P4 Medicine – How do we get from current practice to future progress?

In the current issue of Nature Reviews in Clinical Oncology, Stephen Friend and Leroy Hood – the man who first described predictive, personalized, preventive, and participatory medicine as “P4 medicine” emphasize that clinical trials of the future will need to be designed so they fully capitalize on P4 medicine. Specifically with the advent of high-throughput genomic medicine, Friend and Hood note that science is experiencing a paradigm shift: what we once thought were single diseases, are in reality multiple distinct molecularly defined disease states. The natural extension is that trials previously needing 10,000 patients to show a benefit will now need tens of thousands of patients to have adequate statistical power to detect the same level of benefit. Even in large academic medical centers, the authors note, it will be difficult or even impossible to rapidly accrue proportionally small patient subsets in which personalized interventions can be feasibly explored and tested. The authors suggest the use of patient driven networks to obtain the patient numbers needed for these trials.

We at the center typically think about patient-physician interaction and patient empowerment, activation, or participation when we talk about the “participatory” P of P4 medicine. Friend and Hood extend the definition of participatory to include efforts by patients to enroll themselves in networks whose goal it is to provide access to clinical trials that would otherwise be beyond the reach of single, brick and mortar institutions. Given the authors’ backgrounds, they are understandably preoccupied with the genetics and molecular aspects of such trials, but their concept still applies to models of personalized medicine that may not involve molecular data.

Even for purely non-genomic based disease models, we are beginning to appreciate the importance of individual variation in response to therapy. We already have the tools to perform personalized, risk-based prevention and treatment for many disease states using family history, biochemical markers, and patient preference. For prospective health care to reach its maximum potential in the future, we will need to develop new models that allow iterative improvements in personalization and intervention outside of current models of clinical trials. Much in the way that the physiology-based practice of medicine of the 21st century came to embrace evidence based medicine (EBM), personalized medicine will likely require some analogous, personalized equivalent to maximize its full potential . One possibility is patient driven networks as proposed by Friend and Hood. Another is novel statistical modeling that would allow us to personalize interpretation of conventional clinical trials.

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