Two questions we need to ask ourselves: What is personalized
medicine and why would we want it?
Professor Jeremy K Nicholson of the Imperial College,
London, defined personalized medicine as "effective therapies that are
tailored to the exact biology or biological state of an individual" [1]. Such tailoring of a
treatment, say for your high blood pressure, would require your doctor to evaluate
your biochemical and metabolic profile in order to prescribe you the most
effective drug or treatment at the most effective dose, with the least possibility
of side effects.
Now, why would we want this?
Simply because we don't have it. Because our current drugs do not work optimally in most people [2]. But don't just take my word for it. Take that of Dr. Allen D. Roses, head of the Drug Discovery Institute at Duke University School of Medicine. In an interview he told a UK newspaper, The Independent, that more than 90% of modern drugs work, at best, in 30-50% of the people. He said that in 2003. At the time, Roses was also senior vice president for genetics research and pharmacogenetics at GlaxoSmithKline.
Simply because we don't have it. Because our current drugs do not work optimally in most people [2]. But don't just take my word for it. Take that of Dr. Allen D. Roses, head of the Drug Discovery Institute at Duke University School of Medicine. In an interview he told a UK newspaper, The Independent, that more than 90% of modern drugs work, at best, in 30-50% of the people. He said that in 2003. At the time, Roses was also senior vice president for genetics research and pharmacogenetics at GlaxoSmithKline.
Contrary to what you might think, Roses did not reveal any nasty industry secret. What he said is plainly visible for everyone who can read the results of clinical trials through the lens of statistics. I simply quote Roses for effect. After all, he knows what he is talking about. Contrary to
many medical doctors, who have an amusingly limited grasp of the basic
statistics used to interpret and present the results of clinical trials. Just how
limited, that has been recently demonstrated for the case of cancer screening in
a mock-up trial investigating the understanding of practicing physicians [3].
Before I tell you the results of this trial, let me make you
understand what it was about. One big question in cancer screening is whether
screening helps to reduce the number of people dying from cancer. Let's
take a hypothetical example, and here I reuse the one which the study's authors
used to explain statistical outcomes. Let's say, cancer was detected in a group
of people at age 67. All of them died of their cancer at age 70. The 5-year
survival rate from diagnosis would stand at 0% (they all died before 5 years were over). Now imagine that all those
cancer cases would have been detected at age 60 with a screening test. And also imagine that all of them still died at age 70. In this case the 5-year survival rate
would have been 100% (they were all still alive at 65). You see the issue: the survival rate was better with screening, but the rate of dying remained the same. In epidemiology we call
this sort of thing lead-time bias. That is, simply detecting a disease earlier
might lead to an improved survival rate which has, in fact, nothing to do with
improved survival. Such lead time bias is rarely an all-or-nothing thing as in
this hypothetical case. Most of the time it comes in degrees. But in any case,
it would help you as a patient, if your doctor was able to see through the
reporting, and to question the clinical relevance of the results so presented. Your
doctor should look for the mortality rate, the rate of dying, not the survival
rate.
Back to the results of the mock-up trial about physicians' interpretive skills of clinical research publications. If the results of this mock-up trial are representative of
the population of your doctors, then you should be worried. Of the over 200
practicing physicians enrolled in this trial, fully 76% would recommend you
this useless screening test. They considered an improved 5-year survival rate as prove
for the test's efficacy! These were not undereducated physicians of a third
world country, mind you. They were randomly selected from the Harris
Interactive Physician Panel, which is representative of the general U.S.
physician population.
OK, you may say that this was a test related to cancer
screening. What has it got to do with understanding the efficiency of a drug,
which your doctor prescribes you? Well, maybe your doctor aces the statistics
test on drug trials after he has flunked the one on cancer screening. If you believe that, you probably also believe in the tooth fairy and in Santa Claus. But you may have another question: Can trial results be
presented in such misleading ways? Aren't researchers supposed to report their
results honestly and correctly? And what use is the peer-review process which
every published paper has to go through?
With 70% of all medical research being financed by the
private sector, data are a commodity. So, whether you develop a screening test,
a drug or a treatment, you will want to dress it up as a magic bullet. Because
when you have the magic bullet for, say high blood pressure or high
cholesterol, it will make it into every physician's armory. That's where the
money is. It's certainly not in personalized medicine, which may find your
competitors' drugs as more suitable solutions for a variety of cases.
Which
brings us back to personalized medicine. I have told you in my previous post
how much it costs to develop a drug. Which is why Big Pharma would love to
concentrate its research on the areas where the probability of success is high
and the potential risk of failure is low. That's the area of follow-up drugs, drugs of the same class as established drugs, but with incremental
improvements over the older version. Ironically, our health care system discourages
this type of pharmacological research. Incrementally improved drugs are
typically reimbursed at the same rate as older drugs. Not much profit potential
there. Particularly when competition is fierce.
Which is why Big Pharma looks for new grounds, that is new
therapeutic classes, for which, of course, there need to exist a large market [4]. Again, individualization is
certainly not desirable, as it would fragment any market. There is another
draw-back: when you break new grounds, it takes a lot longer to get off that
ground with some new product. Which is what we see in the FDA's records of drug
approvals over the past 10-15 years [5]. Ten years ago the FDA
approved on average 90-100 new drugs every year. For the past few years this number
has dwindled to 20-30 drugs per year, with the average development period for a
drug increasing from 10 years to 14 years. Seven of those years are locked up
in the clinical trials required by the FDA. Faced with these risks and costs, how eager,
do you think, is Big Pharma to develop niche products for individualized
medicine?
