Impact of CYP2C19 metaboliser status on SSRI response: a retrospective study of 9500 participants of the Australian Genetics of Depression Study

Metabolic activity of CYP2C19 and CYP2D6 on antidepressant response from 13 clinical studies using genotype imputation: a meta-analysis

Cytochrome P450 enzymes including CYP2C19 and CYP2D6 are important for antidepressant metabolism and polymorphisms of these genes have been determined to predict metabolite levels. Nonetheless, more evidence is needed to understand the impact of genetic variations on antidepressant response. In this study, individual clinical and genetic data from 13 studies of European and East Asian ancestry populations were collected. The antidepressant response was clinically assessed as remission and percentage improvement. Imputed genotype was used to translate genetic polymorphisms to metabolic phenotypes (poor, intermediate, normal, and rapid+ultrarapid) of CYP2C19 and CYP2D6. CYP2D6 structural variants cannot be imputed from genotype data, limiting the determination of metabolic phenotypes, and precluding testing for association with response. The association of CYP2C19 metabolic phenotypes with treatment response was examined using normal metabolizers as the reference. Among 5843 depression patients, a higher remission rate was found in CYP2C19 poor metabolizers compared to normal metabolizers at nominal significance but did not survive after multiple testing correction (OR = 1.46, 95% CI [1.03, 2.06], p = 0.033, heterogeneity I2 = 0%, subgroup difference p = 0.72). No metabolic phenotype was associated with percentage improvement from baseline. After stratifying by antidepressants primarily metabolized by CYP2C19, no association was found between metabolic phenotypes and antidepressant response. Metabolic phenotypes showed differences in frequency, but not effect, between European- and East Asian-ancestry studies. In conclusion, metabolic phenotypes imputed from genetic variants using genotype were not associated with antidepressant response. CYP2C19 poor metabolizers could potentially contribute to antidepressant efficacy with more evidence needed. Sequencing and targeted pharmacogenetic testing, alongside information on side effects, antidepressant dosage, depression measures, and diverse ancestry studies, would more fully capture the influence of metabolic phenotypes.

Evening Chronotypes With Depression Report Poorer Outcomes of Selective Serotonin Reuptake Inhibitors: A Survey-Based Study of Self-Ratings

BACKGROUND

Preliminary evidence suggests that evening chronotype is related to poorer efficacy of selective serotonin reuptake inhibitors. It is unknown whether this is specific to particular medications, self-rated chronotype, or efficacy.

METHODS

In the Australian Genetics of Depression Study (n = 15,108; 75% women; 18-90 years; 68% with ≥1 other lifetime diagnosis), a survey recorded experiences with 10 antidepressants, and the reduced Morningness-Eveningness Questionnaire was used to estimate chronotype. A chronotype polygenic score was calculated. Age- and sex-adjusted regression models (Bonferroni-corrected) estimated associations among antidepressant variables (how well the antidepressant worked [efficacy], duration of symptom improvement, side effects, discontinuation due to side effects) and self-rated and genetic chronotypes.

RESULTS

The chronotype polygenic score explained 4% of the variance in self-rated chronotype (r = 0.21). Higher self-rated eveningness was associated with poorer efficacy of escitalopram (odds ratio [OR] = 1.04; 95% CI, 1.02 to 1.06; p = .000035), citalopram (OR = 1.03; 95% CI, 1.01 to 1.05; p = .004), fluoxetine (OR = 1.03; 95% CI, 1.01 to 1.05; p = .001), sertraline (OR = 1.02; 95% CI, 1.01 to 1.04; p = .0008), and desvenlafaxine (OR = 1.03; 95% CI, 1.01 to 1.05; p = .004), and a profile of increased side effects (80% of those recorded; ORs = 0.93-0.98), with difficulty getting to sleep the most common. Self-rated chronotype was unrelated to duration of improvement or discontinuation. The chronotype polygenic score was only associated with suicidal thoughts and attempted suicide (self-reported). While our measures are imperfect, and not of circadian phase under controlled conditions, the model coefficients suggest that dysregulation of the phenotypic chronotype relative to its genetic proxy drove relationships with antidepressant outcomes.

CONCLUSIONS

The idea that variation in circadian factors influences response to antidepressants was supported and encourages exploration of circadian mechanisms of depressive disorders and antidepressant treatments.

