Prepare for Genomics to Personalize Asthma Treatment

The phrase “personalized medicine” may be most familiar in the context of cancer, but it could soon have meaning in the asthma lexicon as well. The combination of electronic medical records, which make it easier to mine patient information for data about medication efficacy, coupled with technological advances that make genomic phenotyping a possiblity even for community physicians, has the potential to bring significant changes to asthma treatment in due course.

So argues Scott T. Weiss, MD, of Brigham and Women’s Hospital in Boston, in a review article on personalized medicine and asthma, which appears in the February 2012 issue of the Journal of Allergy and Clinical Immunology.¹

Something needs to be done, he says, given the increasing health care burden of asthma in the US, now estimated at $56 billion in annual medical costs, about half related to medication use.² While inhaled corticosteroid (ICS) control medication and short-acting bronchodilators can substantially reduce the morbidity and cost of the disease, the drugs don’t work in about half of patients, for reasons that relate to their genetic makeup. Even among those who could benefit, noncompliance further reduces use.^3-6

“In an ideal world,” Dr Weiss writes, “we could identify both the noncompliant patients and the highly compliant patients who are not benefiting from asthma medication and modify their treatment to improve health outcomes.”

Unfortunately, several challenges exist:

• Recurrent exacerbations often arise even among patients who appear to be getting adequate treatment.
• The phenotype (or clinical presentation) of patients is heterogeneous.
• The fact that combination treatment (ICS/long-acting bronchodilators) is the standard of care and is widely used makes it difficult to separate the genetic determinants of efficacy of different drug classes used in combination.
• Most ashtma trials are too small to allow identification of the genetic determinants of drug response.
• There are no published genome-wide association studies of bronchodilator response.

Nonetheless, there are efforts under way to improve understanding genetic issues involved in asthma and medication response, including integrative genomics studies designed to analyze genetic profiles across several clinical trials in order to identify drug-response types.⁷ The review highlights existing knowledge about genetic polymorphisms and their relationship to asthma pathophysiology and medication response.

It required 40 years to progress from the perception of leukemia as a single disease in 1950 to the era in which more than 30 types have been identified, observes Weiss. He predicts it won’t take nearly that long to reach a more sophisticated understanding of asthma based on genomic profiling.

“The goal of the right drug for the right patient is something that is an achievable translational scientific goal over the next 10 to 20 years," he writes-as long as researchers take into account the cost issues that challenge any progress in health care today.



 

References:

REFERENCES:
1. Weiss ST. New approaches to personalized medicine for asthma: Where are we? J Allergy Clin Immunology. 2012; 126(2):327-34.
2. Akinbami LJ, Liu X, Pastor PN, Reuben CA. Data from the national health interview survey, 1998-2009. NCHS Data Brief. 2011;1-8.
3. Donahue JG, Weiss ST, Livingston JM, et al. Inhaled steroids and the risk of hospitalization for asthma. JAMA. 1997;277:887-91.
4. Szefler S, Weiss ST, Tonascia J. Childhood Asthma Management Program (CAMP) Research Group. Long-term effects of budesonide or nedocromil in children with asthma. N Engl J Med. 2000;343:1054-63.
5. Drazen JM, Silverman EK, Lee TH. Heterogeneity of therapeutic responses in asthma. Br Med Bull. 2000;56:1054-70.
6. Malmstrom K, Rodriguez-Gomez G, Guerra J, et al. Oral montelukast, inhaled beclomethasone, and placebo for chronic asthma. A randomized, controlled trial. Montelukast/Beclomethasone Study Group. Ann Intern Med. 1999;130:487-95.
7. Raby BA. Genetic mapping of pharmacogenetic regulatory variation. Curr Pharm Des. 2009;15:3773-81.