executives have for understanding how and where big data can provide an advantage to development productivity and or disease level integration.

In Boston 2016, the World Medical Innovation Forum announced the Disruptive Dozen1, an insight into the 12 initiatives that ‘will have the biggest impact in bringing novel complex health care products and services to greater levels of affordability and accessibility’. No surprise to see big data featuring here, particularly in relation to oncology and personalized medicine.

How far off every day big data use are we?

We’ve been talking about this topic for a good while now, so how far away are we from actively utilizing this vast amount of information to benefit the individual patient? If technology evolves in eras from aspiration to innovation to application, big data is right now somewhere between innovation and application. Those of us close to personalized medicine do not doubt that we are less than five years away from immuno/traditional targeted therapy combinations alongside universal and NGS biomarker panels which enable precision treatment in the armory for prescribing physicians. However, experts at the 2016 Forum oscillated between the revolutionary promise delivered via the biological and outcome insights to be achieved from NGS and other genetic level analysis, and the technological hurdles facing platforms/therapy combinations being built to bring these innovations to community level oncologists.

So this is likely to play out in two ways: drug developers will likely be able to pay the high costs of accessing these data sets as part of long cycle R&D programs, but the marketplace will struggle to adequately reimburse or incorporate it into routine clinical application using platforms like Foundation Medicine’s Foundation One for many years to come.  We are convinced that the clinical rather than the R&D application of big data has powerful potential but it depends upon major adjustments being made in the healthcare delivery systems that will benefit the most, and so clinical application faces a struggle.

The influential hands of the patient will pull big data into clinical use

There was also much discussion at the 2016 Forum about the need for new disease level models which will integrate technology and information and share the rewards with the innovators, but sadly there were few specifics on the ‘what, where and how’ of these models, probably because that information is very fragmented at the moment. Although we believe in the accelerated development of such models (in fact our own support for the PM Connective and its journey to developing collaborative models at the disease level proves this point) we at Diaceutics believe that ultimately it will be the patient (or consumer) who will pull big data into routine clinical use. The absence of the person inside personalized medicine has always been a tetchy point for us, so any new technology that can bring engagement with the individual is to be promoted 2 3.

However, we firmly believe that the companies working to make big data easily accessible to actual patients (albeit early technology adopters) are the real revolutionaries here. Good examples are Janssen with its Gut Check™ platform that allows IBD patients to monitor their symptoms and lifestyles using an app4, and AstraZeneca’s patient-centric data collection (with PatientsLikeMe)5 that incorporates real life opinions on lupus, cancer, respiratory disease and diabetes treatment into R&D for more appropriate therapies.

Personalized medicine was always about moving diagnostic information from the hands of a few experts and democratically sharing it with prescribers and patients. Ironically, big data for all its apparent complexity, provides the impetus and infrastructure to create meaning and application for patient-level engagement. Think of big data as nothing more than the mainframe computing infrastructure of the 1970s and 80s—it will be the healthcare equivalents of Google and Apple that create the simple links between the patient, the blood test and the right treatment regime.





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