Association of graded allele-specific changes in CYP2D6 function with imipramine dose requirement in a large group of depressed patients

Personalized Medicine in the Treatment of a Patient With Obsessive-Compulsive Disorder With Clomipramine

Clomipramine (CIMI) is an effective treatment for obsessive-compulsive disorder in patients who have failed to respond to trials of selective serotonin transport inhibitors (eg, sertraline). The case presented here illustrates how knowledge of the pharmacodynamics and pharmacokinetics of CIMI in a specific patient can be used to personalize treatment to optimize the likelihood of efficacy (ie, maximum benefit to risk ratio). The approach described in this column considered: (1) the patient's diminished ability to clear CIMI and its major metabolite, desmethlyclomipramine due to a genetic deficiency in cytochrome P450 2D6 enzyme activity, and (2) the patient's ability to extensively convert CIMI to desmethlyclomipramine. That conversion impairs the ability to inhibit the serotonin transporter, the mechanism that is most likely responsible for the efficacy of CIMI in obsessive-compulsive disorder.

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

BACKGROUND

Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.

METHODS

We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.

RESULTS

We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.

CONCLUSIONS

Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Pharmacogenetic/Pharmacogenomic Tests for Treatment Prediction in Depression

Genetic factors play a significant but complex role in antidepressant (AD) response and tolerability. During recent years, there is growing enthusiasm in the promise of pharmacogenetic/pharmacogenomic (PGx) tools for optimizing and personalizing treatment outcomes for patients with major depressive disorder (MDD). The influence of pharmacokinetic and pharmacodynamic genes on response and tolerability has been investigated, including those encoding the cytochrome P450 superfamily, P-glycoprotein, monoaminergic transporters and receptors, intracellular signal transduction pathways, and the stress hormone system. Genome-wide association studies are also identifying new genetic variants associated with AD response phenotypes, which, combined with methods such as polygenic risk scores (PRS), is opening up new avenues for novel personalized treatment approaches for MDD. This chapter describes the basic concepts in PGx of AD response, reviews the major pharmacokinetic and pharmacodynamic genes involved in AD outcome, discusses PRS as a promising approach for predicting AD efficacy and tolerability, and addresses key challenges to the development and application of PGx tests.

An economic model of the cost-utility of pre-emptive genetic testing to support pharmacotherapy in patients with major depression in primary care

The pharmacokinetics of many antidepressants (tricyclic antidepressants (TCA) or selective serotonin re-uptake inhibitors (SSRI)) are influenced by the highly polymorphic CYP2D6 enzyme. Therefore, pharmacogenetics could play an important role in the treatment of depressive patients. The potential cost-utility of screening patients is however still unknown. Therefore, a Markov model was developed to compare the strategy of screening for CYP2D6 and subsequently adjust antidepressant treatment according to a patient's metabolizer profile of poor, extensive, or ultra metabolizer, with the strategy of no screening ('one size fits all' principle). Each week a patient had a probability of side effects, which was followed by dosage titration or treatment switching. After 6 weeks treatment effect was evaluated followed by treatment adjustments if necessary, with a total time horizon of the model of 12 weeks. The analysis was performed from a societal perspective. The strategy of screening compared with no screening resulted in incremental costs of €91 (95 percentiles: €39; €152) more expensive but also more effect with 0.001 quality adjusted life years (QALYs) (95 percentiles: 0.001; 0.002) gain. The incremental cost-effectiveness ratio (ICER) was therefore €77,406 per QALY gained, but varied between €22,500 and €377,500 depending on the price of screening and productivity losses. According to our model, we cannot unequivocally conclude that screening for CYP2D6 in primary care patients using antidepressants is be cost-effective, as the results are surrounded by large uncertainty. Therefore, information from ongoing studies should be used to reduce these uncertainties.

Consequences of CYP2D6 Copy-Number Variation for Pharmacogenomics in Psychiatry

Pharmacogenomics represents a potentially powerful enhancement to the current standard of care for psychiatric patients. However, a variety of biological and technical challenges must be addressed in order to provide adequate clinical decision support for personalized prescribing and dosing based on genomic data. This is particularly true in the case of CYP2D6, a key drug-metabolizing gene, which not only harbors multiple genetic variants known to affect enzyme function but also shows a broad range of copy-number and hybrid alleles in various patient populations. Here, we describe several challenges in the accurate measurement and interpretation of data from the CYP2D6 locus including the clinical consequences of increased copy number. We discuss best practices for overcoming these challenges and then explore various current and future applications of pharmacogenomic analysis of CYP2D6 in psychiatry.

