Completed suicides of citalopram users-the role of CYP genotypes and adverse drug interactions

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.

The Role of Pharmacogenetics in Personalizing the Antidepressant and Anxiolytic Therapy

Pharmacotherapy for neuropsychiatric disorders, such as anxiety and depression, has been characterized by significant inter-individual variability in drug response and the development of side effects. Pharmacogenetics, as a key part of personalized medicine, aims to optimize therapy according to a patient's individual genetic signature by targeting genetic variations involved in pharmacokinetic or pharmacodynamic processes. Pharmacokinetic variability refers to variations in a drug's absorption, distribution, metabolism, and elimination, whereas pharmacodynamic variability results from variable interactions of an active drug with its target molecules. Pharmacogenetic research on depression and anxiety has focused on genetic polymorphisms affecting metabolizing cytochrome P450 (CYP) and uridine 5'-diphospho-glucuronosyltransferase (UGT) enzymes, P-glycoprotein ATP-binding cassette (ABC) transporters, and monoamine and γ-aminobutyric acid (GABA) metabolic enzymes, transporters, and receptors. Recent pharmacogenetic studies have revealed that more efficient and safer treatments with antidepressants and anxiolytics could be achieved through genotype-guided decisions. However, because pharmacogenetics cannot explain all observed heritable variations in drug response, an emerging field of pharmacoepigenetics investigates how epigenetic mechanisms, which modify gene expression without altering the genetic code, might influence individual responses to drugs. By understanding the epi(genetic) variability of a patient's response to pharmacotherapy, clinicians could select more effective drugs while minimizing the likelihood of adverse reactions and therefore improve the quality of treatment.

Comparison of targeted vs. expanded pharmacogenomic testing: What are we missing?

BACKGROUND

Pharmacogenomics (PGx) is used as a medication management strategy by a small but growing number of institutions. PGx allows prescribers to individually treat patients concordant with their genes. Recent litigation for preventable PGx-mediated adverse events highlights the need to accelerate PGx implementation for patient safety. Genetic variations cause drug metabolism, transport, and target changes, affecting medication response and tolerability. PGx testing often consists of targeted testing aimed at specific gene-drug pairs or disease states. Conversely, expanded panel testing can evaluate all known actionable gene-drug interactions, enhancing proactive clarity regarding patient response.

OBJECTIVES

Evaluate the divergence of targeted PGx testing with a single gene-drug pair test (cardiac), a two-gene panel, and a focused psychiatric panel compared to expanded PGx testing.

METHODS

An expanded PGx panel (≥25 genes) was compared to a single gene-drug pair test of CYP2C19/clopidogrel, a dual gene test of CYP2C19/CYP2D6, a 7-gene psychiatric list, and a 14-gene psychiatric panel to inform specific depression and pain management drugs. The expanded panel provided a baseline to evaluate total PGx variations compared to those possibly missed by targeted testing.

RESULTS

Targeted testing did not identify up to 95% of total PGx gene-drug interactions discovered. The expanded panel reported all gene-drug interactions for any medication with Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance or U.S. Food and Drug Administration (FDA) labeling for that gene. Single gene CYP2C19/clopidogrel testing missed or did not report on ∼95% of total interactions, CYP2C19/CYP2D6 testing missed or did not report ∼89%, and the 14-gene panel missed or did not report on ∼73%. The 7-gene list missed ∼20% of discovered potential PGx interactions but was not designed to identify gene-drug interactions.

CONCLUSIONS

Targeted PGx testing for limited genes or by specialty may miss or not report significant portions of PGx gene-drug interactions. This can lead to potential patient harm from the missed interactions and subsequent failed therapies and/or adverse reactions.

Validation of Pharmacogenomic Interaction Probability (PIP) Scores in Predicting Drug-Gene, Drug-Drug-Gene, and Drug-Gene-Gene Interaction Risks in a Large Patient Population

Utilizing pharmacogenomic (PGx) testing and integrating evidence-based guidance in drug therapy enables an improved treatment response and decreases the occurrence of adverse drug events. We conducted a retrospective analysis to validate the YouScript^® PGx interaction probability (PIP) algorithm, which predicts patients for whom PGx testing would identify one or more evidence-based, actionable drug-gene, drug-drug-gene, or drug-gene-gene interactions (EADGIs). PIP scores generated for 36,511 patients were assessed according to the results of PGx multigene panel testing. PIP scores versus the proportion of patients in whom at least one EADGI was found were 22.4% vs. 22.4% (p = 1.000), 23.5% vs. 23.4% (p = 0.6895), 30.9% vs. 29.4% (p = 0.0667), and 27.3% vs. 26.4% (p = 0.3583) for patients tested with a minimum of 3-, 5-, 14-, and 25-gene panels, respectively. These data suggest a striking concordance between the PIP scores and the EAGDIs found by gene panel testing. The ability to identify patients most likely to benefit from PGx testing has the potential to reduce health care costs, enable patient access to personalized medicine, and ultimately improve drug efficacy and safety.

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.

