Personalized medicine – reducing the cost and improving the effectiveness of health care

When my guest-bathroom toilet flap valve recently gave out due to old age, I purchased and tried out four different “one size fits all” replacement flap valves, shopping at Home Depot and different hardware stores.  They are very simple devices but none quite fit and all left the toilet leaking.  The process required four different shopping trips and left me with a non-functioning toilet for 10 days.  Then I did the smart thing and found out the brand of my toilet (an Elger) and went to a plumbing supply house and purchased an Elger toilet retrofit kit.  It worked perfectly.  The solution was personalized to the particular design and dimensional specifications of my toilet.

Physicians are trained to diagnose what is going on in an individual and to prescribe a treatment tailored to that individual and his disease condition.  Modern diagnostic tests and instruments such as MRIs have been making this process ever-more precise.  However, until now it has not been possible to make treatment choices based on the molecular, genetic and epigenetic makeup of a given patient.  We are all genomically and epigenomically vastly more different from one another than an Elger toilet is from other toilets.  So, as far as therapies go, medicine has too-often proceeded on the basis of “one size fits all” when the facts have often shown “one size misfits most.”  Finding the right prescription medicine can be like finding the right toilet flap valve, a matter of trial and error and high cost and inconvenience.

According to an article Personalized Medicine Realizing Its Promise by Edward Abrahams, Ph.D. “On average, a drug on the market works for only 50% of the people who take it. The consequences in terms of quality and cost of care are significant, leaving patients to contend with their disease and their medical bills as they switch from one drug to another until they find an effective therapy.”  Trial-and-error of drugs is particularly prevalent in some fields like psychiatry.  A psychiatrist is often unable to make a definitive diagnosis at first between the various shades of depression, bipolar and related mental disorders.  Instead the psychiatrist will keep prescribing different drugs until one or more work to control the symptoms.  Then, based on the drug that works, a diagnosis can be pronounced. 

Now, in the emerging context of personalized medicine, customization of treatment can depend not only on observable diseases conditions but also on the molecular, genomic and epigenomic makeup of the particular patient.  There is increasing evidence, for example, that combinations of genetic and epigenetic markers can be useful in diagnosing mental disease conditions. Genomic, epigenomic and other diagnostic tests are coming on the market which will tell whether a drug treatment or medical procedure is likely to work or not.   For people on an expensive year-long drug therapy, such a test could save their life by indicating that a blockbuster drug is probably not going to work and by preventing exposure to its side effects.  For the government and society, such tests could in aggregate mean hundreds of billions of dollars saved in irrelevant treatments and unnecessary hospital stays.

A special report in Gen provides some examples:

“For example, Genentech/Roche’s Avastin costs $50–$100,000 per year of treatment but works in fewer than 50% of patients. Given that Avastin may generate $12 billion in peak sales, this low rate of efficacy translates into billions of dollars in misdirected healthcare spending. A test for Avastin response, such as that in discovery by BG Medicine, could save the system as much as $6 billion per year if all nonresponders could be removed from the treatment pool. Assuming that a test of this sort is introduced at the beginning of 2013 and is 100% adopted, cumulative savings of $40 billion could be realized by 2019.”  That is not small change. 

“Genomic Health’s Oncotype Dx is a test with compelling cost-saving potential. It is used to predict chemotherapy benefit for patients who have node-negative, estrogen receptor positive (node-, ER+) breast cancer. By averting unnecessary chemotherapy, the test has been shown to save about $2,000 per patient. Extending this cost savings to the roughly 100,000 new cases of node-, ER+ breast cancer in the U.S. each year, this test could save the U.S. healthcare system up to approximately $200 million a year or about $2 billion over the 10-year time horizon under legislative consideration.” Adding up the savings for drug after drug after drug could chop an enormous slice over our annual health care costs.

With the development and adoption of appropriate diagnostic tests, the aggregate effect on health care costs and patient wellbeing could be enormous.  One approach is the development and use of specific tests associated with specific drugs or treatment procedures.  For example.  before prescribing Avastin, the oncologist could order the test being developed by BG Medicine. A better approach that will no doubt be implemented in the longer run involves building databases of patient-specific data, such as their entire genome.  In a recent blog entry, I pointed out that the cost of sequencing the entire genome of a patient is heading down to the $1,000 level probably this year and will be probably at the $100 level within four years or less. If a patient has his or her genome already laid out in such a database, many bad-choice drug treatments could be avoided by a simple computer check against the database, just like transfusions of the wrong types of blood are often avoided now.

Also, the existence of such a database could signal disease susceptibilities and the advisability of preventative actions.  “Over 1,300 genetic tests exist that signal inherited susceptibility to conditions as wide-ranging as hearing loss and sudden cardiac arrest. While not every test has a therapeutic option, a genetic diagnosis often permits targeted prevention or mitigation strategies(ref).”  One example is looking for BRCA1 and BRCA2 genetic mutations indicating a hereditary propensity for breast and ovarian cancer. “Women with BRCA1 or BRCA2 genetic risk factors have a 36% to 85% lifetime chance of developing breast cancer, compared with a 13% chance among the general female population.  For ovarian cancer, women with certain BRCA1 or BRCA2 gene mutations have a 16% to 60% chance of disease, compared with a 1.7% chance among the general population. The BRCA1 and BRCA2 genetic test can guide preventive measures such as increased frequency of mammography, prophylactic surgery, and chemoprevention(ref).

The bottom line for the government is to effectively control health care costs, do everything you can to further implementation of personalized medicine and the building of patient-specific genomic and epigenomic databases..