2026 ECNP Congress

EP02-2037

CYP2D6 metabolizer phenotype is associated with early antidepressant discontinuation in the UK Biobank

Kristian Kleine Schaars — T. Cohen, E. Rebibo Demry, A. Young, M. Juruena, T. Schulze, J. Kaaprio, T. PSY-PGx Consortium, R. Van Westrhenen, N. Shomron · Parnassia Psychiatric institute, Outpatient Clinic Pharmacogenetics-, Amsterdam, Netherlands

2026 ECNP Congress

Background

Major depressive disorder is a leading contributor to global disability with antidepressants as the golden standard for treatment. However, treatment response is variable, with many patients experiencing insufficient efficacy or side effects. Under current prescribing practices, antidepressant selection often follows a trial-and-error approach, which may prolong ineffective treatment and increase patient burden. Genetic variation in drug-metabolising enzymes, particularly cytochrome P450 2D6 (CYP2D6), has been proposed as a contributor to interindividual variability in antidepressant outcomes. CYP2D6 is involved in the metabolism of commonly prescribed antidepressants, and genetic variation results in distinct metaboliser phenotypes that can influence drug exposure. Although pharmacogenetic guidelines provide recommendations based on CYP2D6 status, evidence linking these phenotypes to treatment outcomes remains inconsistent.

Aims

This study aimed to evaluate the association between CYP2D6 metabolizer phenotype and antidepressant treatment outcomes, including discontinuation, switching, and side effects, in a large population-based cohort.

Methods

Data were obtained from the UK Biobank, including genetic information and longitudinal primary care prescription records [1,2,3]. Individuals of European ancestry who were prescribed CYP2D6-metabolised antidepressants, were included (N = 26,957). CYP2D6 metabolizer phenotype was classified into poor, intermediate, normal, and ultrarapid groups according to Dutch Pharmacogenetics Working Group guidelines. Outcomes included early discontinuation, switching within 30–90 days, and recorded side effects. Logistic regression models were used to estimate associations between metabolizer phenotype and binary outcomes, adjusted for age, sex, and Townsend deprivation index. Cox proportional hazards models were applied for time-to-discontinuation analyses. Results are reported as odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CIs).

Results

Reduced CYP2D6 metabolic capacity was associated with increased odds of early discontinuation for several antidepressants. For paroxetine, poor metabolizers had higher odds of discontinuation compared with normal metabolizers (OR 1.57, 95% CI 1.24–1.99, p < 0.001), corresponding to discontinuation rates of 31.0% versus 24.0%. For venlafaxine, intermediate metabolizers showed increased odds (OR 1.26, 95% CI 1.01–1.57, p = 0.043). For mirtazapine, poor metabolizers had higher odds of discontinuation (OR 1.49, 95% CI 1.05–2.12, p = 0.026). No significant associations were observed for fluoxetine. Timing analyses indicated that these effects were concentrated at treatment initiation, with poor metabolizers more likely to discontinue immediately for paroxetine (OR 1.63, 95% CI 1.29–2.05, p < 0.001) and venlafaxine (OR 1.56, 95% CI 1.06–2.31, p = 0.025). No associations were observed with maintenance-phase persistence (all p > 0.05). Associations with switching were limited and did not reach statistical significance. No consistent associations were found between CYP2D6 phenotype and recorded side effects.

Conclusions

Non-normal CYP2D6 metabolizer phenotype is associated with early antidepressant discontinuation for paroxetine, venlafaxine, and mirtazapine in a large real-world cohort, with effects primarily observed at treatment initiation. These findings support a role for CYP2D6-related pharmacokinetic variation in early treatment persistence. However, effect sizes were modest, and no associations were observed for longer-term outcomes or side effects, indicating that CYP2D6 represents only one of multiple factors influencing antidepressant response. Further prospective studies are needed to determine the clinical utility of genotype-guided prescribing [4].

References

  1. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLOS Med. 2015 Mar 31;12(3):e1001779. doi:10.1371/journal.pmed.1001779
  2. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018 Oct;562(7726):203–9. doi:10.1038/s41586-018-0579-z PubMed PMID: 30305743; PubMed Central PMCID: PMC6786975.
  3. Wong WLE, Fabbri C, Laplace B, Li D, van Westrhenen R, Lewis CM, et al. The Effects of CYP2C19 Genotype on Proxies of SSRI Antidepressant Response in the UK Biobank. Pharmaceuticals. 2023 Sep 11;16(9):1277. doi:10.3390/ph16091277 PubMed PMID: 37765085; PubMed Central PMCID: PMC10535191. 4.van Westrhenen R, Young AH, Heilbronner U, Juruena M, Ingelman-Sundberg M, Jukic M, et al. PSY-PGx: a new intervention for the implementation of pharmacogenetics in psychiatry. World Psychiatry 2025 Feb;24(1):141–2. doi:10.1002/wps.21289 PubMed PMID: 39810666; PubMed Central PMCID: PMC11733433.