Utilizing Pharmacogenomics Results to Guide Antidepressant Selection: A Case Report

The case discussion demonstrates the benefit of using Pharmacogenomic (PGx) results to aid in the selection of antidepressant therapy and improve response to treatment. Nearly half of patients diagnosed with major depressive disorder fail initial therapy and may require multiple trials of antidepressants. Genetic variation in several metabolic enzymes contribute to the variable response to antidepressant therapy. PGx testing provides an opportunity to inform antidepressant selection and optimize therapeutic outcomes, while minimizing risk of adverse events. A 79-year-old female who had been experiencing a suboptimal response to escitalopram following dose escalation over a period of three years was referred for a PGx consultation. A clinical pharmacist assessed significant drug-gene, drug-drug, and drug-drug-gene interactions, and relevant clinical information to recommend alternative antidepressant therapy, which resulted in mood improvement.

From genes to drugs: CYP2C19 and pharmacogenetics in clinical practice

The CYP2C19 gene is frequently included in different pharmacogenomic panels tested in clinical practice, due to its involvement in the metabolism of a myriad of frequently prescribed medications. Accordingly, CYP2C19 genotyping can promote precise therapeutic decisions and avoid the occurrence of significant drug-drug-gene interactions in the clinical setting. A comprehensive examination of the role of the CYP2C19 gene in real-world medical settings is presented in this review. This review summarizes the most recent information on how genetic variants in CYP2C19 affect drug metabolism and therapeutic outcomes. It goes into the wide range of CYP2C19 phenotypes, with different degrees of metabolizing activity, and their implications for customized medication response through a review of the literature. The review also analyzes the clinical significance of CYP2C19 in several medical specialties, including cardiology, psychiatry, and gastro-enterology clinics, and illuminates how it affects pharmacological efficacy, safety, and adverse effects. Finally, CYP2C19-supported clinical decision-making is outlined, highlighting the possibility of improving therapeutic outcomes and achieving more affordable treatment options, a step towards optimizing healthcare provision through precision medicine.

Meta-analysis of CYP2C19 and CYP2D6 metabolic activity on antidepressant response from 13 clinical studies using genotype imputation

Cytochrome P450 enzymes including CYP2C19 and CYP2D6 are important for antidepressant metabolism and polymorphisms of these genes have been determined to predict metabolite levels. Nonetheless, more evidence is needed to understand the impact of genetic variations on antidepressant response. In this study, individual clinical and genetic data from 13 studies of European and East Asian ancestry populations were collected. The antidepressant response was clinically assessed as remission and percentage improvement. Imputed genotype was used to translate genetic polymorphisms to metabolic phenotypes (poor, intermediate, normal, and rapid+ultrarapid) of CYP2C19 and CYP2D6. The association of CYP2C19 and CYP2D6 metabolic phenotypes with treatment response was examined using normal metabolizers as the reference. Among 5843 depression patients, a higher remission rate was found in CYP2C19 poor metabolizers compared to normal metabolizers at nominal significance but did not survive after multiple testing correction (OR=1.46, 95% CI [1.03, 2.06], p=0.033, heterogeneity I2=0%, subgroup difference p=0.72). No metabolic phenotype was associated with percentage improvement from baseline. After stratifying by antidepressants primarily metabolized by CYP2C19 and CYP2D6, no association was found between metabolic phenotypes and antidepressant response. Metabolic phenotypes showed differences in frequency, but not effect, between European- and East Asian-ancestry studies. In conclusion, metabolic phenotypes imputed from genetic variants using genotype were not associated with antidepressant response. CYP2C19 poor metabolizers could potentially contribute to antidepressant efficacy with more evidence needed. CYP2D6 structural variants cannot be imputed from genotype data, limiting inference of pharmacogenetic effects. Sequencing and targeted pharmacogenetic testing, alongside information on side effects, antidepressant dosage, depression measures, and diverse ancestry studies, would more fully capture the influence of metabolic phenotypes.

