Towards Estimating the Effect of Antidepressants
In previous posts I’ve written about the rather hit-and-miss business of prescribing antidepressants. Once prescribed, the problem continues as to whether or not the drug will have any positive effects. So it’s not unreasonable to question why some people appear to benefit from antidepressant medication while many others don’t, especially if their life situations appear roughly comparable.
During the extensive STAR*D trial (sequenced treatment alternatives to relieve depression) completed in 2006 and representing the largest antidepressant trial ever conducted, a mere 30 percent of patients responded to their initial antidepressant. Even so, the data collected during the trial has enabled researchers to test new ideas. Dr. Roy Perlis, writing in Biological Psychiatry, used prediction models to establish treatment response patterns. Effectively he has taken a step in the direction of inventing a calculator that could be used in the development of an individual treatment plan, adding it will allow biomarkers to be added in order to help predict clinical outcomes.
Biomarkers in medicine can mean many different things. Every time we are asked to supply a blood or urine sample, the contents are studied in order to help with a diagnosis. Biomarkers can be used to reveal if someone has taken an illicit or banned substance, or whether they have a particular disease, or have been exposed to certain toxins. The search for reliable biomarkers that can tell us something useful about depression is still active. Lines of enquiry do show promise but there is still a path to travel before we can join the dots and lay claim to a test that can physically demonstrate the existence of depression and provide information as to how best it might be treated.
"A Clinical Risk Stratification Tool for Predicting Treatment Resistance in Major Depressive Disorder" by Roy H. Perlis (doi: 10.1016/j..biopsych.2012.12.007). The article appears in Biological Psychiatry, Volume 74, Issue 1 (July 1, 2013), published by Elsevier.