Pharmacokinetic genes do not influence response or tolerance to citalopram in the STAR*D sample

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.

Pharmacomicrobiomics of Antidepressants in Depression: A Systematic Review

This systematic review evaluated the animal and human evidence for pharmacomicrobiomics (PMx) interactions of antidepressant medications. Studies of gut microbiota effects on functional and behavioral effects of antidepressants in human and animal models were identified from PubMed up to December 2022. Risk of bias was assessed, and results are presented as a systematic review following PRISMA guidelines. A total of 28 (21 animal, 7 human) studies were included in the review. The reviewed papers converged on three themes: (1) Antidepressants can alter the composition and metabolites of gut microbiota, (2) gut microbiota can alter the bioavailability of certain antidepressants, and (3) gut microbiota may modulate the clinical or modeled mood modifying effects of antidepressants. The majority (n = 22) of studies had at least moderate levels of bias present. While strong evidence is still lacking to understand the clinical role of antidepressant PMx in human health, there is evidence for interactions among antidepressants, microbiota changes, microbiota metabolite changes, and behavior. Well-controlled studies of the mediating and moderating effects of baseline and treatment-emergent changes in microbiota on therapeutic and adverse responses to antidepressants are needed to better establish a potential role of PMx in personalizing antidepressant treatment selection and response prediction.

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

Background

Variation within the CYP2C19 gene has been linked to differential metabolism of selective serotonin reuptake inhibitors (SSRIs). Pharmacogenetic recommendations based on the effect of CYP2C19 variants have been made available and are used increasingly by clinical practitioners. Nonetheless, the underlying assumption linking differential metabolism to efficacy or adverse side effects remains understudied. Here, we aim to fill this gap by studying CYP2C19 polymorphisms and inferred metabolism and patient-reported antidepressant response in a sample of 9531 Australian adults who have taken SSRIs.

Methods

Metaboliser status was inferred for participants based on CYP2C19 alleles. Primary analysis consisted of assessing differences in treatment efficacy and tolerability between normal (reference) and: ultrarapid, rapid, intermediate and poor metabolisers.

Results

Across medications, poor metabolisers reported a higher efficacy, whereas rapid metabolisers reported higher tolerability. When stratified by drug, associations between metaboliser status and efficacy did not survive multiple testing correction. Intermediate metabolisers were at greater odds of reporting any side effect for sertraline and higher number of side effects across medications and for sertraline.

Conclusions

The effects between metaboliser status and treatment efficacy, tolerability and side effects were in the expected direction. Our power analysis suggests we would detect moderate to large effects, at least nominally. Reduced power may also be explained by heterogeneity in antidepressant dosages or concomitant medications, which we did not measure. The fact that we identify slower metabolisers to be at higher risk of side effects even without adjusting for clinical titration, and the nominally significant associations consistent with the expected metabolic effects provide new evidence for the link between CYP2C19 metabolism and SSRI response. Nonetheless, longitudinal and interventional designs such as randomized clinical trials that stratify by metaboliser status are necessary to establish the effects of CYP2C19 metabolism on SSRI treatment efficacy or adverse effects.

ABCB1 variants and sex affect serotonin transporter occupancy in the brain

Strategies to personalize psychopharmacological treatment promise to improve efficacy and tolerability. We measured serotonin transporter occupancy immediately after infusion of the widely prescribed P-glycoprotein substrate citalopram and assessed to what extent variants of the ABCB1 gene affect drug target engagement in the brain in vivo. A total of 79 participants (39 female) including 31 patients with major depression and 48 healthy volunteers underwent two PET/MRI scans with the tracer [¹¹C]DASB and placebo-controlled infusion of citalopram (8 mg) in a cross-over design. We tested the effect of six ABCB1 single nucleotide polymorphisms and found lower SERT occupancy in ABCB1 rs2235015 minor allele carriers (n = 26, MAF = 0.18) compared to major allele homozygotes (t₇₃ = 2.73, p_FWE < 0.05) as well as in men compared to women (t₇₃ = 3.33, p_FWE < 0.05). These effects were robust to correction for citalopram plasma concentration, age and diagnosis. From occupancy we derived the ratio of occupied to unoccupied SERT, because in theory this measure is equal to the product of drug affinity and concentration at target sites. A model combining genotype with basic clinical variables, predicted that, at the same dosage, occupied to unoccupied SERT ratio was -14.48 ± 5.38% lower in rs2235015 minor allele carriers, +19.10 ± 6.95% higher in women, -4.83 ± 2.70% lower per 10 kg bodyweight, and -2.68 ± 3.07% lower per 10 years of age. Our results support the exploration of clinical algorithms with adjustment of initial citalopram dosing and highlight the potential of imaging-genetics for precision pharmacotherapy in psychiatry.