Effect of CYP2C19 Pharmacogenetic Testing on Predicting Citalopram and Escitalopram Tolerability and Efficacy: A Retrospective, Longitudinal Cohort Study

Background-Various antidepressant agents are metabolized by the CYP2C19 enzyme, including Citalopram and Escitalopram. Variation in CYP2C19 expression might give rise to different plasma concentrations of the active metabolites, potentially affecting both drugs' efficacy and tolerability. Aim-The aim of this study was to evaluate differences in the Escitalopram and Citalopram efficacy and tolerability between different CYP2C19 genotype-based metabolizing categories in outpatients suffering from major depressive disorder (MDD). Methods-In a retrospective, longitudinal cohort study of electronic medical-record data, 283 patients with MDD who were prescribed Escitalopram or Citalopram with the available CYP2C19-genotyping test were enrolled. The primary efficacy end point was adverse drug reactions recorded in the medical files. A proportional-odds, multilevel-regression model for longitudinal ordinal data was used to estimate the relation between the CYP2C19 genotype and adverse drug reactions, adjusting for potential confounding variables and other explanatory variables. Latent-class analysis (LCA) was utilized to detect the presence of clinically significant subgroups and their relation to an individual's metabolizing status for CYP2D6/CYP2C19. Results-With poor CYP2C19 metabolizers as a reference, for each unit difference in the activity score of the CYP2C19 phenotype, the odds ratio for drug intolerability was lowered by 0.73 (95% credible intervals: 0.56-0.89), adjusting for significant covariates. In addition, applying LCA, we identified two qualitatively different subgroups: the first group (61.85%) exhibited multiple side effects, low compliance, and frequent treatment changes, whereas the second group (38.15%) demonstrated fewer side effects, good adherence, and fewer treatment changes. The CYP2C19 phenotype was substantially associated with the group membership. Conclusions-We found a positive association between the CYP2C19 activity scores, as inferred from the genotype, and both the efficacy of and tolerability to both Es/Citalopram. LCA enabled valuable insights into the underlying structure of the population; the CYP2C19 phenotype has a predictive value that discriminates between low-adherence, low-drug-tolerance, and low-response patients and high-adherence, high-drug-tolerance, and high-response patients. Personalized medicine based on CYP2C19 genotyping could evolve as a promising new avenue towards mitigating Escitalopram and Citalopram therapy and the associated side effects and enhancing treatment success.

Psychiatric mental health nurse practitioner student knowledge and perceptions of pharmacogenetic testing

Psychotropic medications are typically prescribed in a trial-and-error fashion, and some providers are beginning to utilize pharmacogenetic testing (PGx) as a supplemental prescribing tool in treatment decision making. PGx testing shows potential in enhancing provider insights into personalized prescribing for patients by examining genetic information related to drug metabolism. Literature points to providers' lack of knowledge in PGx interpretation as a main barrier, including psychiatric mental health nurse practitioners (PMHNPs). The aim of this study was to measure a difference, if any, in the knowledge and perceptions of PGx after implementation using a pre-post design. This study implemented an educational intervention on graduate nursing students (n = 15). Data were collected by using a pre- and post-interventional questionnaire. Results demonstrated a significant difference in findings related to students' knowledge (p < 0.001), students' skills related to pharmacogenetics, (p < 0.001), as well as students' perceived ability to implement pharmacogenetics into their practice, (p = 0.028). The authors propose that the knowledge gained from the study demonstrates the importance of introducing PGx education into the PMHNP curricula and to prepare future PMHNPs to confidently utilize PGx in their clinical practice.

Pharmacogenetic Testing in Patients with Autism Spectrum Disorder Evaluated in a Precision Medicine Clinic

OBJECTIVE

This study investigated outcomes of pharmacogenetic testing of youth with autism spectrum disorder (ASD) referred to a precision medicine clinic and explored associations between patient characteristics and pharmacogenomic testing results.

METHODS

Records for patients diagnosed with ASD and subsequently referred to a pediatric hospital's precision medicine clinic between July 1, 2010, and June 30, 2020, were reviewed. Pharmacogenetic testing results were abstracted focusing on CYP2D6 and CYP2C19. In addition, we compiled counts of patients' co-occurring diagnoses, histories of adverse drug reactions (ADRs), previously trialed ineffective medications, and previous psychiatric medication changes. Logistic regression models were fit to examine CYP2C19 and CYP2D6 metabolizer status as functions of patient demographics and prereferral medication histories.

RESULTS

Of 202 patients (mean age = 12.18 yrs), 66% were referred to precision medicine because of poor medication response. Among patients with pharmacogenomic testing results for CYP2D6, 9% were classified as poor metabolizers; among patients with results for CYP2C19, 10% were classified as rapid/ultrarapid metabolizers. Patient demographics and medication response history did not predict pharmacogenomic results. However, the number of co-occurring diagnoses positively predicted the number of nonpsychiatric ADRs and a higher probability of CYP2D6 poor metabolizer status; moreover, nonpsychiatric ADRs positively predicted CYP2C19 rapid/ultrarapid metabolizer status.

