| Summary: | According to the SEER Cancer Statistics Review, between 1975
and 2005, the deaths from heart disease in the United States
declined from 37.8 to 26.6%, whereas over the same period
those from cancer increased from 19.2 to 22.8% (Ries et al,
2008). In the US alone, it is estimated that in 2008 a total
of 565,650 patients will have died from cancer, whereas
1,437,180 will have been newly diagnosed (Jemal et al, 2008).
Thus, despite undeniable advancements in early diagnostics
and progress in reducing morbidity through therapeutic
efforts, it appears that after spending billions of dollars on
oncology research since 1971, more than three decades later
the ‘war on cancer’ is still far from being won (http://
dtp.nci.nih.gov/timeline/noflash/milestones/M4_Nixon.htm).
Although pressure from patients, advocacy groups and
funding agencies is mounting, the conventional populationbased
approach for therapeutic developments in clinics still
relies on passing a series of randomized controlled trials that
depend on enrollment of many patients in search for favorable
yet averaged outcome patterns that statistically document
safety and efficacy. Research and development (R&D) of a new
therapeutic drug now takes 10–15 years at a cost in excess of
US$1.3 billion, with only 20% of marketed drugs producing
revenues that match or exceed their R&D costs (Pharmaceutical
Research and Manufacturers of America, 2008). Fueled by
the comparably modest progress made so far in pursuing this
expensive conventional route, there has been much interest
lately in moving toward personalized or patient-specific
medicine. For oncology, for instance, ‘specific’ refers to
assessing not only tumor type, size, location, patient age and
many other parameters that are already used and that result in
the conventional grading and staging of the disease; rather, it
argues for incorporating also the molecular fingerprint, or
signature, and associated growth kinetics of the patient’s
tumor when fine-tuning treatment regimen on a case-by-case
basis (Roukos et al, 2007). As everyone’s tumor is distinct, to a
degree, the ‘one-size-fits-all’ treatment strategy cannot work,
so the new paradigm.
Although few would argue against the rationale behind the
concept per se, what remains unclear, however, is how to
actually process personalized medicine when the costs on the
diagnostic side, such as that for advanced personalized
molecular screenings, cease to be the limiting factor due to a
wide range of ongoing biomedical engineering efforts (e.g.
Shendure et al, 2008). Next steps will involve having to
address as to how to design, run and evaluate clinical trials in
this new era, and how to administer personalized health care
in reality. That is, (1) following the new paradigm, the
averaged results derived from randomized clinical trials will
offer insufficient if not even incorrect guidance on how to
approach a specific case. Patient responses to a particular drug,
for instance, are known to fall into a more or less wide range
that deviates from the averaged behavior, a fact that is being
made chiefly responsible for why a particular drug works
better in some than in others. Although, in an effort to limit
adverse side effects for the patient population at large, toxicity
testing will likely have to continue to rely on a conventional
trial-based approach, efficacy assessment will have to reflect
the new paradigm in one form or another. (2) An equally
significant challenge looms on the day-to-day operations side
in clinics where at any point in time, multiple, slightly varied
treatment protocols will have to be designed, administered,
monitored and adjusted if need be. At the very least, that puts
considerable strains on the existing infrastructure. Also,
enrollment numbers will be small, in theory down to one
patient per modified regimen, with the obvious consequence
that clinical institutions will have to pool and exchange such
case-centric data and expertise so that new patients can benefit
from past experience. (3) Even if a compound survives the
elaborate evaluation processes in the current pharmaceutical
‘pipeline,’ by the time it becomes available, it lags several years
behind the pace of basic research that continued while the
drug was under development. Consequently, new data would
have become available that may point toward different, or
higher valued targets, or even question the rationale of the
initially chosen strategy altogether. Short of starting the
development process all over and facing the same dilemma
again in a few years, a workflow has to be designed that allows
including new R&D insights, also from this new case-centric
medicine, into the development process in an effort to improve
the drug’s efficacy at later stages in the pipeline—without
compromising patient safety and at acceptable costs for
industry
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