Depression is a heterogeneous disease with multiple subtypes that vary considerably from one patient to the next. Because of this, only about 30% of patients will reach full remission after one antidepressant treatment trial and as many as 70% of patients will be defined as having treatment-resistant depression (TRD), or failure to respond to two adequate trials of different antidepressant classes.1 This disease variability causes significant deficits in depression treatment, thereby increasing direct and indirect costs.

When it comes to depression, there are numerous efficacious interventions available but tailoring treatment to the individual remains a key challenge in clinical practice. Personalized antidepressants may offer a solution.

Antidepressants: The Trial-and-Error Cycle

Patients prescribed an antidepressant for depression often experience a lengthy trial-and-error process before finding the right medication and dose. A trial of several weeks is usually needed to determine whether an antidepressant will be effective. Because individuals vary widely in their response to specific treatments, many patients wait several weeks for a drug to take effect and then will not have an adequate response, so they are switched to another drug and have to repeat the entire process. Some patients may respond to a second medication while others may never receive the opportunity for second-line treatment.2 As a result, most patients stay on ineffective medications for too long, switch treatments too early, or simply drop out of care. Better means of predicting patient outcomes with antidepressant use can help prescribers personalize treatment recommendations for greater efficacy and safety.

Until recently, personalized medicine for depression focused on the characteristics or symptom patterns of individual patients as well as limited biomarker and genetic information. So, a clinician might decide, for example, that someone showing a pattern of atypical depression (eg, oversleeping, overeating, anergy, rejection sensitivity) might respond better to a selective serotonin reuptake inhibitor (SSRI) than to a tricyclic antidepressant (TCA).

Can Biomarkers Make a Difference in Treating Depression?

However, a 2010 review determined that there were no such clinically useful biologic or genetic predictors.2 More recently, studies have examined the role of inflammation in the pathophysiology of depression, based on findings that patients who receive cytokines or interferon-α for hepatitis C or cancer treatment develop depressive symptoms. Patients with depression also have been shown to have elevated levels of CRP, a nonspecific marker of inflammation, and this biomarker, along with more specific markers such as interleukin 7 (IL-7), can help predict a patient’s response to antidepressant medication.3

Following that line of reasoning, anti-cytokine treatments have emerged as potentially selective agents to target proinflammatory cytokines in patients with markers of systemic inflammation. Other strategies include targeting of the blood-brain barrier to mitigate the CNS effects of peripheral inflammation.3

Additional research has progressed to focus on pharmacogenomic testing to identify genetic variants that could be used to predict patients’ differential responses to antidepressants.

See also, how ethnicity can affect treatment response to antipsychotics in this ethnopsychopharmacology review, and our antipsychotic primer. Plus, more on inflammatory biomarkers and depression treatment.

Pharmacogenomic Testing in Psychiatry

Pharmacogenomic testing (PGx) examines how genes affect a person’s response to drugs, the results of which can be used to facilitate individualized prescribing and thereby reduce undesirable outcomes. Widespread adoption of this strategy is growing across other medical disciplines such as oncology and hematology but adoption has been slow to occur in psychiatry. Clinicians may lack training in genetics and may have mixed opinions on the utility of PGx testing in psychiatric practice.

Recently, the International Society of Psychiatric Genetics (ISPG) assembled a group of experts to provide an overview of PGx mechanisms, summarize the current evidence and treatment recommendations related to PGx in psychiatry, and provide consensus recommendations for the use of PGx testing in clinical practice.4 They concluded that the current published evidence, prescribing guidelines, and product labels of several commonly used antidepressants support the use of PGx testing for two cytochrome P450 genes: CYP2D6 and CYP2C19.5

As genes involved in encoding hepatic metabolizing enzymes, CYP2D6 and CYP2C19 contain variants known to influence enzymatic activity. In addition, genetic variation in drug transporters expressed in the liver, gut, and at the blood-brain barrier may alter the drugs’ distribution and consequently, their pharmacokinetic profile. Genotypes are translated into metabolizer phenotypes, which are classified as:6

  • ultrarapid metabolizers (UMs)
  • rapid metabolizers (RMs)
  • normal metabolizers (NMs)
  • intermediate metabolizers (IMs)
  • poor metabolizers (PMs)

Guidelines on Pharmacogenetic Testing for Antidepressant Use

Although the FDA is still reviewing the evidence and cautions against using PGx testing to guide antidepressant prescribing at this time,7 practice guidelines from the Clinical Pharmacogenetic Implementation Consortium (CPIC) include the following recommendations:8,9

  • For CYP2C19 PMs, reduce the starting dose of citalopram, escitalopram, sertraline, and tertiary amine tricyclic antidepressants such as amitriptyline by 50%.
  • For CYP2C19 RMs and ultrarapid metabolizers, select an alternate antidepressant as circulating antidepressant blood levels may be inadequate.
  • For CYP2D6 PMs, reduce most tricyclic antidepressants, fluvoxamine, and paroxetine doses by 50%.
  • For CYP2D6 UMs, select an alternative antidepressant that is not predominantly metabolized by CYP2D6.

In addition, the Dutch Pharmacogenetics Working Group (DPWG) recommends reducing the dose of venlafaxine by an unspecified amount for CYP2D6 poor metabolizers and increasing the dose by up to 150% for ultrametabolizers.10

In a January 2021 review and consensus on pharmacogenomic testing in psychiatry, led by Chad A. Bousman at the University of Calgary, an international group of just over 30 clinicians discussed the challenges and limitations to PGx testing in psychiatry. For instance, they highlighted the number of genes included on a testing panel, the number of alleles assayed within those genes, variability in reporting among accredited laboratories, and the detection of rare variants that are not part of PGx panels.5 In addition, they pointed out that testing availability, turnaround times, and cost may reduce the acceptance of PGx testing in clinical settings.

Nevertheless, “PGx testing should be viewed as a decision-support tool to assist in the thoughtful implementation of good clinical care, enhancing rather than offering an alternative to standard treatment protocols,” wrote Dr. Bousman et al. “In this context, genetic markers can supplement demographic, clinical, and lifestyle information to help guide treatment decisions.”5

Currently, in the United States, the Precision Medicine in Mental Health Care Study (NCT03170362) is underway to evaluate the utility of PGx testing to personalize the treatment of MDD in military Veterans with combat exposure. The study is examining whether Veterans whose care is guided by the results of PGx testing will have a higher rate of remission of depression than the control group, and whether those Veterans will use fewer medications that have potential gene-drug interactions. Preliminary results are expected in October 2021.

More on using pharmacogenomic testing to optimize opioid drug therapy and to initiate antidepressants and antipsychotics in patients with comorbid pain and mental health disorders on our sister site PPM.

References
Last Updated: Jun 2, 2021