CONCLUSION

In one of the largest reported samples of youth with ASD clinically referred for pharmacogenetic testing, we observed high variability in medication response and yield for actionable results. Our findings suggest potential clinical utility for pharmacogenetic testing and introduce possible clinical profiles associated with metabolizer status.

The Effects of CYP2C19 Genotype on Proxies of SSRI Antidepressant Response in the UK Biobank

Selective serotonin reuptake inhibitors (SSRIs) are the most commonly used psychopharmaceutical treatment for major depressive disorder (MDD), but individual responses to SSRIs vary greatly. CYP2C19 is a key enzyme involved in the metabolism of several drugs, including SSRIs. Variations in the CYP2C19 gene are associated with differential metabolic activity, and thus differential SSRI exposure; accordingly, the CYP2C19 genotype may affect the therapeutic response and clinical outcomes, though existing evidence of this link is not entirely consistent. Therefore, we analysed data from the UK Biobank, a large, deeply phenotyped prospective study, to investigate the effects of CYP2C19 metaboliser phenotypes on several clinical outcomes derived from primary care records, including multiple measures of antidepressant switching, discontinuation, duration, and side effects. In this dataset, 24,729 individuals were prescribed citalopram, 3012 individuals were prescribed escitalopram, and 12,544 individuals were prescribed sertraline. Consistent with pharmacological expectations, CYP2C19 poor metabolisers on escitalopram were more likely to switch antidepressants, have side effects following first prescription, and be on escitalopram for a shorter duration compared to normal metabolisers. CYP2C19 poor and intermediate metabolisers on citalopram also exhibited increased odds of discontinuation and shorter durations relative to normal metabolisers. Generally, no associations were found between metabolic phenotypes and proxies of response to sertraline. Sensitivity analyses in a depression subgroup and metabolic activity scores corroborated results from the primary analysis. In summary, our findings suggest that CYP2C19 genotypes, and thus metabolic phenotypes, may have utility in determining clinical responses to SSRIs, particularly escitalopram and citalopram, though further investigation of such a relationship is warranted.

Pharmacogenetics and phenoconversion: the influence on side effects experienced by psychiatric patients

Introduction: Preventing side effects is important to ensure optimal psychopharmacotherapy and therapeutic adherence among psychiatric patients. Obtaining the pharmacogenetic profile of CYP2C19 and CYP2D6 can play an important role in this. When the genotype-predicted phenotype shifts because of the use of co-medication, this is called phenoconversion. The aim was to study the influence of the pharmacogenetic (PGx) profile and phenoconversion on side effects experienced by psychiatric patients. Methods: A retrospective cohort study was performed using data from 117 patients from a psychiatric outpatient clinic. Patients were genotyped with a psychiatric PGx panel and side effects were evaluated using the Udvalg for Kliniske Undersølgelser side effects rating scale (UKU). Results: Of all patients, 10.3% and 9.4% underwent phenoconversion (any shift in predicted phenotype) for CYP2C19 and CYP2D6 respectively. No significant associations were found between the phenotype and UKU-score. 75% of the patients with an Intermediate metabolizer (IM) or Poor metabolizer (PM) phenoconverted phenotype of CYP2C19 experienced nausea and vomiting compared to 9.1% of the Normal metabolizer (NM) and Ultrarapid metabolizer (UM) patients (p = 0.033). 64% of the patients with an IM or PM phenoconverted phenotype of CYP2D6 experienced the side effect depression compared to 30.4% NMs and UMs (p = 0.020). CYP2D6 IM and PM patients had a higher concentration-dose ratio than NM patients (p < 0.05). Discussion: This study underlines the importance to consider phenoconversion when looking at a patient's genotype. This is important for a better prediction of the phenotype and preventing possible side effects under a specific psychopharmacotherapy.

