Antidepressant Not Working? You Could Be a "Nonresponder"
New study unearths why antidepressants are not a one-size-fits-all prescription.
Posted December 29, 2017
Over the years, psychopharmacologists have learned through trial and error that individual patients respond differently to specific antidepressant medications. However, the underlying mechanisms that cause only one out of three patients with depression to benefit from the first type of antidepressant he or she is prescribed have remained enigmatic.
Fortunately, this mystery may have just been solved. A pioneering hybrid study on mice and humans recently pinpointed why a specific antidepressant compound successfully alleviates depression in one person but doesn't work for someone else.
The new paper, “Common Genes Associated with Antidepressant Response in Mouse and Man Identify Key Role of Glucocorticoid Receptor Sensitivity,” was published online, December 28, in the journal PLOS Biology. This study was spearheaded by Marianne Müller and colleagues at the Max Planck Institute of Psychiatry in Germany. Ultimately, the final paper involved an international A-team of collaborators from a variety of institutions including Emory University, Harvard University, and the University of Miami.
What Are Some Different Types of Antidepressants?
Antidepressants were first developed in the 1950s and have been widely prescribed for the treatment of depression since the mid-20th century. Although there are countless types of antidepressants on the market today, the most popular can be categorized under a few different umbrellas and drug classes:
- SSRIs (Selective Serotonin Reuptake Inhibitors) This category includes citalopram (Celexa), escitalopram (Lexapro), fluvoxamine (Luvox), paroxetine (Paxil, Pexeva, Brisdelle), fluoxetine (Prozac, Sarafem), and sertraline (Zoloft).
- SNRIs (Serotonin and Noradrenaline Reuptake Inhibitors) This category includes duloxetine (Cymbalta), venlafaxine (Effexor), desvenlafaxine (Pristiq, Khedezla), and levomilnacipran (Fetzima).
- SARIs (Serotonin Antagonist and Reuptake Inhibitor) This class of antidepressants includes nefazodone (Serzone) and trazodone (Desyrel).
- MAOIs (Monoamine Oxidase Inhibitors) This category includes selegiline (Emsam), isocarboxazid (Marplan), phenelzine (Nardil), and tranylcypromine (Parnate).
- NDRIs (Norepinephrine and Dopamine Reuptake Inhibitors) To the best of my knowledge, this category only includes bupropion (Wellbutrin).
- Tetracyclics This class of antidepressants includes amoxapine (Asendin), maprotiline (Ludiomil), and mirtazapine (Remeron).
The Million-Dollar Question: Are You a Responder or Nonresponder?
For the latest groundbreaking research on the molecular basis of antidepressant effectiveness, Müller and her colleagues developed a novel technique that allowed them to identify biomarkers and molecular signatures associated with responding (or not responding) to a specific antidepressant medication in mice.
This mouse model unearthed the importance of the stress-related glucocorticoid receptor in the pharmaceutical treatment of depression. It also allowed the researchers to pinpoint specific biomarkers or biosignatures that could predict “responders” and “nonresponders” to a particular class of antidepressants in humans.
The laboratory researchers hypothesized that their mouse-model findings could potentially apply to human beings taking antidepressants in real-world clinical situations. So, they enlisted help from collaborators at Emory University working directly with human patients.
In a statement, first author Tania Carrillo-Roa, from the Max Planck Institute of Psychiatry, explained how the international team created a cross-species "Of Mice and Men" hybrid model that resulted in their latest findings:
"We were able to identify a cluster of antidepressant response-associated genes in the mouse model that we then validated in a cohort of depressed patients from our collaborators from Emory University in Atlanta. This suggests that molecular signatures associated with antidepressant response in the mouse could, in fact, predict the outcome of antidepressant treatment in the patient cohort. Additional analyses indicated that the glucocorticoid receptor, which is one of the most important players in fine-tuning the stress hormone system, shapes the response to antidepressant treatment.”
Major depression disorders (MDD) affect an estimated 350 million people around the globe and are the leading cause of disability, according to the World Health Organization.
What makes these new findings by the Müller team a potential game changer is that being able to identify "nonresponders" could eliminate the current hit-or-miss "guessing game" of inadvertently prescribing a useless antidepressant to someone suffering from MDD.
The ability to test specific biomarkers and molecular signatures to pinpoint the best antidepressant treatment would reduce the uncertainty of medication selection and expedite the process of matching a clinically depressed patient with the most efficacious type of medication quickly.
In my opininion, being able to predict a clinically depressed patient's treatment response in advance—based on a test that identifies whether he or she is a “responder” or “nonresponder” to a specific antidepressant medication—could revolutionize psychiatry.
Stay tuned. The authors of this study are optimistic that their discovery on the key role of glucocorticoid receptor sensitivity based on individual biomarkers and biosignatures could lead to better antidepressant prescription methods sometime in the near future.
Tania Carrillo-Roa, Christiana Labermaier, Peter Weber, David P. Herzog, Caleb Lareau, Sara Santarelli, Klaus V. Wagner, Monika Rex-Haffner, Daniela Harbich, Sebastian H. Scharf, Charles B. Nemeroff, Boadie W. Dunlop,
W. Edward Craighead, Helen S. Mayberg, Mathias V. Schmidt, Manfred Uhr, Florian Holsboer, Inge Sillaber,
Elisabeth B. Binder, Marianne B. Müller. "Common Genes Associated with Antidepressant Response in Mouse and Man Identify Key Role of Glucocorticoid Receptor Sensitivity.” PLOS Biology (First published online: December 28, 2017) DOI: 10.1371/journal.pbio.2002690