In my post defining "personalized medicine" I mentioned trying to tailor a person's drug treatment to get the best possible effect. Using a person's genetic makeup to choose the optimal drug treatment is called pharmacogenomics. Put another way, this is the study of the way a person's genome influences the effect of drug treatments. Drug response is a very complex phenotype that's influenced by both genetic and environmental factors, but high-throughput technologies such as gene expression microarrays and SNP genotyping arrays allow these genetic factors to be considered on a scale never before possible.
Drug response has two separate components, each of which can be studied in pharmacogenomic terms. The first component of drug response is pharmacokinetics, or the way the body metabolizes a drug. This can be crudely estimated now with some simple genotyping. Polymorphisms in he genes CYP2C19 and CY2D6 are known to affect the rates of metabolisms of many drugs. By modifying the effective concentration of medications, these polymorphisms can either decrease the drug's effectiveness or increase the risk of toxic side effects. The second component is pharmacodynamics, which is how the drug acts to treat the specific condition. Complex diseases from cancer to hypertension are heterogeneous both in their symptoms and in their response to drugs, and some of this variability is due to genetic factors. The underlying molecular cause of the disease, then, can be used to decide which drug is best suited for which patient.
Pharmacogenomic profiling for pharmacokinetic information should lead to a deeper understanding of drug metabolism. As it stands now, a few important polymorphisms are well-known, but have next to no role in clinical practice, because the predictions that can be made with that information are of uncertain accuracy. Knowing more factors that influence metabolism of certain drugs should lead to more nuanced predictions about individual responses. Determining what genetic factors affect the metabolism of a specific drug, however, is a very difficult problem, one that requires a great deal of genetic information and a fairly large sample of people, with blood drawn at regular intervals to measure drug concentrations. This process must be repeated for every drug (and every combination of drugs, too) under investigation.
The application of pharmacogenomics to pharmacodynamic research is similar, and requires a great deal of data and effort. By considering genetic information about a group of patients treated with a specific drug, polymorphisms that mark response can be identified. Finding a number of these can lead to a signature or profile that can be used for prediction. In fact, this has already been done successfully in cancer research[1,2,3], but it is not routine in cancer treatment, both because the technologies involved are expensive and because the outcomes that are being predicted by these methods are limited. Larger scale investigations will fix the latter problem, technological advances will hopefully fix the former.
Pharmacogenomics is only beginning as an area of research, but it has the potential to improve the way drugs are developed and prescribed. AT the same time, hyperbole about the benefits of pharmacogenomics (and about how close it is to real clinical impact) has led to disappointment4. Being realistic about the potential of pharmacogenomics is important, but optimism is definitely warranted. The lessons learned in predicting other complex phenotypes will be of service in pharmacogenomics, and it is realistic to think that in the next 10 or 15 years it will be routine to screen people with common, complex diseases to choose the medicine and dosage most likely to work for them. This profiling may take significantly longer for rarer diseases, particularly uncommon forms of cancer, but the momentum is there, and as the cost of high-throughput measurements drop, the number of samples being collected will climb, pushing this knowledge closer to clinical usage.
Okay, now that we've spent some time defining our terms and delineating some of the problems it's time to start thinking about solutions. I'm going to start with the one I think is most fundamental: infrastructure. I'll talk about some strategies for developing infrastructure and encouraging investment in infrastructure by both private industry and health care providers. I have another presentation this Thursday, so the post may be delayed a day or so.
Reagan Kelly is a PhD student at University of Michigan studying bioinformatics. His thesis is focused on risk prediction algorithms for personalized medicine systems, and he is also interested in the policy and societal implications of individualized healthcare.You can read his CV for more information about him. If you would like to contact him, please send an email to reagank -at- reagank.com