Tools for optimising pharmacotherapy in psychiatry (therapeutic drug monitoring, molecular brain imaging and pharmacogenetic tests): focus on antidepressants

Objectives: More than 40 drugs are available to treat affective disorders. Individual selection of the optimal drug and dose is required to attain the highest possible efficacy and acceptable tolerability for every patient.Methods: This review, which includes more than 500 articles selected by 30 experts, combines relevant knowledge on studies investigating the pharmacokinetics, pharmacodynamics and pharmacogenetics of 33 antidepressant drugs and of 4 drugs approved for augmentation in cases of insufficient response to antidepressant monotherapy. Such studies typically measure drug concentrations in blood (i.e. therapeutic drug monitoring) and genotype relevant genetic polymorphisms of enzymes, transporters or receptors involved in drug metabolism or mechanism of action. Imaging studies, primarily positron emission tomography that relates drug concentrations in blood and radioligand binding, are considered to quantify target structure occupancy by the antidepressant drugs in vivo. Results: Evidence is given that in vivo imaging, therapeutic drug monitoring and genotyping and/or phenotyping of drug metabolising enzymes should be an integral part in the development of any new antidepressant drug.Conclusions: To guide antidepressant drug therapy in everyday practice, there are multiple indications such as uncertain adherence, polypharmacy, nonresponse and/or adverse reactions under therapeutically recommended doses, where therapeutic drug monitoring and cytochrome P450 genotyping and/or phenotyping should be applied as valid tools of precision medicine.

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.

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.

Pharmacogenetic testing in psychiatric inpatients with polypharmacy is associated with decreased medication side effects but not via medication changes

In psychiatric patients, medication adverse effects are regularly attributed to psychosomatic causes. However, many psychotropic medications are metabolized by cytochrome P450 (CYP450) enzymes. In the setting of polypharmacy, the activity of these enzymes may produce unfavorable drug-drug interactions (DDI) and drug-genotype interactions (DGI) that contribute to morbidity and mortality. This study sought to estimate the risk of adverse DDI and DGI in psychiatric inpatients with polypharmacy. We assessed whether medication changes made after pharmacogenetics (PGx) testing correlated with changes in side effects and overall improvement. Adult psychiatry inpatients with polypharmacy, defined as 5 or more scheduled prescription medications, completed the 24-item Antidepressant Side Effect Checklist (ASEC) questionnaire on enrollment and underwent PGx testing. Analysis of PGx results focused on whether the CYP2D6 and CYP2C19 phenotypes were "extreme," defined as poor, poor to intermediate, or ultrarapid. Approximately 30 days after PGx results were sent to outpatient providers, patients were contacted to obtain their current medication list and ASEC and Clinical Global Impression Improvement (CGI-I) scores. A total of 80 patients were enrolled, and 52 (65%) completed follow-up. ASEC scores improved from 11.5 (±8.1) to 7.2 (±6.0) (p = 0.0009). Mean CGI-I score was 2.7 (±1.4), between "minimal" to "much improved." However, linear regression revealed that these improvements were not correlated with whether medications were changed. We concluded that the impact of drug-genotype interactions in this small sample of inpatients with polypharmacy was low, and that patient improvement was related not to PGx-guided medication changes but to other treatments during hospitalization.

Analysis of the Deleterious Single-Nucleotide Polymorphisms Associated With Antidepressant Efficacy in Major Depressive Disorder

Major depressive disorder (MDD) is a serious mental disease with negative effects on both mental and physical health of the patient. Currently, antidepressants are among the major ways to ease or treat MDD. However, the existing antidepressants have limited efficacy in treating MDD, with a large fraction of patients either responding inadequately or differently to antidepressants during the treatment. Pharmacogenetics studies have found that the genetic features of some genes are associated with the antidepressant efficacy. In order to obtain a better understanding on the relationship between the genetic factors and antidepressant treatment response, we compiled a list of 233 single-nucleotide polymorphisms (SNPs) significantly associated with the antidepressant efficacy in treating MDD. Of the 13 non-synonymous SNPs in the list, three (rs1065852, rs3810651, and rs117986340) may influence the structures and function of the corresponding proteins. Besides, the influence of rs1065852 on the structure of CYP2D6 was further investigated via molecular dynamics simulations. Our results showed that compared to the native CYP2D6 the flexibility of the F-G loop was reduced in the mutant. As a portion of the substrate access channel, the lower flexibility of F-G loop may reduce the ability of the substrates to enter the channel, which may be the reason for the lower enzyme activity of mutant. This study may help us to understand the impact of genetic variation on antidepressant efficacy and provide clues for developing new antidepressants.

Genetic testing for CYP2D6 and CYP2C19 suggests improved outcome for antidepressant and antipsychotic medication

Individuals carrying genetic variants that result in non-extensive CYP2D6 and CYP2C19 enzyme activity seem to be more prone to non-response and side-effects of psychotropic medications. Therefore, tailoring prescriptions using genetic information may improve patient outcomes. This study examined treatment outcome in psychiatric care after CYP2D6 and CYP2C19 genetic information was provided to patients and physicians. CYP2D6 and CYP2C19 genotyping, assessment of side effects and medical histories were obtained from 80 subjects who were prescribed either antidepressant or antipsychotic medications. Our measure of outcome was mainly physicians' opinions however UKU side effects scores were also used. For CYP2D6, we calculated an activity score based on genotype and psychiatric medications. Correlation analysis was performed for CYP2D6 activity scores and UKU scores. Overall, we received supportive responses from physicians who enrolled patients in our study. Notably, while almost every fourth physician reported improvement in patient outcome, not a single physician indicated that their patient's symptoms worsened after they had used a pharmacogenetic report to guide treatment. We did not observe statistically significant differences in side effects. Overall, our results suggest improved patient outcome following pharmacogenetic testing; nonetheless, more research is required to assess the exact benefit of pharmacogenetics in clinical practice.