Development of a point-of-care genetic test for effective treatment of ischaemic stroke: an early model-based cost-effectiveness analysis

Background: People who have experienced a stroke are at high risk of recurrent strokes. Clopidogrel is prescribed to people who have had a non-cardioembolic stroke. There is evidence that clopidogrel is not effective for patients with CYP2C19 loss-of-function alleles. Pharmacogenetic testing is a potential strategy to identify such patients and guide prescription of appropriate antiplatelet treatment. This study aimed to provide an early estimate of the cost-effectiveness of using a point-of-care pharmacogenetic CYP2C19 test in the UK National Health System. Methods: A decision-analytic model comprising a linked decision tree and Markov model were created in R comparing pharmacogenetic testing with current prescribing practice. In the pharmacogenetic testing arm, patients identified to have one of three loss-of-function alleles were prescribed modified-release dipyridamole and aspirin or aspirin alone. Indicative data were sourced from reviews of the literature supported by expert consultation to select the most appropriate value for the input parameters. The healthcare costs (£;2021) and quality adjusted life years resulting from each strategy were estimated and the incremental cost-effectiveness of testing calculated. Deterministic threshold analysis and probabilistic sensitivity analysis (PSA) was conducted to account for uncertainty in the parameter estimates. Results: The pharmacogenetic testing strategy generated 0.107 additional QALYs per patient tested and saved £512. Pharmacogenetic testing dominated current prescribing practice. The results were robust to extreme changes in key input variables. The PSA suggested that there was a 77% chance that pharmacogenetic testing would be cost-effective with a 62% chance it is cost-saving. Conclusions: A point-of-care pharmacogenetic test to guide prescription of clopidogrel for people who have experienced a stroke has the potential to provide a significant health gain by preventing secondary strokes and may save resources in the health system. This early economic analysis has also informed the direction for future research.

The pharmacogenetics of CYP2D6 and CYP2C19 in a case series of antidepressant responses

Pharmacogenetics has potential for optimizing use of psychotropics. CYP2D6 and CYP2C19 are two clinically relevant pharmacogenes in the prescribing of antidepressants. Using cases recruited from the Understanding Drug Reactions Using Genomic Sequencing (UDRUGS) study, we aimed to evaluate the clinical utility of genotyping CYP2D6 and CYP2C19 in antidepressant response. Genomic and clinical data for patients who were prescribed antidepressants for mental health disorders, and experienced adverse reactions (ADRs) or ineffectiveness, were extracted for analysis. Genotype-inferred phenotyping of CYP2D6 and CYP2C19 was carried out as per Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. A total of 52 patients, predominantly New Zealand Europeans (85%) with a median age (range) of 36 years (15-73), were eligible for analysis. Thirty-one (60%) reported ADRs, 11 (21%) ineffectiveness, and 10 (19%) reported both. There were 19 CYP2C19 NMs, 15 IMs, 16 RMs, one PM and one UM. For CYP2D6, there were 22 NMs, 22 IMs, four PMs, three UMs, and one indeterminate. CPIC assigned a level to each gene-drug pair based on curated genotype-to-phenotype evidence. We analyzed a subgroup of 45 cases, inclusive of response type (ADRs/ineffectiveness). Seventy-nine (N = 37 for CYP2D6, N = 42 for CYP2C19) gene-drug/antidepressant-response pairs with CPIC evidence levels of A, A/B, or B were identified. Pairs were assigned as 'actionable' if the CYP phenotypes potentially contributed to the observed response. We observed actionability in 41% (15/37) of CYP2D6-antidepressant-response pairs and 36% (15/42) of CYP2C19-antidepressant-response pairs. In this cohort, CYP2D6 and CYP2C19 genotypes were actionable for a total of 38% pairs, consisting of 48% in relation to ADRs and 21% in relation to drug ineffectiveness.

Association between CYP metabolizer phenotypes and selective serotonin reuptake inhibitors induced weight gain: a retrospective cohort study

BACKGROUND

Prescription medications such as selective serotonin reuptake inhibitors (SSRIs), commonly used to treat depression, are associated with weight gain. The role of pharmacogenomics in predicting SSRI-induced weight gain is unclear.

METHODS

In this retrospective cohort study from participants in the Mayo Clinic RIGHT study who were prescribed citalopram, paroxetine, sertraline, or fluoxetine, our aim was to evaluate the association of metabolizer phenotype and total body weight after 6 months of SSRIs initiation. We evaluated the metabolizer phenotypes (poor/intermediate, normal, and rapid/ultra-rapid) of the cytochromes P450 enzymes genes: CYP2C9, CYP2C19, and CYP2D6 known to influence the metabolism of SSRI medications: CYP2C19 for citalopram, CYP2D6 for paroxetine, CYP2D6 and CYP2C19 for sertraline, and CYP2D6 and CYP2C9 fluoxetine. In addition, we assessed the association of metabolizer phenotype and total body weight change at six months following SSRI prescription using parametric analysis of covariance adjusted for baseline body weight and multivariate regression models.