Effect of single-dose imipramine on chronic low-back and experimental pain. A randomized controlled trial

Antidepressants are frequently prescribed as co-analgesics in chronic pain. While their efficacy is well documented for neuropathic pain, the evidence is less clear in musculoskeletal pain conditions. The present study therefore evaluated the effect of the tricyclic antidepressant imipramine on chronic low-back pain in a randomized, double-blinded placebo-controlled design. To explore the mechanisms of action and the influence of drug metabolism, multimodal quantitative sensory tests (QST) and genotyping for cytochrome P450 2D6 (CYP2D6) were additionally performed. A single oral dose of imipramine 75 mg was compared to active placebo (tolterodine 1 mg) in 50 patients (32 females) with chronic non-specific low-back pain. Intensity of low-back pain was assessed on a 0–10 numeric rating scale at baseline and every 30 minutes after drug intake. Multimodal QST were performed at baseline and in hourly intervals for 2 hours. Pharmacogenetic influences of cytochrome P450 were addressed by CYP2D6 genotyping. No significant analgesic effect was detected neither on low-back pain nor on any of the sensory tests in the overall analyses. However, evidence for an interaction of the imipramine effect and CYP2D6 genotype was found for electrical and for pressure pain detection thresholds. Intermediate but not extensive metabolizers had a 1.20 times greater electrical pain threshold (95%-CI 1.10 to 1.31) and a 1.10 times greater pressure pain threshold (95%-CI 1.01 to 1.21) 60 minutes after imipramine than after placebo (p<0.001 and p = 0.034, respectively). The present study failed to demonstrate an immediate analgesic effect of imipramine on low-back pain. Anti-nociceptive effects as assessed by quantitative sensory tests may depend on CYP2D6 genotype, indicating that metabolizer status should be accounted for when future studies with tricyclic antidepressants are undertaken.

Flexible and Scalable Full-Length CYP2D6 Long Amplicon PacBio Sequencing

Cytochrome P450 2D6 (CYP2D6) is among the most important genes involved in drug metabolism. Specific variants are associated with changes in the enzyme's amount and activity. Multiple technologies exist to determine these variants, like the AmpliChip CYP450 test, Taqman qPCR, or Second‐Generation Sequencing, however, sequence homology between cytochrome P450 genes and pseudogene CYP2D7 impairs reliable CYP2D6 genotyping, and variant phasing cannot accurately be determined using these assays. To circumvent this, we sequenced CYP2D6 using the Pacific Biosciences RSII and obtained high‐quality, full‐length, phased CYP2D6 sequences, enabling accurate variant calling and haplotyping of the entire gene‐locus including exonic, intronic, and upstream and downstream regions. Unphased diplotypes (Roche AmpliChip CYP450 test) were confirmed for 24 of the 25 samples, including gene duplications. Cases with gene deletions required additional specific assays to resolve. In total, 61 unique variants were detected, including variants that had not previously been associated with specific haplotypes. To further aid genomic analysis using standard reference sequences, we have established an LOVD‐powered CYP2D6 gene‐variant database, and added all reference haplotypes and data reported here. We conclude that our CYP2D6 genotyping approach produces reliable CYP2D6 diplotypes and reveals information about additional variants, including phasing and copy‐number variation.

The Use of In Vitro Data and Physiologically-Based Pharmacokinetic Modeling to Predict Drug Metabolite Exposure: Desipramine Exposure in Cytochrome P4502D6 Extensive and Poor Metabolizers Following Administration of Imipramine