ABCB1 Gene Is Associated With Clinical Response to SNRIs in a Local Chinese Han Population

Background: The relation between the ATP-binding cassette subfamily B member 1 (ABCB1) gene and major depressive disorder (MDD) has been studied in a local Chinese Han population. MDD is associated with the rs2032582 (G2677T) and rs1128503 (C1236T) single-nucleotide polymorphisms (SNPs) of ABCB1 but not with rs1045642, rs2032583, rs2235040, and rs2235015. This study aims to explore the potential correlations of therapeutic responses with selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) in a local Chinese Han population.

Methods: The study population included 292 patients with MDD. All patients were assessed at baseline and at first, second, fourth, and sixth weeks according to the 17-item Hamilton Rating Scale for Depression (HAM-D17) to determine their therapeutic responses to SSRIs and SNRIs.

Results: In the SSRI therapy group, the genotype or allele distribution of six SNPs was not significantly different between responders and nonresponders. In the SNRI therapy group, only rs2032583 was associated with a therapeutic response to SNRIs. The C allele of the ABCB1 rs2032583 polymorphism was negatively correlated with therapeutic responses according to logistic regression analysis.

Conclusion: The ABCB1 gene polymorphisms may not be associated with therapeutic responses to SSRIs but not with SNRIs. The TT genotype of rs2032583 could be a predictive factor of improved treatment responses to SNRIs in the Chinese population. These findings should be replicated in future studies with larger patient groups.

Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1,ERICH3,AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN‐AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

The Three Ps: Psychiatry, Pharmacy, and Pharmacogenomics, a Brief Report From New Zealand

We describe a case series of 22 individuals who were referred to our laboratory by a pharmacist based in a mental health hospital, for pharmacogenetic analysis due to severe or unexpected adverse drug reactions (ADRs) to psychiatric medication. The participants were genotyped for common variation in the CYP2D6, CYP2C19, and CYP2C9 genes, using Sanger sequencing. We tested variants in these genes as they have the strongest evidence with respect to altering the pharmacokinetics of commonly prescribed psychiatric medicine. Looking specifically at the subset of 18 European study participants, we observed a comparatively high but non-significant rate of pharmacogenetic variants, compared to allele frequency surveys in unselected population samples. For CYP2D6, we observed an elevated frequency of both poor (17%) and intermediate (33%) metabolizers when compared with previously reported frequencies (6% and 12% respectively). For CYP2C19, we observed an increased frequency of intermediate (33%) and ultra-rapid (17%) metabolizers compared to expected frequencies (21% and 4% respectively). For CYP2C9, the frequency of intermediate metabolizers (22%) was elevated compared to the expected population frequency (11%). While sample size is a major limitation of this brief report, we can conclude that patients with adverse reactions to antidepressant or antipsychotic drugs selected by a specialist mental health pharmacist appear to have a relatively high rate of genetic variants in pharmacogenes known to affect the pharmacokinetics of these drugs. The selective application of such pharmacogenetic tests by clinical pharmacists may be a valuable approach to clarify the basis for adverse or unusual responses to medication, and to guide ongoing prescribing decisions for this group of patients.

Genetic variants in major depressive disorder: From pathophysiology to therapy

In spite of promising preclinical results there is a decreasing number of new registered medications in major depression. The main reason behind this fact is the lack of confirmation in clinical studies for the assumed, and in animals confirmed, therapeutic results. This suggests low predictive value of animal studies for central nervous system disorders. One solution for identifying new possible targets is the application of genetics and genomics, which may pinpoint new targets based on the effect of genetic variants in humans. The present review summarizes such research focusing on depression and its therapy. The inconsistency between most genetic studies in depression suggests, first of all, a significant role of environmental stress. Furthermore, effect of individual genes and polymorphisms is weak, therefore gene x gene interactions or complete biochemical pathways should be analyzed. Even genes encoding target proteins of currently used antidepressants remain non-significant in genome-wide case control investigations suggesting no main effect in depression, but rather an interaction with stress. The few significant genes in GWASs are related to neurogenesis, neuronal synapse, cell contact and DNA transcription and as being nonspecific for depression are difficult to harvest pharmacologically. Most candidate genes in replicable gene x environment interactions, on the other hand, are connected to the regulation of stress and the HPA axis and thus could serve as drug targets for depression subgroups characterized by stress-sensitivity and anxiety while other risk polymorphisms such as those related to prominent cognitive symptoms in depression may help to identify additional subgroups and their distinct treatment. Until these new targets find their way into therapy, the optimization of current medications can be approached by pharmacogenomics, where metabolizing enzyme polymorphisms remain prominent determinants of therapeutic success.

Clinical Implementation of Pharmacogenetic Decision Support Tools for Antidepressant Drug Prescribing

The accrual and analysis of genomic sequencing data have identified specific genetic variants that are associated with major depressive disorder. Moreover, substantial investigations have been devoted to identifying gene-drug interactions that affect the response to antidepressant medications by modulating their pharmacokinetic or pharmacodynamic properties. Despite these advances, individual responses to antidepressants, as well as the unpredictability of adverse side effects, leave clinicians with an imprecise prescribing strategy that often relies on trial and error. These limitations have spawned several combinatorial pharmacogenetic testing products that are marketed to physicians. Typically, combinatorial pharmacogenetic decision support tools use algorithms to integrate multiple genetic variants and assemble the results into an easily interpretable report to guide prescribing of antidepressants and other psychotropic medications. The authors review the evidence base for several combinatorial pharmacogenetic decision support tools whose potential utility has been evaluated in clinical settings. They find that, at present, there are insufficient data to support the widespread use of combinatorial pharmacogenetic testing in clinical practice, although there are clinical situations in which the technology may be informative, particularly in predicting side effects.

Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: Meta-analysis of data from genome-wide association studies

Cytochrome (CYP) P450 enzymes have a primary role in antidepressant metabolism and variants in these polymorphic genes are targets for pharmacogenetic investigation. This is the first meta-analysis to investigate how CYP2C19 polymorphisms predict citalopram/escitalopram efficacy and side effects. CYP2C19 metabolic phenotypes comprise poor metabolizers (PM), intermediate and intermediate+ metabolizers (IM; IM+), extensive and extensive+ metabolizers (EM [wild type]; EM+) and ultra-rapid metabolizers (UM) defined by the two most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560) in Caucasians. These polymorphisms were genotyped or imputed from genome-wide data in four samples treated with citalopram or escitalopram (GENDEP, STAR*D, GenPod, PGRN-AMPS). Treatment efficacy was assessed by standardized percentage symptom improvement and by remission. Side effect data were available at weeks 2-4, 6 and 9 in three samples. A fixed-effects meta-analysis was performed using EM as the reference group. Analysis of 2558 patients for efficacy and 2037 patients for side effects showed that PMs had higher symptom improvement (SMD = 0.43, CI = 0.19-0.66) and higher remission rates (OR = 1.55, CI = 1.23-1.96) compared to EMs. At weeks 2-4, PMs showed higher risk of gastro-intestinal (OR = 1.26, CI = 1.08-1.47), neurological (OR = 1.28, CI = 1.07-1.53) and sexual side effects (OR = 1.52, CI = 1.23-1.87; week 6 values were similar). No difference was seen at week 9 or in total side effect burden. PMs did not have higher risk of dropout at week 4 compared to EMs. Antidepressant dose was not different among CYP2C19 groups. CYP2C19 polymorphisms may provide helpful information for guiding citalopram/escitalopram treatment, despite PMs being relatively rare among Caucasians (∼2%).

Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years

BACKGROUND

Antidepressant drugs are widely prescribed, but response rates after 3 months are only around one-third, explaining the importance of the search of objectively measurable markers predicting positive treatment response. These markers are being developed in different fields, with different techniques, sample sizes, costs, and efficiency. It is therefore difficult to know which ones are the most promising.

OBJECTIVE

Our purpose was to compute comparable (i.e., standardized) effect sizes, at study level but also at marker level, in order to conclude on the efficacy of each technique used and all analyzed markers.

METHODS

We conducted a systematic search on the PubMed database to gather all articles published since 2000 using objectively measurable markers to predict antidepressant response from five domains, namely cognition, electrophysiology, imaging, genetics, and transcriptomics/proteomics/epigenetics. A manual screening of the abstracts and the reference lists of these articles completed the search process.

RESULTS

Executive functioning, theta activity in the rostral Anterior Cingular Cortex (rACC), and polysomnographic sleep measures could be considered as belonging to the best objectively measured markers, with a combined d around 1 and at least four positive studies. For inter-category comparisons, the approaches that showed the highest effect sizes are, in descending order, imaging (combined d between 0.703 and 1.353), electrophysiology (0.294-1.138), cognition (0.929-1.022), proteins/nucleotides (0.520-1.18), and genetics (0.021-0.515).

CONCLUSION

Markers of antidepressant treatment outcome are numerous, but with a discrepant level of accuracy. Many biomarkers and cognitions have sufficient predictive value (d ≥ 1) to be potentially useful for clinicians to predict outcome and personalize antidepressant treatment.

Effect of ABCB1 C3435T Polymorphism on Pharmacokinetics of Antipsychotics and Antidepressants

P-glycoprotein, encoded by ABCB1, is an ATP-dependent drug efflux pump which exports substances outside the cell. Some studies described connections between C3435T polymorphism T allele and lower P-glycoprotein expression; therefore, homozygous T/T could show higher plasma levels. Our aim was to evaluate the effect of C3435T on pharmacokinetics of 4 antipsychotics (olanzapine, quetiapine, risperidone and aripiprazole) and 4 antidepressants (trazodone, sertraline, agomelatine and citalopram). The study included 473 healthy volunteers receiving a single oral dose of one of these drugs, genotyped by real-time PCR. Multivariate analysis was performed to adjust the effect of sex and genotype of the main cytochrome P450 enzymes. C3435T polymorphism had an effect on olanzapine pharmacokinetics, as T/T individuals showed lower clearance and volume of distribution. T/T individuals showed lower T_1/2 of 9-OH-risperidone, but this difference disappeared after multivariate correction. T/T homozygous individuals showed lower dehydro-aripiprazole and trazodone area under the concentration-time curve, along with lower half-life and higher clearance of trazodone. C/T genotype was associated to higher citalopram maximum concentration. C3435T had no effect on quetiapine, sertraline or agomelatine pharmacokinetics. C3435T can affect the elimination of some drugs in different ways. Regarding risperidone, trazodone and dehydro-aripiprazole, we observed enhanced elimination while it was reduced in olanzapine and citalopram. However, in quetiapine, aripiprazole, sertraline and agomelatine, no changes were detected. These results suggest that P-glycoprotein polymorphisms could affect CNS drugs disposition, but the genetic factor that alters its activity is still unknown. This fact leads to consider the analysis of ABCB1 haplotypes instead of individual variants.

