Traditionally, in advertising we tout the benefits of a new drug to market by the results of their phase 3 clinical trial, reporting on the efficacy and safety of a given compound. In some cases the clinical trial might be set up to compare against the “gold standard” therapy, but most often it is compared to placebo. Depending on the size of the affected population most studies are expected to be large, multi-centered, and patient’s are evaluated for an appropriate length of time as dictated by the condition being treated. In a few studies “responder rates” are reported which describe what percentage of the population actually responded favorable in comparison to those that the effect was not significant.
Individualized drug therapy allows us to identify populations that based on genetic variants, may be higher responders, or may have higher propensity toward an adverse reaction. This new science, called Pharmacogenomics identifies how potential gene drug interactions can better predict a patient’s response to therapy. This could eliminate having to endure a course of therapy that may not work, before the next level of intervention is considered. This type of assessment could help to target therapy to the individual that will best respond. In a recent Journal of American Medical Association (JAMA) article they described Pharmacogenomics as a decision support strategy that will allow physicians to individualize drug therapy, maximize the likelihood of response, and minimize risk for adverse reactions.
But can we afford to determine every ones specific genetic code before any therapy decision is made? Who will be paying for this expense? Clinical adoption of this science will be influenced by regulatory recommendations, and third-party payment. Until a cost/benefit story can be told, use of this science universally will be slow. However, with the transition to electronic medical records (EMR) there are more opportunities to look for trends and discoveries with different treatment categories across populations. For example in the Asian culture, there is a gene variant that is strongly associated with a very debilitating skin reaction when given certain seizure medication. Identifying population based responses can help us to predict response and avoid serious adverse events. With EMR there can be better follow through and record keeping of an individual’s response to therapy and sensitivities that might apply across categories. There may also be an opportunity to share patient experiences among clinician’s so trends can be observed, and population variants can be identified.
So what does this mean in advertising? Well responder rates might become one of the pivotal messages of our communication platforms. Drugs that are efficacious across a broad range of patients will become an important component of early adoption. And looking for trends with serious adverse events might help the clinician to determine which population to avoid using this medication in, rather than blanketing their concern toward every potential patient.
This is going to be a new and exciting frontier for medicine and for individualizing therapy. As is often true in science, sometimes the data collection does not answer the question but helps to identify where further exploration needs to be developed. This is a new and exciting time for more exacting pharmaceutical treatments.
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