The Role of Pharmacogenomics in Precision Mental Healthcare

Josh Hamilton, DNP, APRN-BC, CTMH, CNE, CLNC, FAANP

Anxiety disorders are the world’s most common mental disorders, affecting 301 million people in 2019 (World Health Organization, 2023). Eighty-five million adults lived with elevated depression symptoms between March and April 2021 (Ettman et al., 2022). Additionally, mental illnesses like depression and anxiety predict decreases in role functioning, social relationships, and quality of life (Amos et al, 2018). Enhancing the nurse practitioner’s ability to provide effective, individualized care to persons suffering from low mood and high anxiety is increasingly important.  

Recommended course: Precision Medicine for Depression, Anxiety and ADHD 

What is pharmacogenomics? 

The field of pharmacogenomics uses information about a person’s genetic makeup, or genome, to choose the drugs and drug doses that are likely to be most effective for a that individual (National Genome Research Institute, 2023). As part of an overall strategy to deliver precision mental healthcare, pharmacogenomics is a useful tool that should be integrated into everyday practice. 

When the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial was completed in 2006, one of the most compelling findings was the fact that less than 35% of patients achieve remission with the first antidepressant prescribed (NIMH, 2006). Further, a “law of diminishing returns” was elucidated from current treatment-as-usual, which amounted to little more than a trial-and-error approach that produced increasing rates of medication side-effects, patient non-adherence and unnecessary polypharmacy with each subsequent medication trial (NIMH, 2006).  

The study underscored the need for “high-quality care and attention to the individual needs of patients; providing medication at optimal doses, maintaining awareness of (and offering) treatment choices, and maintaining diligent monitoring of patients, both during treatment and after they become symptom-free so as to avoid relapse” (NIMH, 2006). 

“Rational pharmacotherapy” 

Considering the individual needs of patients drives the well-established practice of “rational pharmacotherapy.” This involves constructing symptoms (to make the diagnosis), de-constructing the symptoms into a specific symptom list, and matching symptoms to brain circuits. The clinician must then consider the known neuropharmacology of those circuits, matching available pharmacotherapies (based upon neuropharmacology), and then fine tune (Maxwell, 2016).  

Personalized medication selection also includes environmental factors, cost/insurance, adverse effects, and patient adherence. However, these are largely subjective.  

Recommended course: Clinical Series Episode: The Gene-Drug Connection: Unlocking the Power of Pharmacogenomics 

Challenges in pharmacogenomics and precision healthcare 

The goal is to get patients to remission, but there are many challenges that keep clinicians from achieving that goal with most patients. Medication adverse-effects and lack of efficacy occur in seemingly random individuals and at seemingly random doses.  

The growing field of precision healthcare has proposed that genetic variability could help to explain some of these differences. Therefore, psychopharmacogenomic testing could inform a more individualized approach. The Food and Drug Administration (FDA) includes pharmacogenomic language in the package inserts of 40 of the 56 psychotropic medications that appear on most commercially available psychopharmacogenomic test reports. This objective information can help to improve patient outcomes. 

Pharmacogenomic testing 

Pharmacogenomic (PGx) testing, using two buccal cheek swabs and traditional methods of genetic amplification and analysis, combines results for both pharmacokinetic and pharmacodynamic genes to inform an overall classification for each relevant medication. A report follows receipt of samples relatively quickly. Most commercial PGx testing laboratories present data that assist the clinician to evaluate each potential psychotropic medication in the context of various gene-drug interactions. 

Literature, studies, and results 

Studies that examine this particular application of precision healthcare are certainly compelling. In vivo results are generally consistent with those reported in the literature, including the Genomics Used to Improve Depression Decisions (GUIDED) study (Greden et al., 2019).  

The first of its kind to examine PGx-guided treatment of major depressive disorder, the GUIDED study demonstrated an 11% overall symptom improvement, 30% relative response improvement and 50% relative remission improvement for depression treatment guided by pharmacogenomics (Greden et al., 2019). Secondary analysis demonstrated an even more robust effect from switching a patient from a genetically incompatible medication to any more compatible option (improvements of 59%, 71% and 153% on the same measures, respectively) (Greden et al., 2019). 

Several specialty laboratories offer pharmacogenomic testing for mental health, and commercial insurance carriers are increasingly approving related claims. In fact, Medicare recipients generally have no out-of-pocket expense for psychopharmacogenomic testing. Labs generally cap the patient’s coinsurance costs, utilizing sliding fee scales, interest-free payment plans and competitive cash pricing for patients who are uninsured. 

While PGx testing serves as a useful objective tool, it does not replace the clinician’s skill and knowledge in selecting the best treatment options using the global formulation of the patient’s presenting symptoms. It can also be an extremely powerful instrument to strengthen the patient-provider relationship and to reduce the stigma of mental illness in the context of integrated healthcare. 

Interested in learning more about this topic? Check out our recent Continuing Education session for NPs, Precision Medicine for Depression, Anxiety and ADHD. 

 

References 

  • Amos, T., Tandon, N., Lefebvre, P., Pilon, D, Kamstra, R., Pivneva, I., & Greenberg, P. (2018). Direct and indirect cost burden and change of employment status in treatment-resistant depression: A matched-cohort study using a U.S. commercial claims database. Journal of Clinical Psychiatry, (79)2. https://www.psychiatrist.com/jcp/economic-burden-and-employment-status-change-in-treatment-resistant-depression/ 
  • Ettman, C., Cohen, G., Abdalla, S., Sampson, L., Trinquart, L., Castrucci, B., Bork, R., Clark, M., Wilson, I., Vivier, P., & Galea, S. (2022). Persistent depressive symptoms during COVID-19: A national, population-representative, longitudinal study of U.S. adults. The Lancet Regional Health, Jan;5:100091. 
  • Greden, J., Parikh, S., Rothschild, A., Thase, M., Dunlop, B., DeBattista, C., Conway, C., Forester, B., Mondimore, F., Shelton, R., Macaluso, M., Li, J., Brown, K., Gilbert, A., Burns, L., Jablonski, M., & Dechiaro, B. (2019). Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: A large, patient- and rater-blinded, randomized, controlled study. Journal of Psychiatric Research, 111, 59-67. https://doi.org/10.1016/j.jpsychires.2019.01.003. 
  • Halaris, A., Sohl, E., & Whitham, E. (2021). Treatment-resistant depression revisited: A glimmer of hope. Journal of personalized medicine, 11(2), 155. https://doi.org/10.3390/jpm11020155 
  • IsHak, W., Mirocha, J., Pi, S., Tobia, G., Becker, B., Peselow, E., & Cohen, R. (2014). Patient-reported outcomes before and after treatment of major depressive disorder. Dialogues in Clinical Neuroscience, 16(2), 171-183. 
  • Maxwell, S. (2016). Rational prescribing: The principles of drug selection. Clinical Medicine, 16 (5), 459-464; DOI: 10.7861/clinmedicine.16-5-459 
  • National Genome Research Institute. (2023). Pharmacogenomics. https://www.genome.gov/genetics-glossary/Pharmacogenomics 
  • National Institute of Mental Health (NIMH). (2006). Questions and answers about the NIMH Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study. https://www.nimh.nih.gov/funding/clinical-research/practical/stard/allmedicationlevels 
  • Whisman, M. (2007). Marital distress and DSM-IV psychiatric disorders in a population-based national survey. Journal of Abnormal Psychology, 116, 638-643. http://dx.doi.org/10.1037/0021-843X.116.3.638 
  • World Health Organization. (2023). Anxiety Disorders. https://www.who.int/news-room/fact-sheets/detail/anxiety-disorders