Skip to content. Skip to main navigation.


Blog


  • December 16 2010


    Prescription doesn't seem to be effective, can doctor look at genomics test results and figure out why?

    • By: ,

    swen_fig1I received the following email:

    My doctor put me on metoprolol [Lopressor or Toprol-XL] for my high blood pressure about 3 months ago, and increased my dose to 200 mg twice a day about a month ago, but so far it's not working.  My blood pressure hasn't changed a bit. Is there something in my genomics SNP results here that can explain this?  Should I expect it'll just take awhile?  Should I be on a different drug?  Here are my SNP microarray results. What should I ask my doctor when I go for my check-up in 2 weeks?

    A significant percentage of patients are nonresponders to beta-blocker type blood pressure medications like metoprolol. There are a couple of SNPs in the beta-adrenergic receptor gene ADRB1 on Chr. 10 that account for the a large percentage of the variability in blood pressure response (or lack thereof) to beta-blocker medications.

    For people who have the 'normal' variants of these two SNPs, beta-blockers ordinarily produce from 10 mmHg to 15 mmHg or more decrease in blood pressure.  For other variants at these SNPs, the blood pressure response is much smaller or, in some cases, a complete lack of response. Your results for SNPs rs1801252 (AG) and rs1801253 (GG) indicate that you are one of the approximately 22% of people who are non-responders to this and related beta-blocker medications.

    With non-responder genomics like this, even further beta-blocker dosage adjustments are almost certain to be ineffective for lowering blood pressure.

    Your doctor will know other alternative medications that have a better chance of working for you to reduce your blood pressure.  You can print this blog post and download and print the attachments to take with you to your appointment if you wish.

    I have prepared a simple Excel spreadsheet for anyone who wants to enter their genotype results for these two SNPs and see what the blood pressure-lowering response is likely to be.  The equations in this Excel sheet are built from data published in the journal literature, including the paper by Johnson and coworkers (link below). You can see from the spreadsheet that the "logic" for interpreting the genomics results is not complex or difficult. In fact, it's downright simple!

    This is just one typical example, involving a very commonly-prescribed medication type and involving dramatic personal variations in people's responsiveness to it, for one of these medicines' intended purposes, blood pressure management. But, today, there are hundreds of different types of medications for which there is adequate published "pharmacogenomics" knowledge (specifically, strong evidence that has been validated in multiple, independent studies), sufficient to guide hundreds of practical, safe, and effective applications, in terms of selecting the best medications and dosages for a person.

    Despite this, in general physicians have over the past 10 years been slow to learn about the published pharmacogenomics knowledge that's become available, and slow to learn how to put it into practice. There are a couple of articles below that discuss this issue, and what might be done to improve the situation.

    The fact of the slow uptake of pharmacogenomics knowledge by clinicians might seem peculiar, given that they are lifelong learners and self-starters about other things. On their own, they learn about myriad other kinds of advances each year, and they adopt them and routinely apply them in their treatments and decision-making.  The difference in those other areas is that competitive marketplace forces and regulatory compliance and tort pressures provide powerful external motivations for them to learn about and apply new techniques and new information.  In the case of pharmacogenomics, there are up to now no strong external motivating factors.  But increasing consumer demand for personalized medicine, in exactly the way that you posed your question at the top of this blog post, is one vivid form of market pressure. How can any reasonable clinician who wants to do the best for her/his patients not be persuaded by patients and family members asking things exactly in the way that you are doing, coming with your direct-to-consumer test results in-hand?

    "In mechanism-based medicine (MBM), it is already common practice to adjust drug dose to age, liver and renal function. Pharmacogenetics (the study of variations in DNA sequence as related to drug response) is trying to obtain comparable acceptance among these factors. Nevertheless, we experience a lack of enthusiasm in implementating pharmacogenetics, especially when compared with adaptating drug dosing to altered liver or renal function or to bodyweight." -Bob Wilffert, Department of Quality and Patient Safety, Zorggroep Noorderbreedte, Leeuwarden, The Netherlands

    Thank you for your question.  If you like, please feel free to email me after your doctor visit, and let me know how the discussion went and what decisions were made.

    Additional Resources:

    http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=rs1801252

    http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=rs1801253

    Johnson J, et al. Beta-1-adrenergic receptor polymorphisms and antihypertensive response to metoprolol. Clin Pharmacol Ther 2003; 74: 44–52.

    Liu J, et al. Beta-1-adrenergic receptor polymorphisms influence the response to metoprolol monotherapy in patients with essential hypertension. Clin Pharmacol Ther. 2006;80:23-32.

    Swen J, et al. Translating pharmacogenomics: Challenges on the road to the clinic. PLoS Med. 2007;4:e209-16.

    Wilffert B, et al. From evidence based medicine to mechanism based medicine: Reviewing the role of pharmacogenetics. Pharm World Sci. 2010 Nov 4 [ePub ahead of print]

    Woodcock J, Lesko L. Pharmacogenetics: Tailoring treatment for the outliers. N Engl J Med. 2009;360:811–3.

    Attachments:

    beta_blocker_antihypertensive_advisor.xls

    swen_plos_med_2007_4_e209.pdf

    wilffert_pharm_world_sci_2010_nov_4.pdf  

    • Comments (0) | Rating: 0/5
  • Comment
  • Bookmark
  • Print