Predictors of drug-drug interactions of medications prescribed to patients admitted due to suicidal behavior

INTRODUCTION

Drug-drug interactions among people with suicidal behavior is a challenging topic, considering the harm it poses for patients already vulnerable and the lack of literature on the thematic. This aspect must not be neglected in research and clinical practice, and thus requires thorough investigation.

OBJECTIVE

to investigate predictors of drug-drug interaction of prescribed drugs and the prescription of two or more drugs for people admitted due to suicidal behavior in a psychiatric emergency department (short-stay hospital ward).

METHOD

A cross-sectional study with retrospective approach, carried out in a Brazilian psychiatric emergency unit in 2015. Data about first and last medical prescriptions were collected from 127 patients' files. Descriptive statistics and the Zero Adjusted Logarithmic Distribution (ZALG) model were adopted, with the significance level α = 0.05.

RESULTS

Potential drug-drug interactions were found in most of the first and last prescriptions. The sample majority were female, with previous suicide attempts, being discharged from the hospital with three drugs (or more) prescribed, and without referral to any health service. Age and comorbidities were predictors of more drug prescriptions and the amount of prescribed drugs was the most important predictor of drug-drug interactions (quantity and severity).

CONCLUSIONS

the variables associated with drug-drug interactions and prescription of two or more drugs among people with suicidal behavior needs to be investigated in different contexts and addressed in interventions with the aim to promote patient safety.

CYP2C19 polymorphisms are associated with severity of depression at initial evaluation and after the treatment independently of the prescribed medications: 4 weeks prospective study

BACKGROUND

The cytochrome P-450 2C19 (CYP2C19) enzyme is involved in the metabolism of numerous antidepressants. It also metabolises some endogenous substrates, which could also confer to vulnerability. We aimed to establish whether the severity of depression and treatment response are associated with the genetically predicted CYP2C19 phenotype.

METHODS

We assessed the CYP2C19 genotype-predicted metabolic phenotypes (normal, intermediate or ultrarapid, there were no poor metabolisers) in patients with moderate or severe depression. We used the self-rated Beck Depression Inventory-II (BDI-II) scale and the clinician-rated Montgomery-Åsberg Depression Rating Scale (MADRS) at baseline, after 2 and 4 weeks of an empirical treatment trial. Patients and clinicians were blind to the genetic testing results.

RESULTS

Seventy-six patients participated in the present study. At baseline, impaired CYP2C19 metabolisers, compared to normal metabolisers, had higher BDI-II (P = 0.046; ηp2 = 0.08) but not MADRS score. Intermediate metabolisers more often had a diagnosis of severe depression than normal metabolisers (P = 0.003). After 4 weeks of empirical treatment, intermediate metabolisers had significantly higher MADRS and BDI-II scores than normal metabolisers (P = 0.006; ηp2  = 0.131 and P = 0.030; ηp2 = 0.091). These differences were independent of the use of CYP2C19-metabolised medications in the treatment trial, as well as the treatment discrepancy status.

CONCLUSIONS

Intermediate CYP2C19 polymorphism-predicted activity was associated with more severe depression after an empirical treatment trial. The lack of association between the prescription of CYP2C19-metabolised drugs and treatment response calls for a further look into the role of endogenous substrates of CYP2C19.

Association of CYP2C19 and CYP2D6 Poor and Intermediate Metabolizer Status With Antidepressant and Antipsychotic Exposure: A Systematic Review and Meta-analysis

IMPORTANCE

Precise estimation of the drug metabolism capacity for individual patients is crucial for adequate dose personalization.

OBJECTIVE

To quantify the difference in the antipsychotic and antidepressant exposure among patients with genetically associated CYP2C19 and CYP2D6 poor (PM), intermediate (IM), and normal (NM) metabolizers.

DATA SOURCES

PubMed, Clinicaltrialsregister.eu, ClinicalTrials.gov, International Clinical Trials Registry Platform, and CENTRAL databases were screened for studies from January 1, 1990, to June 30, 2020, with no language restrictions.

STUDY SELECTION

Two independent reviewers performed study screening and assessed the following inclusion criteria: (1) appropriate CYP2C19 or CYP2D6 genotyping was performed, (2) genotype-based classification into CYP2C19 or CYP2D6 NM, IM, and PM categories was possible, and (3) 3 patients per metabolizer category were available.

DATA EXTRACTION AND SYNTHESIS

The Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines were followed for extracting data and quality, validity, and risk of bias assessments. A fixed-effects model was used for pooling the effect sizes of the included studies.

MAIN OUTCOMES AND MEASURES

Drug exposure was measured as (1) dose-normalized area under the plasma level (time) curve, (2) dose-normalized steady-state plasma level, or (3) reciprocal apparent total drug clearance. The ratio of means (RoM) was calculated by dividing the mean drug exposure for PM, IM, or pooled PM plus IM categories by the mean drug exposure for the NM category.