Even if we didn't have all those economic issues,
individualizing medicine is not as easy as making some genetic test and reading
the right drug combo and dosage for your ailment from it. True, genetic testing
has become possible and prices are coming down. But to know your
organism's blueprint doesn't mean to know what your organism does with this
blueprint. In my earlier post I have explained about epigenetics, and how environmental
and behavioral factors have a great influence on how your genes play out in the
final version of "you". I'm afraid, without this knowledge we can't
get individualized medicine off the ground. Not to the extent it exists in most
people's fantasy.
How about personalized prevention? What's the big difference
to personalized medicine? Well, for one, we don't need to develop
a drug. When I talk about prevention, I talk about preventing what kills most
of us today: heart disease, stroke, cancer, and diabetes. Actually, diabetes
per se does not kill us, it's those cardiovascular diseases which ride on it.
Anyway, to prevent them and diabetes and many cancers takes only some
modifications to your lifestyle, chiefly not smoking, not being overweight,
being physically active and eating a healthy diet. Any of these comes without
undesirable side effects. And for all of them an incredibly large number of
studies has investigated their effects under virtually all possible
combinations of risk factors, biomarkers and population characteristics.
What doesn't exist is the knowledge of what will work best
for you. For two reasons: First, most of this research has been correlated with
our classical risk factors. In an earlier post I suggested why these risk factors really suck when it comes to
predicting your risk for disease or your health career. Second, there is no
knowing how you will react to any intervention even if a research paper tells
you that this-and-this exercise routine has cut blood pressure in the participants
from 140 to 120 mmHg. Each participant will have experienced a different effect
on his blood pressure, ranging from a lot more to no effect at all. The 20-mmHg
reduction is merely an average value. We would need to know how similar you are
to which participant to tell you exactly what you might expect.
These are the two issues which we work on in my lab: getting
away from inconclusive risk factors to what really predicts health, disease and
longevity. And making this trial-and-error approach a systematic one. Instead
of working with risk factors we have identified key organic functions which
predict health and disease much more accurately than risk factors do. And
instead of dishing out the generic
"spend-150-minutes-per-week-on-exercise" advice we are building a
database of biomedical knowledge which will match your profile with the most
promising exercise and dietary interventions to help you achieve your personal
goals with the least possible effort. And to monitor the effects of your
efforts on your organic functions, we are developing tools for you to precisely
measure them. For convenience's sake, preferably at home, or at least in your
fitness center, at your office or your doctor's practice.
We do walk entirely new ways to achieve all this, but we
never stray from the scientific method. I will, in the twice-weekly postings of
this blog, report occasionally on the progress we make. I can't hold my tongue,
simply because this work is so fascinating and exciting, at least to me. Of
course, I do know that most people are obviously not interested in their
health. Judging by the fact that less than 2% of Americans achieve ideal health
metrics [6]. But for those who really
want to achieve chronic health and functional longevity, we will have something
to offer. In fact, I have something right now:
With overweight being one of the biggest
issues, we have developed a little tool with which you train what we call a 6th
sense for your calorie balance. We have tested this tool in a successful
proof-of-concept study. Which is why I would like to invite you to use it. Free of charge, no strings attached. Except for the following three:
First, bear with us for the design of this web-based tool.
It can't compete with what you are used to from the design gods of Apple. Second, give me
your feedback and suggestions. And third, use it as it is intended to be used:
daily. You'll see what I mean when you get there.
You can find it on facebook. Just type the name "adiphea" into the search bar and click on the app. Or call it up directly from here. It doesn't cost anything, and there is no advertisement other than what facebook
puts on all our pages. The tool itself is described in all details on its
app-page on facebook. Most of the explanations come in the form of short
videos. Which is why I'm not going into details right here. Only one thing I
need to mention: Ideally, you should have a body-fat scale instead of the
regular bathroom scale. Body fat scales calculate your body water, too. And the app works best
when you enter body water together with your weight daily.
We have set aside a limited contingent for users who are truly
interested to work on their health and on their weight in an entirely new way. For
those who are determined enough to use our tool properly and thereby help us to
perfect it, it will remain accessible free of charge. For all others,
utilization will be terminated after one month.
If you are a coach, operate a fitness center, run a company or a medical practice,
and you want the app for a group of your clients, staff or patients, talk to me.
You'll find my email on my lab's website (www.adiphea.com) . I will arrange for
you to get administrative functions, so that you can manage your clients. And not to worry, the tool is built on top of an electronic patient data file, which
meets the strictest data security and privacy requirements.We also do not use your email address for anything else than responding to your inquiry.
Let's see whether we can make personalized prevention fly. Big
Pharma certainly wouldn't like it. They can't make money from chronically
healthy people. But you could be on your way to NOT become one of the 50-70% of people in whom Big Pharma's drugs don't work so well. Now, is that an inducement or what ?
Nicholson, J. (2006). Global systems biology, personalized medicine and molecular epidemiology Molecular Systems Biology, 2 DOI: 10.1038/msb4100095
Wegwarth O, Schwartz LM, Woloshin S, Gaissmaier W, & Gigerenzer G (2012). Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States. Annals of internal medicine, 156 (5), 340-9 PMID: 22393129
Pammolli, F., Magazzini, L., & Riccaboni, M. (2011). The productivity crisis in pharmaceutical R&D Nature Reviews Drug Discovery, 10 (6), 428-438 DOI: 10.1038/nrd3405
Loscalzo, J. (2012). Personalized Cardiovascular Medicine and Drug Development: Time for a New Paradigm Circulation, 125 (4), 638-645 DOI: 10.1161/CIRCULATIONAHA.111.089243
Yang, Q. (2012). Trends in Cardiovascular Health Metrics and Associations With All-Cause and CVD Mortality Among US Adults JAMA: The Journal of the American Medical Association, 307 (12) DOI: 10.1001/jama.2012.339
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