Peripheral biomarkers of major depression and antidepressant treatment response: Current knowledge and future outlooks

BACKGROUND

In recent years, we have accomplished a deeper understanding about the pathophysiology of major depressive disorder (MDD). Nevertheless, this improved comprehension has not translated to improved treatment outcome, as identification of specific biologic markers of disease may still be crucial to facilitate a more rapid, successful treatment. Ongoing research explores the importance of screening biomarkers using neuroimaging, neurophysiology, genomics, proteomics, and metabolomics measures.

RESULTS

In the present review, we highlight the biomarkers that are differentially expressed in MDD and treatment response and place a particular emphasis on the most recent progress in advancing technology which will continue the search for blood-based biomarkers.

LIMITATIONS

Due to space constraints, we are unable to detail all biomarker platforms, such as neurophysiological and neuroimaging markers, although their contributions are certainly applicable to a biomarker review and valuable to the field.

CONCLUSIONS

Although the search for reliable biomarkers of depression and/or treatment outcome is ongoing, the rapidly-expanding field of research along with promising new technologies may provide the foundation for identifying key factors which will ultimately help direct patients toward a quicker and more effective treatment for MDD.

Suggested Biomarkers for Major Depressive Disorder

Currently, the diagnosis of major depressive disorder (MDD) mainly relies on clinical examination and subjective evaluation of depressive symptoms. There is no non-invasive, quantitative test available today for the diagnosis of MDD. In MDD, exploration of biomarkers will be helpful in diagnosing the disorder as well as in choosing a treatment, and predicting the treatment response. In this article, it is aimed to review the findings of suggested biomarkers such as growth factors, cytokines and other inflammatory markers, oxidative stress markers, endocrine markers, energy balance hormones, genetic and epigenetic features, and neuroimaging in MDD and to evaluate how these findings contribute to the pathophysiology of MDD, the prediction of treatment response, severity of the disorder, and identification of subtypes. Among these, the findings related to the brain-derived neurotrophic factor, the hypothalamo-pituitary-adrenal axis, cytokines, and neuroimaging may be strong candidates for being biomarkers MDD, and may provide critical information in understanding biological etiology of depression. Although the findings are not sufficient yet, we think that the results of epigenetic studies will also provide very important contributions to the biomarker research in MDD. The availability of biomarkers in MDD will be an advancement that will facilitate the diagnosis of the disorder, treatment choices in the early stages, and prediction of the course of the disorder.

Blood-based biomarkers predicting response to antidepressants

Major depressive disorder is a common, serious and in some cases, life-threatening condition and affects approximately 350 million people globally. Although there is effective treatment available for it, more than 50% of the patients fail to respond to the first antidepressant they receive. The selection of a distinct treatment is still exclusively based on clinical judgment without incorporating lab-derived objective measures. However, there is growing evidence of biomarkers that it helps to improve diagnostic processes and treatment algorithms. Here genetic markers and blood-based biomarkers of the monoamine pathways, inflammatory pathways and the hypothalamic-pituitary-adrenal (HPA) axis are reviewed. Promising findings arise from studies investigating inflammatory pathways and immune markers that may identify patients suitable for anti-inflammatory based treatment regimes. Next, an early normalization of a disturbed HPA axis or depleted neurotrophic factors may predict stable treatment response. Genetic markers within the serotonergic system may identify patients who are vulnerable because of stressful life events, but evidence for guiding treatment regimes still is inconsistent. Therefore, there is still a great need for studies investigating and validating biomarkers for the prediction of treatment response to facilitate the treatment selection and shorten the time to remission and thus provide personalized medicine in psychiatry.

Pharmacogenomics and Biomarkers of Depression

The standard of care for antidepressant treatment in major depressive disorder (MDD) is a trial-and-error approach. Patients often have to undergo multiple medication trials for weeks to months before finding an effective treatment. Clinical factors such as severity of baseline symptoms and the presence of specific individual (anhedonia or insomnia) or cluster (atypical, melancholic, or anxious) of symptoms are commonly used without any evidence of their utility in selecting among currently available antidepressants. Genomic and proteomic biomarker have gained recent attention for their potential in informing antidepressant medication selection. In this report, we have reviewed some of the major pharmacogenomics studies along with individual genetic and proteomic biomarker of antidepressant response. Additionally, we have reviewed the blood-based protein biomarkers that can inform selection of one antidepressant over another. Among all currently available biomarkers, C-reactive protein (CRP) appears to be the most promising and pragmatic choice. Low CRP (<1 mg/L) in patients with MDD predicts better response to escitalopram while higher levels are associated with better response to noradrenergic/dopaminergic antidepressants. Future studies are needed to demonstrate the superiority of a CRP-based treatment assignment over high-quality measurement-based care in real-world clinical practices.

Genetic testing as a supporting tool in prescribing psychiatric medication: Design and protocol of the IMPACT study

OBJECTIVE

Pharmacotherapy is one of the primary treatments for psychiatric disorders. Given the variation in individual response, a more personalized approach is needed. This paper will discuss methods for user-friendly referrals, recruitment criteria, data storage and dissemination, biological sample and clinical questionnaire collection, and advertising.

METHODS

The Individualized Medicine: Pharmacogenetics Assessment and Clinical Treatment (IMPACT) study is one of the first to use pharmacogenetic testing on a large scale in psychiatry as a tool to predict individual drug response and tolerability. As IMPACT's eligibility criteria includes all diagnoses and comorbidities, the participant population will reflect the diversity amongst mental health consumers. IMPACT's innovative study design will demonstrate the utility of this testing within the health care system.