RESULTS

CYP2C19 poor/intermediate metabolizers prescribed citalopram gained significantly more weight than normal or rapid/ultra-rapid metabolizers at 6 months (TBWG %: 2.6 [95% CI 1.3-4.1] vs. 0.4 [95% CI -0.5 - 1.3] vs. -0.1 [-95% CI -1.5-1.1]; p = 0.001). No significant differences in weight outcomes at six months of treatment with paroxetine, sertraline, or fluoxetine were observed by metabolizer status.

CONCLUSIONS

Weight gain observed with citalopram may be mediated by CYP2C19 metabolizer status.

Clinical Impact of Functional CYP2C19 and CYP2D6 Gene Variants on Treatment with Antidepressants in Young People with Depression: A Danish Cohort Study

Background: The clinical impact of the functional CYP2C19 and CYP2D6 gene variants on antidepressant treatment in people with depression is not well studied. Here, we evaluate the utility of pharmacogenetic (PGx) testing in psychiatry by investigating the association between the phenotype status of the cytochrome P450 (CYP) 2C19/2D6 enzymes and the one-year risks of clinical outcomes in patients with depression with incident new-use of (es)citalopram, sertraline, or fluoxetine. Methods: This study is a population-based cohort study of 17,297 individuals who were born between 1981 and 2005 with a depression diagnosis between 1996 and 2012. Using array-based single-nucleotide-polymorphism genotype data, the individuals were categorized according to their metabolizing status of CYP2C19/CYP2D6 as normal (NM, reference group), ultra-rapid- (UM), rapid- (RM), intermediate- (IM), or poor-metabolizer (PM). The outcomes were treatment switching or discontinuation, psychiatric emergency department contacts, and suicide attempt/self-harm. By using Poisson regression analyses, we have estimated the incidence rate ratios (IRR) with 95% confidence intervals (95% CI) that were adjusted for covariates and potential confounders, by age groups (<18 (children and adolescents), 19−25 (young adults), and 26+ years (adults)), comparing the outcomes in individuals with NM status (reference) versus the mutant metabolizer status. For statistically significant outcomes, we have calculated the number needed to treat (NNT) and the number needed to genotype (NNG) in order to prevent one outcome. Results: The children and adolescents who were using (es)citalopram with CYP2C19 PM status had increased risks of switching (IRR = 1.64 [95% CI: 1.10−2.43]) and suicide attempt/self-harm (IRR = 2.67 [95% CI; 1.57−4.52]). The young adults with CYP2C19 PM status who were using sertraline had an increased risk of switching (IRR = 2.06 [95% CI; 1.03−4.11]). The young adults with CYP2D6 PM status who were using fluoxetine had an increased risk of emergency department contacts (IRR = 3.28 [95% CI; 1.11−9.63]). No significant associations were detected in the adults. The NNG for preventing one suicide attempt/suicide in the children who were using (es)citalopram was 463, and the NNT was 11. Conclusion: The CYP2C19 and CYP2D6 PM phenotype statuses were associated with outcomes in children, adolescents, and young adults with depression with incident new-use of (es)citalopram, sertraline, or fluoxetine, therefore indicating the utility of PGx testing, particularly in younger people, for PGx-guided antidepressant treatment.

CYP2C19 slow metabolizer phenotype is associated with lower antidepressant efficacy and tolerability

The inter-individual variability in CYP2C19-mediated metabolism may affect the antidepressant treatment. The aim of this study is to evaluate differences in antidepressant efficacy and tolerability between different CYP2C19 metabolizer categories in inpatients suffering from major depressive disorder. The cohort was divided into experimental groups based on CYP2C19 genotype and it contained 24 slow (SMs), 41 normal (NMs), and 37 fast metabolizers (FMs). Efficacy and tolerability were assessed at baseline, and after two and four weeks as a follow-up. The primary efficacy measurement was the change from baseline in Hamilton's Depression Rating Scale (HAMD), while the primary tolerability measurement was the Toronto Side-Effects Scale (TSES) intensity scores at the last visit. The reduction in HAMD score was 36% less pronounced and response rate was exceedingly less prevalent (75% lower) in SMs, compared with NMs. The TSES intensity score was increased in SMs, compared with NMs, by 43% for central nervous system and by 22% for gastrointestinal adverse drug reactions. No significant differences in measured parameters were observed between NMs and FMs. Compared with NM and RM, lower antidepressant efficacy and tolerability was observed in SMs; this association is likely connected with the lower SM capacity to metabolize antidepressant drugs.