Major circulating drug metabolites can be as important as the drugs themselves in efficacy and safety, so establishing methods whereby exposure to major metabolites following administration of parent drug can be predicted is important. In this study, imipramine, a tricyclic antidepressant, and its major metabolite desipramine were selected as a model system to develop metabolite prediction methods. Imipramine undergoes N-demethylation to form the active metabolite desipramine, and both imipramine and desipramine are converted to hydroxylated metabolites by the polymorphic enzyme CYP2D6. The objective of the present study is to determine whether the human pharmacokinetics of desipramine following dosing of imipramine can be predicted using static and dynamic physiologically-based pharmacokinetic (PBPK) models from in vitro input data for CYP2D6 extensive metabolizer (EM) and poor metabolizer (PM) populations. The intrinsic metabolic clearances of parent drug and metabolite were estimated using human liver microsomes (CYP2D6 PM and EM) and hepatocytes. Passive diffusion clearance of desipramine, used in the estimation of availability of the metabolite, was predicted from passive permeability and hepatocyte surface area. The predicted area under the curve (AUCm/AUCp) of desipramine/imipramine was 12- to 20-fold higher in PM compared with EM subjects following i.v. or oral doses of imipramine using the static model. Moreover, the PBPK model was able to recover simultaneously plasma profiles of imipramine and desipramine in populations with different phenotypes of CYP2D6. This example suggested that mechanistic PBPK modeling combined with information obtained from in vitro studies can provide quantitative solutions to predict in vivo pharmacokinetics of drugs and major metabolites in a target human population.

Practical interpretation of CYP2D6 haplotypes: Comparison and integration of automated and expert calling

We describe a population genetic approach to compare samples interpreted with expert calling (EC) versus automated calling (AC) for CYP2D6 haplotyping. The analysis represents 4812 haplotype calls based on signal data generated by the Luminex xMap analyzers from 2406 patients referred to a high-complexity molecular diagnostics laboratory for CYP450 testing. DNA was extracted from buccal swabs. We compared the results of expert calls (EC) and automated calls (AC) with regard to haplotype number and frequency. The ratio of EC to AC was 1:3. Haplotype frequencies from EC and AC samples were convergent across haplotypes, and their distribution was not statistically different between the groups. Most duplications required EC, as only expansions with homozygous or hemizygous haplotypes could be automatedly called. High-complexity laboratories can offer equivalent interpretation to automated calling for non-expanded CYP2D6 loci, and superior interpretation for duplications. We have validated scientific expert calling specified by scoring rules as standard operating procedure integrated with an automated calling algorithm. The integration of EC with AC is a practical strategy for CYP2D6 clinical haplotyping.

A combined high CYP2D6-CYP2C19 metabolic capacity is associated with the severity of suicide attempt as measured by objective circumstances

This study examined, for the first time, whether a high CYP2D6-CYP2C19 metabolic capacity combination increases the likelihood of suicidal intent severity in a large study cohort. Survivors of a suicide attempt (n=587; 86.8% women) were genotyped for CYP2C19 (*2, *17) and CYP2D6 (*3, *4, *4xN, *5, *6, *10, wtxN) genetic variation and evaluated with the Beck Suicide Intent Scale (SIS). Patients with a high CYP2D6-CYP2C19 metabolic capacity showed an increased risk for a severe suicide attempt (P<0.01) as measured by the SIS-objective circumstance subscale (odds ratio (OR)=1.37; 95% confidence interval (CI)=1.05-1.78; P=0.02) after adjusting for confounders (gender, age, level of studies, marital status, mental disorders, tobacco use, family history of suicide, personal history of attempts and violence of the attempt). Importantly, the risk was greater in those without a family history of suicide (OR=1.82; CI=1.19-2.77; P=0.002). Further research is warranted to evaluate whether the observed relationship is mediated by the role of CYP2D6 and CYP2C19 involvement in the endogenous physiology or drug metabolism or both.

The Coriell personalized medicine collaborative pharmacogenomics appraisal, evidence scoring and interpretation system

Implementation of pharmacogenomics (PGx) in clinical care can lead to improved drug efficacy and reduced adverse drug reactions. However, there has been a lag in adoption of PGx tests in clinical practice. This is due in part to a paucity of rigorous systems for translating published clinical and scientific data into standardized diagnostic tests with clear therapeutic recommendations. Here we describe the Pharmacogenomics Appraisal, Evidence Scoring and Interpretation System (PhAESIS), developed as part of the Coriell Personalized Medicine Collaborative research study, and its application to seven commonly prescribed drugs.

