Pharmacogenomics is an exciting aspect of Personalized Medicine

Pharmacogenomics is an exciting aspect of Personalized Medicine
Alison Motsinger_Reif
Alison
Motsinger-Reif

While personalized medicine can rely on a number of different biomarkers, demographic characteristics, etc., the overall goal is to understand variation among individuals and their response to drugs. The drugs we have available today really can do amazing things, but because of that, they're actually quite dangerous unless they're used properly.

Any time you are trying to understand variation in individuals, genetic information needs to be considered. While I of course appreciate all advances in personalized medicine, my own personal passion is to incorporate genetic data into this framework. What my group really wants to do is individualize drug therapy, and the way we're trying to do that is to take advantage of genetic differences among people.

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So, this is a field of pharmacogenetics, and in this post-Human Genome Project era, it's become pharmacogenomics, meaning that we don't just focus in on a single gene; we now have the technical ability to scan the whole genome and try and understand which genes make all of us respond slightly differently to these wonderful drugs that the physicians have available to treat all kinds of diseases that we couldn't treat before. The new era of readily accessible data presents all sort of interesting statistical challenges – from variable selection, dimensionality reduction…. the list could go on and on.

So, one of the critical factors with regard to our ability to use this amazing new science is for us to collaborate with each other, across institutions and disciplines. I have been lucky enough in my career to both collect high throughput genomic data related to personalized medicine as well as develop statistical and computational tools to handle such data. This only can be done with the deep types of collaboration that IMPACT supports.

As the field of pharmacogenomics advances, we are getting more exciting translational examples. From individualized dosing with warfarin, to HLA screening in drug testing, and individual screening in abacavir, the number of personalized medicine models have been booming. Recently, I had a student look through the World Health Organizations Essentials medicine list, and then looked at how many of the oral and injectable medicines had at least one gene found to be important in the response to that drug. The answer was over 70%! While most of these findings are no where near the clinical translation stage, this is exciting motivation to keep working on methods to make genetic information useful personalized biomarkers!


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