RESULTS

Based on the data derived from 94 unique studies and 8379 unique individuals, the most profound differences were observed in the patients treated with aripiprazole (CYP2D6 PM plus IM vs NM RoM, 1.48; 95% CI, 1.41-1.57; 12 studies; 1038 patients), haloperidol lactate (CYP2D6 PM vs NM RoM, 1.68; 95% CI, 1.40-2.02; 9 studies; 423 patients), risperidone (CYP2D6 PM plus IM vs NM RoM, 1.36; 95% CI, 1.28-1.44; 23 studies; 1492 patients), escitalopram oxalate (CYP2C19 PM vs NM, RoM, 2.63; 95% CI, 2.40-2.89; 4 studies; 1262 patients), and sertraline hydrochloride (CYP2C19 IM vs NM RoM, 1.38; 95% CI, 1.27-1.51; 3 studies; 917 patients). Exposure differences were also observed for clozapine, quetiapine fumarate, amitriptyline hydrochloride, mirtazapine, nortriptyline hydrochloride, fluoxetine hydrochloride, fluvoxamine maleate, paroxetine hydrochloride, and venlafaxine hydrochloride; however, these differences were marginal, ambiguous, or based on less than 3 independent studies.

CONCLUSIONS AND RELEVANCE

In this systematic review and meta-analysis, the association between CYP2C19/CYP2D6 genotype and drug levels of several psychiatric drugs was quantified with sufficient precision as to be useful as a scientific foundation for CYP2D6/CYP2C19 genotype-based dosing recommendations.

How Can Drug Metabolism and Transporter Genetics Inform Psychotropic Prescribing?

Many genetic variants in drug metabolizing enzymes and transporters have been shown to be relevant for treating psychiatric disorders. Associations are strong enough to feature on drug labels and for prescribing guidelines based on such data. A range of commercial tests are available; however, there is variability in included genetic variants, methodology, and interpretation. We herein provide relevant background for understanding clinical associations with specific variants, other factors that are relevant to consider when interpreting such data (such as age, gender, drug-drug interactions), and summarize the data relevant to clinical utility of pharmacogenetic testing in psychiatry and the available prescribing guidelines. We also highlight areas for future research focus in this field.

Buprenorphine: prospective novel therapy for depression and PTSD

BACKGROUND

Depression and post-traumatic stress disorder (PTSD) are leading causes of disability and loss of life by suicide. Currently, there are less than satisfactory medical solutions to treat these mental disorders. Here, we explore recent preclinical and clinical studies demonstrating the potential of using buprenorphine to treat major depressive disorder, treatment-resistant depression, and PTSD.

METHOD

Bibliographic databases were searched to include preclinical and clinical studies demonstrating the therapeutic potential of buprenorphine and the involvement of the kappa opioid receptor (KOR) in mediating these effects.

RESULTS

Original clinical studies examining the effectiveness of buprenorphine to treat depression were mixed. The majority of participants in the PTSD studies were males and suffer from chronic pain and/or substance use disorders. Nonetheless, these recent studies and analyses established proof of concept warranting farther investigations. Additionally, KOR likely mediates the antidepressant and some of the anxiolytic effects of buprenorphine. Still, it appears that the full spectrum of buprenorphine's beneficial effects might be due to activity at other opioid receptors as well.

CONCLUSIONS

Pharmaceuticals' abilities to treat medical conditions directly relates to their ability to act upon the endogenous biological systems related to the conditions. Thus, these recent findings are likely a reflection of the central role that the endogenous opioid system has in these mental illnesses. Further studies are necessary to study the involvement of endogenous opioid systems, and specifically KOR, in mediating buprenorphine's beneficial effects and the ability to treat these medical conditions while minimizing risks for misuse and diversion.

Pharmacogenomics of Antidepressant and Antipsychotic Treatment: How Far Have We Got and Where Are We Going?

In recent decades, very few new psychiatric drugs have entered the market. Thus, improvement in the use of antidepressant and antipsychotic therapy has to focus mainly on enhanced and more personalized treatment with the currently available drugs. One important aspect of such individualization is emphasizing interindividual differences in genes relevant to treatment, an area that can be termed neuropsychopharmacogenomics. Here, we review previous efforts to identify such critical genetic variants and summarize the results obtained to date. We conclude that most clinically relevant genetic variation is connected to phase I drug metabolism, in particular to genetic polymorphism of CYP2C19 and CYP2D6. To further improve individualized pharmacotherapy, there is a need to take both common and rare relevant mutations into consideration; we discuss the present and future possibilities of using whole genome sequencing to identify patient-specific genetic variation relevant to treatment in psychiatry. Translation of pharmacogenomic knowledge into clinical practice can be considered for specific drugs, but this requires education of clinicians, instructive guidelines, as well as full attention to polypharmacy and other clinically relevant factors. Recent large patient studies (n > 1,000) have replicated previous findings and produced robust evidence warranting the clinical utility of relevant genetic biomarkers. To further judge the clinical and financial benefits of preemptive genotyping in psychiatry, large prospective randomized trials are needed to quantify the value of genetic-based patient stratification in neuropsychopharmacotherapy and to demonstrate the cost-effectiveness of such interventions.

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.