Clinical validity of cytochrome P450 metabolism and serotonin gene variants in psychiatric pharmacotherapy

Adverse events, response failures and medication non-compliance are common in patients receiving medications for the treatment of mental illnesses. A systematic literature review assessed whether pharmacokinetic (PK) or pharmacodynamic (PD) responses to 26 commonly prescribed antipsychotic and antidepressant medications, including efficacy or side effects, are associated with nucleotide polymorphisms in eight commonly studied genes in psychiatric pharmacotherapy: CYP2D6, CYP2C19, CYP2C9, CYP1A2, CYP3A4, HTR2C, HTR2A, and SLC6A4. Of the 294 publications included in this review, 168 (57%) showed significant associations between gene variants and PK or PD outcomes. Other studies that showed no association often had insufficient control for confounding variables, such as co-medication use, or analysis of medications not substrates of the target gene. The strongest gene-outcome associations were for the PK profiles of CYP2C19 and CYP2D6 (93% and 90%, respectively), for the PD associations between HTR2C and weight gain (57%), and for SLC6A4 and clinical response (54%), with stronger SLC6A4 response associations for specific drug classes (60-83%). The preponderance of evidence supports the validity of analyzing nucleotide polymorphisms in CYP and pharmacodynamic genes to predict the metabolism, safety, or therapeutic efficacy of psychotropic medications commonly used for the treatment of depression, schizophrenia, and bipolar illness.

Towards the implementation of CYP2D6 and CYP2C19 genotypes in clinical practice: update and report from a pharmacogenetic service clinic

Genetic testing may help to improve treatment outcomes in order to avoid non-response or severe side effects to psychotropic medication. Most robust data have been obtained for gene variants in CYP2D6 and CYP2C19 enzymes for antipsychotics and antidepressant treatment. We reviewed original articles indexed in PubMed from 2008-2013 on CYP2D6 and CYP2C19 gene variants and treatment outcome to antidepressant or antipsychotic medication. We have started providing CYP2D6 and CYP2C19 genotype information to physicians and conducted a survey where preliminary results are reported. Studies provided mixed results regarding the impact of CYP2D6 and CYP2C19 gene variation on treatment response. Plasma levels were mostly found associated with CYP metabolizer status. Higher occurrence/severity of side effects were reported in non-extensive CYP2D6 or CYP2C19 metabolizers. Results showed that providing genotypic information is feasible and generally well accepted by both patients and physicians. Although currently available studies are limited by small sample sizes and infrequent plasma drug level assessment, research to date indicates that CYP2D6 and CYP2C19 testing may be beneficial particularly for non-extensive metabolizing patients. In summary, clinical assessment of CYP2D6 and CYP2C19 metabolizer status is feasible, well accepted and optimizes drug treatment in psychiatry.

Influence of CYP2D6 and CYP2C19 gene variants on antidepressant response in obsessive-compulsive disorder

Numerous studies have reported on pharmacogenetics of antidepressant response in depression. In contrast, little is known of response predictors in obsessive-compulsive disorder (OCD), a disorder with among the lowest proportion of responders to medication (40-60%). Our study is the largest investigation to date (N=184) of treatment response and side effects to antidepressants in OCD based on metabolizer status for CYP2D6 and CYP2C19. We observed significantly more failed medication trials in CYP2D6 non-extensive compared with extensive metabolizers (P=0.007). CYP2D6 metabolizer status was associated with side effects to venlafaxine (P=0.022). There were nonsignificant trends for association of CYP2D6 metabolizer status with response to fluoxetine (P=0.056) and of CYP2C19 metabolizer status with response to sertraline (P=0.064). Our study is the first to indicate that CYP genes may have a role in antidepressant response in OCD. More research is required for a future clinical application of genetic testing, which could lead to improved treatment outcomes.

Pain, analgesia and genetics

OBJECTIVES

In the clinical setting, there is marked intersubject variability in the intensity of pain reported by patients with apparently similar pain states, as well as widely differing analgesic dosing requirements between individuals to produce satisfactory pain relief with tolerable side-effects. Genetic and environmental factors as well as their interaction are implicated, and these are discussed in this review.