RESULTS

IMPACT has successfully implemented pharmacogenetic testing on a relatively large scale, and in both tertiary level and primary care settings. It represents a novel approach to psychiatric care and from its initial stages the design has evolved to accommodate the nature and needs of the health care community.

CONCLUSION

It is anticipated that IMPACT will continue to demonstrate the feasibility of pharmacogenetic testing and facilitate its introduction and implementation in routine healthcare practice.

Optimization of Antidepressant use with Pharmacogenetic Strategies

BACKGROUND

The response rate in the pharmacological treatment of depression has been estimated to be around 50%, achieving a remission in symptomatology in only one third of the patients. Suboptimal prescription of antidepressants has been proposed as a significant explanatory factor for this therapeutic inefficacy. The use of pharmacogenetic testing might favor the optimization of pharmacotherapy in emotional disorders. However, its implementation in the clinical routine requires studies which prove its efficacy.

OBJECTIVE

The aim is to explore the clinical effects obtained by means of the personalization of antidepressant treatment derived from the pharmacogenetic profile of the individual.

METHOD

A sample of 291 patients under antidepressant treatment was selected, and these patients were genotyped for the most common polymorphisms of the CYP2D6, CYP2C9, CYP2C19 and CYP3A4/5 genes using RT-PCR and TaqMan® technology. 30 of them were subjected to psycho-affective assessment using the HDRS scale before and after a process of individualization of their psychopharmacological treatment in accordance with the genotype obtained.

RESULTS

70% of the individuals treated using the traditional criterion of trial-and-error were not taking the active ingredient most suited to their pharmacogenetic profile. The inclusion of this genetic information in the choice of drug and its dosage entailed a significant, progressive reduction in depressive symptomatology, with an efficacy ratio of 80% and a remission of the pathology in almost 30% of the cases.

CONCLUSION

These results suggest that the prescription of pharmacogenetic profile-based strategies has a positive effect on the therapeutic response to antidepressants.

Optimization of Antidepressant use with Pharmacogenetic Strategies

Background:

The response rate in the pharmacological treatment of depression has been estimated to be around 50%, achieving a remission in symptomatology in only one third of the patients. Suboptimal prescription of antidepressants has been proposed as a significant explanatory factor for this therapeutic inefficacy. The use of pharmacogenetic testing might favor the optimization of pharmacotherapy in emotional disorders. However, its implementation in the clinical routine requires studies which prove its efficacy.

Objective:

The aim is to explore the clinical effects obtained by means of the personalization of antidepressant treatment derived from the pharmacogenetic profile of the individual.

Method:

A sample of 291 patients under antidepressant treatment was selected, and these patients were genotyped for the most common polymorphisms of the CYP2D6, CYP2C9, CYP2C19 and CYP3A4/5 genes using RT-PCR and TaqMan® technology. 30 of them were subjected to psycho-affective assessment using the HDRS scale before and after a process of individualization of their psychopharmacological treatment in accordance with the genotype obtained.

Results:

70% of the individuals treated using the traditional criterion of trial-and-error were not taking the active ingredient most suited to their pharmacogenetic profile. The inclusion of this genetic information in the choice of drug and its dosage entailed a significant, progressive reduction in depressive symptomatology, with an efficacy ratio of 80% and a remission of the pathology in almost 30% of the cases.

Conclusion:

These results suggest that the prescription of pharmacogenetic profile-based strategies has a positive effect on the therapeutic response to antidepressants.

Personalized prescribing: a new medical model for clinical implementation of psychotropic drugs

The use of pharmacogenetic tests was already being proposed in psychiatry in the early 2000s because genetic factors were known to influence drug pharmacokinetics and pharmacodynamics. However, sufficient levels of evidence to justify routine use have been achieved for only a few tests (eg, major histocompatibility complex, class I, B, allele 1502 [HLA-B*1502] for carbamazepine in epilepsy and bipolar disorders); many findings are too preliminary or, when replicated, of low clinical relevance because of a small effect size. Although drug selection and dose adaptation according to cytochrome P450 genotypes are sound, a large number of patients need to be genotyped in order to prevent one case of severe side effect and/or nonresponse. The decrease in cost for genetic analysis shifts the cost: benefit ratio toward increasing use of pharmacogenetic tests. However, they have to be combined with careful clinical evaluations and other tools (eg, therapeutic drug monitoring and phenotyping) to contribute to the general aim of providing the best care for psychiatric patients.

Possibility of Predicting Serotonin Transporter Occupancy From the In Vitro Inhibition Constant for Serotonin Transporter, the Clinically Relevant Plasma Concentration of Unbound Drugs, and Their Profiles for Substrates of Transporters

Accurate prediction of target occupancy facilitates central nervous system drug development. In this review, we discuss the predictability of serotonin transporter (SERT) occupancy in human brain estimated from in vitro K_i values for human SERT and plasma concentrations of unbound drug (C_u,plasma), as well as the impact of drug transporters in the blood-brain barrier. First, the geometric means of in vitro K_i values were compared with the means of in vivo K_i values (K_i,u,plasma) which were calculated as C_u,plasma values at 50% occupancy of SERT obtained from previous clinical positron emission tomography/single photon emission computed tomography imaging studies for 6 selective serotonin transporter reuptake inhibitors and 3 serotonin norepinephrine reuptake inhibitors. The in vitro K_i values for 7 drugs were comparable to their in vivo K_i,u,plasma values within 3-fold difference. SERT occupancy was overestimated for 5 drugs (P-glycoprotein substrates) and underestimated for 2 drugs (presumably uptake transporter substrates, although no evidence exists as yet). In conclusion, prediction of human SERT occupancy from in vitro K_i values and C_u,plasma was successful for drugs that are not transporter substrates and will become possible in future even for transporter substrates, once the transporter activities will be accurately estimated from in vitro experiments.

