Can Digital Health Successfully Apply Lessons Of Molecular Biomarker Development To Create Useful Measures Of Phenotype (Phenomarkers)?

By David Shaywiz, Forbes Contributor, November 24, 2013

The appeal of personalized medicine is the opportunity to provide the right treatment to the right patient at the right time.  This approach reduces patient exposure to treatments unlikely to benefit them, achieving a two-fer of protecting patients while reducing costs.

In addition, by targeting treatments to exactly the right patients, you’re often able to observe a far more profound effect than if you study the treatment in an unselected population, where the impact of the drug on the right patients can be diluted by the lack of effect on non-responders.  This enables far more efficient clinical studies.

In oncology in particular, personalized medicine has started to become real; patients often receive – or don’t receive – drug based on the molecular phenotype of their disease.  For example, a genetic test for EGF-receptor mutations is used to determine which lung cancer patients receive gefitinib (Iressa), while a test for level of HER2 expression help oncologists figure out which breast cancer patients should receive trastuzumab (Herceptin).

The important but largely unanswered question raised by the availability of digital health technologies is whether these will afford the opportunity to segment patients, and personalize treatment, based on novel phenotypic assessments as well.

In other words: can digital health technologies provide what I call “phenomarkers” that are as informative as the molecular biomarkers used in oncology?  (I define a phenomarker as an analog of a conventional molecular biomarker, but where the readout is a phenotypic measurement rather than a molecular one.)

There are so many reasons why this makes sense.  As highlighted in Tech Tonics, (and as Denny Ausiello and I have specifically discussed here and here), phenotype is the next frontier, and the technologies of digital health should enable profoundly improved measurement of the patient experience of illness.   We should be able to critically assess many more parameters, and we should be able to take an improved look at even established parameters (like blood pressure), which we should now be able to measure anywhere, anytime – rather than once every six months in the doctor’s office.

We also should be able to bring to bear powerful analytics to identify important patterns, and tease apart relevant subgroups.  It’s easy to imagine these dense phenotypic data could provide empirically useful segmentation information (i.e. which patients might most likely to respond to a particular treatment, even if we don’t understand the reason), but it’s also exciting to recognize these data might also provide novel, mechanistic insights into the underlying pathophysiology of a particular disease.

That’s the vision, anyway.

Thus far the going has been slow; I struggle to name an emerging digital health technology that has meaningfully transformed the way patients are treated, or that has provided a key mechanistic insight that has helped crack open a disease.

Admittedly, it’s still early days; there are a few tantalizing tidbits here and there, and it’s possible I’ve missed something more substantial.


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Concierge Medicine Journal (CMJ) curates breaking concierge medicine news, and editorial opinion on a wide variety of topics relevant to the practice of Concierge Medicine.

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