KEY FINDINGS

Pioneering work undertaken in mice more than a decade ago, showed a strong genetic contribution to levels of nociception/hypersensitivity as well as levels of antinociception produced by commonly available analgesic agents. To date more than 300 candidate 'pain' genes have been identified as potentially contributing to heritable differences in pain sensitivity and analgesic responsiveness in animals and humans, with this information available in a publicly accessible database http://www.jbldesign.com/jmogil/enter.html. Since then, many genetic association studies have been conducted in humans to investigate the possibility that single nucleotide polymorphisms (SNPs) in an individual gene may explain drug inefficacy or excessive toxicity experienced by a small subset of the whole population who have the rare allele for a particular SNP.

SUMMARY

Despite the fact that SNPs in more than 20 genes that affect pain sensitivity or contribute to interindividual variability in responses to analgesic medications have been identified in the human genome, much of the data is conflicting. Apart from deficiencies in the design and conduct of human genetic association studies, recent research from other fields has implicated epigenetic mechanisms that facilitate dynamic gene-environment communication, as a possible explanation.

Genetic polymorphisms of cytochrome P450 enzymes and antidepressant metabolism

INTRODUCTION

The cytochrome P450 (CYP) enzymes are the major enzymes responsible for Phase I reactions in the metabolism of several substances, including antidepressant medications. Thus, it has been hypothesized that variants in the CYP network may influence antidepressant efficacy and safety. Nonetheless, data on this field are still contradictory. The authors aim to give an overview of the published studies analyzing the influence of CYP highly polymorphic loci on antidepressant treatment in order to translate the acquired knowledge to a clinical level.

AREAS COVERED

The authors collected and compared experimental works and reviews published from the 1980s to the present and included in the Medline database. The included studies pertain to the effects of CYP gene polymorphisms on antidepressant pharmacokinetic parameters and clinical outcomes (response and drug-related adverse effects), with a focus on applications in clinical practice. The authors focused mainly on in vivo studies in humans (patients or healthy volunteers).

EXPERT OPINION

Great variability in antidepressant metabolism among individuals has been demonstrated. Thus, with the current interest in individualized medicine, several genetic tests to detect CYP variants have been produced. They provide a potentially useful way to anticipate some clinical outcomes of antidepressant treatment, although they will only be extensively used in clinical practice if precise and specific treatment options and guidelines based on genetic tests can be provided.

Genetic polymorphisms of cytochrome P450 enzymes influence metabolism of the antidepressant escitalopram and treatment response

AIMS

The antidepressant escitalopram (S-CIT) is metabolized by the cytochrome-P450 (CYP) enzymes CYP 2D6, 2C19 and 3A4. This study evaluated the impact of CYP2D6, 2C19 and 3A4 genetic polymorphisms on plasma concentrations of S-CIT and patient treatment response.

MATERIALS & METHODS

A total of 100 patients diagnosed with major depressive disorder were recruited to the study and their depression symptoms were assessed using the Hamilton Depression Rating Scale. The genetic polymorphisms *4, *5 and *10 on CYP2D6, *2, *3 and *17 on CYP2C19, and *18 on CYP3A4 were selected based on their function and respective allele frequencies in Asian populations. Polymorphisms were analyzed using the SNPstream genotyping system, PCR and direct sequencing methods. The steady-state serum concentrations of S-CIT and its metabolites S-desmethylcitalopram and S-didesmethylcitalopram were analyzed by HPLC. According to semiquantitative gene dose (SGD) and gene dose (GD) models for allele combinations of these polymorphisms, CYP2D6 was clustered into intermediate (0.5, 1 and 1.5 SGD) and extensive (2 SGD) metabolizers, while CYP2C19 was clustered into poor (0 GD) and extensive (1 and 2 GDs) metabolizers.

RESULTS

The group of patients with intermediate CYP2D6 metabolism (0.5 SGD) had a significantly higher frequency of remitters from major depressive disorder during the 8-week treatment (p = 0.0001). Furthermore, CYP2C19 poor metabolizers had significantly higher S-CIT serum levels than did extensive metabolizers at weeks 2, 4 and 8 (p < 0.05). The allele frequencies in CYP3A4*18 and CYP2C19*17 were too low to permit further subgroup analyses.

CONCLUSION

Our results suggest that the genetic polymorphisms in CYP2C19 may be influencing S-CIT serum concentrations, and that specific CYP2D6 polymorphisms may be predicting patient treatment outcomes based on gene dosage analyses.