Pharmacogenetics and Imaging-Pharmacogenetics of Antidepressant Response: Towards Translational Strategies

Genetic variation underlies both the response to antidepressant treatment and the occurrence of side effects. Over the past two decades, a number of pharmacogenetic variants, among these the SCL6A4, BDNF, FKBP5, GNB3, GRIK4, and ABCB1 genes, have come to the forefront in this regard. However, small effects sizes, mixed results in independent samples, and conflicting meta-analyses results led to inherent difficulties in the field of pharmacogenetics translating these findings into clinical practice. Nearly all antidepressant pharmacogenetic variants have potentially pleiotropic effects in which they are associated with major depressive disorder, intermediate phenotypes involved in emotional processes, and brain areas affected by antidepressant treatment. The purpose of this article is to provide a comprehensive review of the advances made in the field of pharmacogenetics of antidepressant efficacy and side effects, imaging findings of antidepressant response, and the latest results in the expanding field of imaging-pharmacogenetics studies. We suggest there is mounting evidence that genetic factors exert their impact on treatment response by influencing brain structural and functional changes during antidepressant treatment, and combining neuroimaging and genetic methods may be a more powerful way to detect biological mechanisms of response than either method alone. The most promising imaging-pharmacogenetics findings exist for the SCL6A4 gene, with converging associations with antidepressant response, frontolimbic predictors of affective symptoms, and normalization of frontolimbic activity following antidepressant treatment. More research is required before imaging-pharmacogenetics informed personalized medicine can be applied to antidepressant treatment; nevertheless, inroads have been made towards assessing genetic and neuroanatomical liability and potential clinical application.

ABCB1 genotyping in the treatment of depression

P-glycoprotein (P-gp), the gene product of ABCB1, is a drug transporter at the blood–brain barrier and could be a limiting factor for entrance of antidepressants into the brain, the target site of antidepressant action. Animal studies showed that brain concentrations of many antidepressants depend on P-gp. In humans, ABCB1 genotyping in the treatment of depression rests on the assumption that genetic variations in ABCB1 explain individual differences in antidepressant response via their effects on P-gp expression at the blood–brain barrier. High P-gp expression is hypothesized to lead to lower and often insufficient brain concentrations of P-gp substrate antidepressants. In this review, we summarize 32 studies investigating the question of whether ABCB1 polymorphisms predict clinical efficacy and/or tolerability of antidepressants in humans and evaluate the clinical application status of ABCB1 genotyping in depression treatment.

Role of glutamate receptors and glial cells in the pathophysiology of treatment-resistant depression

Treatment-resistant depression (TRD) causes substantial socioeconomic burden. Although a consensus on the definition of TRD has not yet been reached, it is certain that classic monoaminergic antidepressants are ineffective for TRD. One decade ago, many researchers found ketamine, an N-methyl-d-aspartate receptor (NMDAR) antagonist, to be an alternative to classic monoaminergic antidepressants. The major mechanisms of action of ketamine rapidly induce synaptogenesis in the brain-derived neurotrophic factor (BDNF) pathway. Although excessive glutamatergic neurotransmission and consequent excitotoxicity were considered a major cause of TRD, recent evidence suggests that the extrasynaptic glutamatergic receptor signal pathway mainly contributes to the detrimental effects of TRD. Glial cells such as microglia and astrocytes, early life adversity, and glucocorticoid receptor dysfunction participate in complex cross-talk. An appropriate reuptake of glutamate at the astrocyte is crucial for preventing 'spill-over' of synaptic glutamate and binding to the extrasynaptic NMDA receptor. Excessive microglial activation and the inflammatory process cause astrocyte glutamatergic dysfunction, which in turn activates microglial function. Early life adversity and glucocorticoid receptor dysfunction result in vulnerability to stress in adulthood. A maladaptive response to stress leads to increased glutamatergic release and pro-inflammatory cytokines, which then activate microglia. However, since the role of inflammatory mediators such as pro-inflammatory cytokines is not specific for depression, more disease-specific mechanisms should be identified. Last, although much research has focused on ketamine as an alternative antidepressant for TRD, its long-lasting effectiveness and adverse events have not been rigorously demonstrated. Additionally, evidence suggests that substantial brain abnormalities develop in ketamine abusers. Thus, more investigations for ketamine and other novel glutamatergic agents are needed.

Identifying genetic loci affecting antidepressant drug response in depression using drug-gene interaction models

Antidepressants are often only moderately successful in decreasing the severity of depressive symptoms. In part, antidepressant treatment response in patients with depression is genetically determined. However, although a large number of studies have been conducted aiming to identify genetic variants associated with antidepressant drug response in depression, only a few variants have been repeatedly identified. Within the present review, we will discuss the methodological challenges and limitations of the studies that have been conducted on this topic to date (e.g., 'treated-only design', statistical power) and we will discuss how specifically drug-gene interaction models can be used to be better able to identify genetic variants associated with antidepressant drug response in depression.

