If you watch the evening news or read the newspaper then there is a good chance that you have heard the term Personalized Medicine. The most common area where we hear about this is in reference to cancer treatments. But what does it mean?
When your doctor makes a diagnosis, and reaches for a treatment, the doctor is trying to match the best treatment for the individual patient. However, the matching process is not very precise. For example, take a group of 10 people with pulmonary hypertension, 5 men and 5 women. Each patient has a different etiology or cause for their PAH (Idiopathic, familial, scleroderma, congenital heart disease, diet-pill associated…).
Now all the medicines that are approved to treat PAH are broadly approved. The studies that have been done did not allow us to determine if one medicine works best for young patients or old patients, male or female, white or black, idiopathic or due to another disease. Imagine if we had a way to match You as an Individual to the best treatment. The probability that you would have a good response to the treatment would be much higher.
Why don’t we have more information about individualized treatments? In order to determine the best treatments for each type of patient we need to conduct studies that allow us to figure out who responds to what. This requires large studies and special types of testing. To date we have yet to conduct these types of studies. We are beginning to conduct studies that will eventually allow us to better match treatments to the individual.
One way is to look at a responder analysis. When we hear about a clinical trial’s results we often hear a specific number. For example, Letairis increased six-minute walk by 40 meters compared to placebo. What we don’t often hear is how was the benefit distributed. For example, if we did a study of 20 patients with PAH and gave them new Drug X vs Placebo. 10 patients would get new Drug X and 10 patients would get placebo (sugar pill). Now let’s imagine that after 12 weeks, patients getting new Drug X walked an average of 40 meters further than placebo. There are many ways to get to an average improvement of 40 meters.
For example, all patients getting new Drug X could walk 40 meters further. Another way to get to the same average improvement is for 5 patients getting new Drug X to walk 80 meters and the other 5 patients getting new Drug X to walk the same as placebo. This second scenario implies that the drug is very effective in some patients and not effective in others. If we knew how to identify the patients who were going to respond well, we could target them with this therapy and not waste time using a drug that would not work in the other half of the patients.
We hope that as we continue to improve our treatment options for PAH patients that we will soon be making Personalized treatment decisions. We are starting to evaluate patients with great responses with the hope of uncovering clues to who will respond best.