If I want to talk about personalized medicine (and I do), I have to begin by saying what I mean by it. (As a side note, I'll use the term individualized medicine interchangeably. Occasionally, people will use them to slightly different effect, but for my purposes, they're the same thing.) And what I mean is pretty simple - the combining of all different types of data (clinical, environmental, and genetic) to predict what diseases a person is at risk for and to identify medical treatments that will work for that specific person.
It's easy to lose sight of how far medicine has come in the past 100 years. We take for granted that most diseases are able to be treated if not cured, and we dedicate significant resources to medical research. Modern chemistry has led to hundreds of drugs that have saved countless lives. For all that, medicine can still be a crude endeavor.
Consider hypertension. It is one of the most prevalent diseases in America, and the single most common reason that people visit their doctors. In spite of that, less than half of people taking drugs to treat they hypertension actually have their blood pressure under control. Why is that? Well, partly because people don't change their lifestyles to combat the disease, but also because there is no way to identify which patient will respond to which drug. Hypertension is extremely heterogeneous, and it stands to reason that different subsets will respond to different medicines. For now, though, there is neither a way to easily assign a person to a subset of hypertensions, nor a mapping for which drug best treats which subtype.
But let's back up for a second. Why is hypertension so common? What leads to a person developing hypertension? For now the best predictors of hypertension are age (the older you are, the higher your risk of high blood pressure) and family history (if your relatives have high blood pressure, you're more likely to, also). But that casts a very wide net, and it's difficult to identify the people who would most benefit from early interventions to prevent them from developing hypertension. One potential application of personalized medicine is being able to combine all of the information available to make better predictions about who is really at risk of developing hypertension
Finding and targeting those at risk, though, will not stop everyone from getting hypertension. And the next potential application of personalized medicine is determining who will respond to which drug. This type of prediction is currently not even considered as part of treating a patient, rather the physician makes an educated guess about what drug may work and then monitors to see if the dosage needs to be increased or if another drug needs to be tried. But by identifying subsets of hypertensives and identifying which drugs work best in a subset, hypertension treatment will not only be more effective, there will likely be fewer adverse reactions and less wasted money.
Now that we know what personalized medicine is, the next three posts will cover the scientific, policy, and ethical issues that face the field. I don't intend to lay out much in the way of answers, and I also doubt that my listing of problems will be exhaustive. Rather, I want to convey a sense of the breadth of the issues that I'll be discussing in more depth over the next few months.
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