Pharmacogenetics and psychoactive drug therapy: ready for the patient?

Psychiatry is one of the most promising areas for bringing pharmacogenomics to the patient. Psychiatric disorders such as depression and schizophrenia contribute significantly to worldwide morbidity and mortality. Forecasts rank depression second only to ischemic heart disease by 2020. In depression and schizophrenia, 30% to 50% of all patients do not respond sufficiently to the initial treatment regime. Genetic variability has been demonstrated to play an important role in the response to pharmacotherapy. Most data are available with regard to polymorphisms in the genes coding for drug-metabolizing enzymes and recommendations for the choice of personalized dosages based on genotyping results are available. Clinical outcome, in particular adverse effects, has been shown to correlate with the results from genotyping. Incorporating pharmacogenomics into clinical practice has, however, been slow and it is still not clear in which clinical situations genotyping should be performed and what the benefit of such procedures could be beyond therapeutic drug monitoring. Additionally, many studies in psychiatry focus on genetic variation in candidate genes of drug targets. However, despite promising reports, no clear recommendation can be given at present to perform such testing in clinical use.

The intersection of pharmacology, imaging, and genetics in the development of personalized medicine

We currently rely on large randomized trials and meta-analyses to make clinical decisions; this places us at a risk of discarding subgroup or individually specific treatment options owing to their failure to prove efficacious across entire populations. There is a new era emerging in personalized medicine that will focus on individual differences that are not evident phenomenologically. Much research is directed towards identifying genes, endophenotypes, and biomarkers of disease that will facilitate diagnosis and predict treatment outcome. We are at the threshold of being able to predict treatment response, primarily through genetics and neuroimaging. In this review we discuss the most promising markers of treatment response and adverse effects emerging from the areas of pharmacogenetics and neuroimaging in depression and schizophrenia.

Cytochrome P450 CYP1A2, CYP2C9, CYP2C19 and CYP2D6 genes are not associated with response and remission in a sample of depressive patients

Cytochrome P450 genes are involved in the metabolism of antidepressants and could influence treatment response. The aim of this study was to investigate the role of allelic variations of the cytochrome P450 CYP1A2, CYP2C9, CYP2C19 and CYP2D6 genes in antidepressant treatment response and remission rates. Two hundred and seventy-eight patients affected by major depression, responders (N = 81) and nonresponders (N=197) to at least one adequate antidepressant treatment, were recruited with a multicentre design for resistant depression and genotyped for all relevant variations. None of the considered metabolic profiles (e.g. poor, intermediate, extensive and ultrarapid metabolizers) was found to be associated with either response or remission rates. In conclusion, the investigated cytochrome genes do not seem to play a major role in antidepressant response in the present sample of depressive patients. Nevertheless, methodological and sample size limitations of this study do not allow definitive conclusions.

Genetic determinants of variable metabolism have little impact on the clinical use of leading antipsychotics in the CATIE study

PURPOSE

To evaluate systematically in real clinical settings whether functional genetic variations in drug metabolizing enzymes influence optimized doses, efficacy, and safety of antipsychotic medications.

METHODS

DNA was collected from 750 patients with chronic schizophrenia treated with five antipsychotic drugs (olanzapine, quetiapine, risperidone, ziprasidone, and perphenazine) as part of the Clinical Antipsychotic Trials of Intervention Effectiveness study. Doses for each of the medicines were optimized to 1, 2, 3, or 4x units in identically appearing capsules in a double-blind design. We analyzed 25 known functional genetic variants in the major and minor metabolizing enzymes for each medication. These variants were tested for association with optimized dose and other relevant clinical outcomes.

RESULTS

None of the tested variants showed a nominally significant main effect in association with any of the tested phenotypes in European-Americans, African-Americans, or all patients. Even after accounting for potential covariates, no genetic variant was found to be associated with dosing, efficacy, overall tolerability, or tardive dyskinesia.

CONCLUSION

There are no strong associations between common functional genetic variants in drug metabolizing enzymes and dosing, safety, or efficacy of leading antipsychotics, strongly suggesting merely modest effects on the use of these medicines in most patients in typical clinical settings.