Depression 12 Jul, 2022 Read Time: 4 mins

Predicting antidepressant response – problems and promises

Precision medicine is a burgeoning field and has the potential to aid diagnosis and tailor treatment for the individual patient. Professor Charles Nemeroff (Dell Medical School, Texas, USA) discussed current problems with predicting antidepressant response and the future promise of pharmacogenomics and neurophysiology in this APA 2022 session.

Early effective treatment is important

Precision medicine may seem a long way off in the clinic. The Diagnostic and Statistical Manual of Mental Disorders relies on symptom criteria for diagnosis, whilst clinicians are aware of the many different combinations of symptoms and responses to treatment seen in their patients. Psychiatric disorders such as major depressive disorder (MDD) are heterogenous1, especially once genetic risk variants2 and comorbid conditions3 are considered.

Suboptimal treatment response prolongs the period of depression

Early effective treatment is important as a suboptimal response prolongs the period of depression. This is associated with worse outcomes including increased risk of relapse4 and decreased ability to work5. Depression also worsens outcomes of many general medical conditions6 with the associated personal and societal costs.

Can we predict antidepressant response?

Knowledge of the MDD subtype can already help to guide treatment, for example adding an antipsychotic or electroconvulsive therapy for psychotic depression. The holy grail is being able to predict which is the best first line antidepressant for a particular patient and their constellation of symptoms, comorbidities and risk factors rather than resort to trial and error.

Certain clinical and demographic factors have been shown to predict antidepressant response

Data from the STAR*D study (Sequenced Treatment Alternatives to Relieve Depression)7 showed that only 33% of patients achieved remission after first line treatment. The remission rate increased with subsequent steps, but there was diminished effectiveness and increased treatment intolerance with each treatment level.

Certain clinical and demographic factors have been shown to predict antidepressant response including depression severity8, gender7, and childhood trauma9, but there is little known about the mechanisms or modification targets.

Pharmacogenomics in psychiatry

Precision psychiatry would allow the treatment most likely to have the highest drug effect based on genetic profiling and stratification. This is being used in other areas of medicine, including breast cancer treatment based on oestrogen receptor and human epidermal growth factor receptor 2 status10. Pharmacogenetic variability that may determine the dose-effect relationship includes both pharmacokinetic (mutations in genes encoding metabolic enzymes and transporters) and pharmacodynamic (mutations in genes encoding receptors and other molecular drug targets) variability.

Antidepressant response maybe associated with norepinephrine transporter and corticotropin-releasing factor system genetic polymorphisms

Studies have suggested that antidepressant response maybe associated with norepinephrine transporter11 and corticotropin-releasing factor system12 genetic polymorphisms. Commercial tests are available to guide drug response in various psychiatric conditions based on serotonin transporter and calcium channel mutations.

The future

Although specific genetic variants have been associated with conditions such as MDD, Prof Nemeroff suggested there is still insufficient data to support the widespread use of combinatorial pharmacogenetic testing in clinical practice13. The FDA have issued a warning regarding the use of genetic tests to predict patient response to specific medications14, although some specific examples such as use of cytochrome P-450 status to aid psychiatric drug dose adjustment maybe useful15.

Still insufficient data to support the widespread use of combinatorial pharmacogenetic testing

Prof Nemeroff concluded by discussing the use of functional imaging16 and neurophysiology17 in this area, moving from focusing on neurotransmitters to a circuit-based model. Electroencephalography is showing some promise on both the group and single-subject level to predict antidepressant response versus placebo18, allowing the balance between mechanistic information and clinical utility.

References

  1. Ostergaard SD, Jensen SOW, Bech P. The heterogeneity of the depressive syndrome: when numbers get serious. Acta Psychiatr Scand 2011;124(6):495-6.
  2. Howard DM, Adams MJ, Clarke TK, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci 2019;22(3):343-52.
  3. Kessler RC, Berglund P, Demler O, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA 2003;289(23):3095-105.
  4. Paykel ES, Ramana R, Cooper Z, Hayhurst H, Kerr J, Barocka A. Residual symptoms after partial remission: an important outcome in depression. Psychol Med 1995;25(6):1171-80.
  5. Simon GE, Revicki D, Heiligenstein J, et al. Recovery from depression, work productivity, and health care costs among primary care patients. Gen Hosp Psychiatry 2000;22(3):153-62.
  6. Penninx BW, Beekman AT, Honig A, Deeg DJ, Schoevers RA, van Eijk JT, van Tilburg W. Depression and cardiac mortality: results from a community-based longitudinal study. Arch Gen Psychiatry 2001;58(3):221-7.
  7. Trivedi MH, Rush AJ, Wisniewski SR, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry 2006;163(1):28-40.
  8. Rush AJ, Wisniewski SR, Warden D, et al. Selecting among second-step antidepressant medication monotherapies: predictive value of clinical, demographic, or first-step treatment features. Arch Gen Psychiatry 2008;65(8):870-80.
  9. Nanni V, Uher R, Danese A. Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: a meta-analysis. Am J Psychiatry 2012;169(2):141-51.
  10. Cho SH, Jeon J, Kim SI. Personalized medicine in breast cancer: a systematic review. J Breast Cancer 2012;15(3):265-72.
  11. Yoshida K, Takahashi H, Higuchi H, et al. Prediction of antidepressant response to milnacipran by norepinephrine transporter gene polymorphisms. Am J Psychiatry 2004;161(9):1575-80.
  12. Binder EB, Owens MJ, Liu W, et al. Association of polymorphisms in genes regulating the corticotropin-releasing factor system with antidepressant treatment response. Arch Gen Psychiatry 2010;67(4):369-79.
  13. Zeier Z, Carpenter LL, Kalin NH, et al. Clinical Implementation of Pharmacogenetic Decision Support Tools for Antidepressant Drug Prescribing. Am J Psychiatry 2018;175(9):873-86.
  14. https://www.fda.gov/news-events/press-announcements/jeffrey-shuren-md-jd-director-fdas-center-devices-and-radiological-health-and-janet-woodcock-md
  15. https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations
  16. Dunlop BW, Rajendra JK, Craighead WE, et al. Functional Connectivity of the Subcallosal Cingulate Cortex And Differential Outcomes to Treatment With Cognitive-Behavioral Therapy or Antidepressant Medication for Major Depressive Disorder. Am J Psychiatry 2017;174(6):533-45.
  17. Widge AS, Rodriguez CI, Carpenter LL, et al. EEG Biomarkers for Treatment Response Prediction in Major Depressive Illness. Am J Psychiatry 2019;176(1):82.
  18. Rolle CE, Fonzo GA, Wu W, et al. Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial. JAMA Psychiatry 2020;77(4):397-408.

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