Predictors of Antidepressant Treatment Response: The Emory CIDAR

Emory University, Atlanta GA

Investigators

Linked publications & trials

Abstract

[unreadable] Major depression (MDD) is a highly prevalent disease associated with significant morbidity and mortality and[unreadable] estimated to be one of the leading causes of disability worldwide. A variety of antidepressant drugs,[unreadable] psychotherapies, and non-drug somatic therapies have demonstrated efficacy in acute treatment trials.[unreadable] However, the majority of patients in these trials do not attain remission, increasing the risk for the[unreadable] development of chronic depression, suicide, substance abuse and several serious medical disorders. A[unreadable] major unmet need in the field is the identification of predictors of response to individual treatment modalities,[unreadable] as has been utilized other branches of medicine such as oncology and infectious disease, to improve patient[unreadable] outcome. In view of advances in functional brain imaging, molecular neurobiology and genetics, and a[unreadable] number of promising findings in small studies, it is propitious to conduct both hypothesis-generating and[unreadable] hypothesis-testing studies to determine whether a concatenation of factors, taken together, predict[unreadable] antidepressant treatment response. To achieve that goal, we propose a 3-arm, 12-week treatment trial led[unreadable] by Philip T. Ninan, M.D. of 400 treament naive adult depressed patients randomized to one of the following[unreadable] treatments after a 1 week placebo treatment period: 1) escitalopram, an SSRI; 2) duloxetine, an SNRI and[unreadable] 3) CBT. A series of behavioral and biological measures will be obtained that include functional brain imaging[unreadable] (fMRI) led by Helen S. Mayberg, M.D., several genetic polymorphisms led by Joseph F. Cubells, M.D., Ph.D.[unreadable] and Elisabeth Binder, M.D., Ph.D., indices of HPA axis activity, markers of immune and inflammatory[unreadable] function, as well as measures of personality, early life trauma, depression and anxiety, and cognitive[unreadable] function. In addition, in a study led by Michael J. Owens, Ph.D. both PET and an ex vivo method will be[unreadable] utilized to determine the relationship of the magnitude of SERT occupancy to clinical response in the[unreadable] escitalopram and duloxetine treatment groups and, moreover, the ex vivo method will be used to assess the[unreadable] importance of NET occupancy in treatment response to duloxetine. A Special Scientific Procedures,[unreadable] Statistical Modeling Core, led by Mary Kelley, Ph.D. will seek to determine which measure and/or[unreadable] combination of measures leads to predictors of response to the three treatments under study. Delineation of[unreadable] patient characteristics that predict treatment response to a specific treatment modality will dramatically[unreadable] improve patient outcomes and reduce the risk of inadequate treatment.[unreadable]