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Open Access Publications from the University of California

Biomarkers to Predict Antidepressant Response

  • Author(s): Leuchter, Andrew F.
  • Cook, Ian A.
  • Hamilton, Steven P.
  • Narr, Katherine L.
  • Toga, Arthur
  • Hunter, Aimee M.
  • Faull, Kym
  • Whitelegge, Julian
  • Andrews, Anne M.
  • Loo, Joseph
  • Way, Baldwin
  • Nelson, Stanley F.
  • Horvath, Steven
  • Lebowitz, Barry D.
  • et al.

During the past several years, we have achieved a deeper understanding of the etiology/pathophysiology of major depressive disorder (MDD). However, this improved understanding has not translated to improved treatment outcome. Treatment often results in symptomatic improvement, but not full recovery. Clinical approaches are largely trial-and-error, and when the first treatment does not result in recovery for the patient, there is little proven scientific basis for choosing the next. One approach to enhancing treatment outcomes in MDD has been the use of standardized sequential treatment algorithms and measurement-based care. Such treatment algorithms stand in contrast to the personalized medicine approach, in which biomarkers would guide decision making. Incorporation of biomarker measurements into treatment algorithms could speed recovery from MDD by shortening or eliminating lengthy and ineffective trials. Recent research results suggest several classes of physiologic biomarkers may be useful for predicting response. These include brain structural or functional findings, as well as genomic, proteomic, and metabolomic measures. Recent data indicate that such measures, at baseline or early in the course of treatment, may constitute useful predictors of treatment outcome. Once such biomarkers are validated, they could form the basis of new paradigms for antidepressant treatment selection.

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