Genetic Influences on Response to Drug Treatment for Major Psychiatric Disorders by Janusz K. Rybakowski & Alessandro Serretti
Author:Janusz K. Rybakowski & Alessandro Serretti
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham
4.1 Background and Motivation
Around 1/3 of individuals who receive initial antidepressant treatment for major depressive disorder will not reach symptomatic remission. A subset of these, perhaps approaching 50 %, will not remit despite additional antidepressant treatment [31]. Such nonremission in the face of adequate antidepressant trials is referred to as treatment-resistant depression [9], a concept first characterized more than 40 years ago [15]. Both the retrospective assessment of antidepressant treatment history [24, 37] and the definition of treatment resistance itself have varied. In general, however, treatment-resistant depression is often taken to refer to the absence of remission following at least two adequate treatment trials in the current episode. (While past definitions often required two trials of medications of different classes, more recently even two SSRI failures may be considered TRD, on the basis of evidence from studies such as STAR*D suggesting similar probability of response to next-step treatment regardless of medication class [31].
The clinical rationale for studying treatment resistance relates to the consequences of more prolonged depression: a longer period of vulnerability to suicide, functional impairment, and poorer quality of life. Treatment-resistant depression makes a major contribution to the cost of depression as a whole: directly, by consuming more treatment resources and, indirectly, by increasing the cost of other comorbidities across medicine. For example, one claims study found medical costs 40 % greater among individuals with TRD [13].
Pharmacogenomics is often tied to personalized medicine, the effort to match individuals to treatment most effective for them. In the case of TRD, however, even stratified medicine – achieving more precise estimates of risk for multiple treatment failures, even where the specific effective treatment cannot yet be predicted – could have profound public health implications.
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