Metabolizing status of CYP2C19 in response and side effects to medications for depression: Results from a naturalistic study

Identifying genetic variants associated with side effects of antidepressant treatment: A systematic review

Major Depressive Disorder (MDD) is one of the most prevalent neurobiological disorders globally. Antidepressant medications are the first-line treatment for managing symptoms. However, over time, pharmacotherapy has been linked to several challenges, primarily due to the wide array of side effects that often reduce patient adherence to treatment. The literature suggests that these side effects may be influenced by polymorphisms in genes related to the pharmacokinetics and pharmacodynamics of antidepressants. Thus, this systematic review aimed to identify studies that investigated the association between genetic variants and side effects resulting from antidepressant treatment in individuals with MDD. Original articles indexed in the electronic databases Cochrane Library, EMBASE, MEDLINE via PubMed, and Scopus were identified. A total of 55 studies were included in the review, and data regarding the outcomes of interest were extracted. Due to the exploratory nature of the review, a narrative/descriptive synthesis of the results was performed. The risk of bias was evaluated using the Joanna Briggs Institute's tools, tailored to the design of each study. Polymorphisms in 35 genes were statistically associated with the development of side effects. A subsequent Protein-Protein Interaction Network analysis helped elucidate the key biological pathways involved in antidepressant side effects, with a view toward exploring the potential application of pharmacogenetic markers in clinical practice.

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.

[Pharmacogenomic and pharmacometabolomic biomarkers of the efficacy and safety of antidepressants: focus on selective serotonin reuptake inhibitors]

The efficacy and safety of psychopharmacotherapy with antidepressants is of great medical importance. The search for clinical and biological predictors for choosing the optimal psychopharmacotherapy with antidepressants is actively underway all over the world. Research is mainly devoted to searching for associations of polymorphic gene variants with the efficacy and safety of therapy. However, information about a patient's genetic polymorphism is often insufficient to predict the efficacy and safety of a drug. Modern research on the personalization of pharmacotherapy should include, in addition to genetic, phenotypic biomarkers. This is important because genotyping, for example, cannot accurately predict the actual metabolic activity of an isoenzyme. To personalize therapy, a combination of methods is required to obtain the most complete profile of the efficacy and safety of the drug. Successful treatment of depression remains a challenge, and inter-individual differences in response to antidepressants are common. About half of patients with depressive disorders do not respond to the first attempt at antidepressant therapy. Serious side-effects of antidepressant pharmacotherapy and discontinuation of treatment due to their intolerance are associated with ineffective therapy. This review presents the results of the latest studies of «omics» biomarkers of the efficacy and safety of antidepressants.

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.

Pharmacokinetic Markers of Clinical Outcomes in Severe Mental Illness: A Systematic Review

The term severe mental illness (SMI) encompasses those psychiatric disorders exerting the highest clinical burden and socio-economic impact on the affected individuals and their communities. Pharmacogenomic (PGx) approaches hold great promise in personalizing treatment selection and clinical outcomes, possibly reducing the burden of SMI. Here, we sought to review the literature in the field, focusing on PGx testing and particularly on pharmacokinetic markers. We performed a systematic review on PUBMED/Medline, Web of Science, and Scopus. The last search was performed on the 17 September 2022, and further augmented with a comprehensive pearl-growing strategy. In total, 1979 records were screened, and after duplicate removal, 587 unique records were screened by at least 2 independent reviewers. Ultimately, forty-two articles were included in the qualitative analysis, eleven randomized controlled trials and thirty-one nonrandomized studies. The observed lack of standardization in PGx tests, population selection, and tested outcomes limit the overall interpretation of the available evidence. A growing body of evidence suggests that PGx testing might be cost-effective in specific settings and may modestly improve clinical outcomes. More efforts need to be directed toward improving PGx standardization, knowledge for all stakeholders, and clinical practice guidelines for screening recommendations.

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.