Genetic variation in the tryptophan hydroxylase 2 gene moderates depressive symptom trajectories and remission over 8 weeks of escitalopram treatment

The serotonin system plays an important role in the pathogenesis of major depressive disorder (MDD) and genetic variations in serotonin-related genes affect the efficacy of antidepressants. The aim of this study was to investigate the relationship between genotypic variation in six candidate serotonergic genes (ADCY9, HTR1B, GNB3, HTR2A, TPH2, SLC6A4) and depressive and anxiety symptom severity trajectories as well as remission following escitalopram treatment. A total of 166 Chinese patients with MDD were treated with escitalopram (open-label) for 8 weeks. TPH2 rs4570625 GG carriers were more likely to achieve depressive and anxiety symptom remission compared with T-allele carriers. At the trend level (P(corrected)=0.05), depressive symptom severity trajectories were moderated by TPH2 rs4570625. Patients with the GT or the GG genotype showed more favorable depressive symptom severity trajectories compared with TT genotype carriers. Polymorphisms in ADCY9, HTR1B, and HTR2A were nominally associated with symptom remission, but did not withstand correction for multiple comparisons. The HTTLPR polymorphism was not included in our final analysis because of a high percentage of missing data. These results suggested that genotypic variation in TPH2 may moderate the therapeutic response to esciatlopram among Chinese patients with MDD.

Measuring citalopram in blood and cerebrospinal fluid: revealing a distribution pattern that differs from other antidepressants

The aim of this study was to measure blood and cerebrospinal fluid concentrations of citalopram and its weakly active N-demethylated metabolite desmethylcitalopram to account for the distribution between the two compartments. The findings are discussed in the context with own preceding studies on the distribution pattern of different antidepressants. Concentrations of citalopram were measured in blood serum and cerebrospinal fluid of 18 patients treated with daily doses of 10-40 mg. Daily doses were correlated with serum and cerebrospinal fluid concentrations, and serum concentrations were correlated with concentrations in cerebrospinal fluid. Serum concentrations of citalopram and desmethylcitalopram showed no significant correlation to the daily dose, r=0.164, P=0.515, and r=0.174, P=0.505, respectively, whereas citalopram concentrations in serum and cerebrospinal fluid were highly correlated (r=0.763, P<0.001). The cerebrospinal fluid/serum ratio for citalopram (total=bound+unbound concentration) varied between 0.14 and 0.86 (mean 0.35, SD 0.16). By correcting the mean cerebrospinal fluid/serum ratio for 80% plasma protein binding, cerebrospinal fluid concentrations of citalopram were on average 77% higher than the calculated unbound serum concentration with a ratio of 1.77 (SD 0.81, range 0.68-4.29). Findings indicate a very good ability of citalopram to cross the blood-brain and cerebrospinal fluid barrier. High concentrations of citalopram in the cerebrospinal fluid are indicative of active transport of citalopram into or missing active transport out of the cerebrospinal fluid. The results suggest a high ability of citalopram to enter the brain with sufficiently high drug concentrations at the target sites within the brain, contributing toward clinical efficacy.

Pharmacogenetics of major depressive disorder: top genes and pathways toward clinical applications

The pharmacogenetics of antidepressants has been not only a challenging but also frustrating research field since its birth in the 1990s. Indeed, great expectations followed the first evidence of familiar aggregation of antidepressant response. Despite the progress from candidate gene studies to genome-wide association studies (GWAS), results fell out the expectations and they were often inconsistent. Anyway, the cumulative evidence supports the involvement of some genes and molecular pathways in antidepressant efficacy. The best single genes are SLC6A4, HTR2A, BDNF, GNB3, FKBP5, ABCB1, and cytochrome P450 genes (CYP2D6 and CYP2C19). Molecular pathways involved in inflammation and neuroplasticity show the greatest support. The first studies evaluating benefits of genotype-guided antidepressant treatments provided encouraging results and confirmed the relevance of SLC6A4, HTR2A, ABCB1, and cytochrome P450 genes. Further progress in genotyping and data analysis would allow to move forward and complete the understanding of antidepressant pharmacogenetics and its translation into clinical applications.

Exploring the role of drug-metabolising enzymes in antidepressant side effects

RATIONALE

Cytochrome P450 enzymes are important in the metabolism of antidepressants. The highly polymorphic nature of these enzymes has been linked to variability in antidepressant metabolism rates, leading to hope regarding the use of P450 genotyping to guide treatment. However, evidence that P450 genotypic differences underlie the variation in treatment outcomes is inconclusive.

OBJECTIVES

We explored the links between both P450 genotype and serum concentrations of antidepressant with antidepressant side effects, using data from the Genome-Based Therapeutic Drugs for Depression Project (GENDEP), which is a large (n = 868), pharmacogenetic study of depressed individuals treated with escitalopram or nortriptyline.

METHODS

Patients were genotyped for the enzymes CYP2C19 and CYP2D6, and serum concentrations of both antidepressant and primary metabolite were measured after 8 weeks of treatment. Side effects were assessed weekly. We investigated associations between P450 genotypes, serum concentrations of antidepressants and side effects, as well as the relationship between P450 genotype and study discontinuation.

RESULTS

P450 genotype did not predict total side effect burden (nortriptyline: n = 251, p = 0.5638, β = -0.133, standard error (SE) = 0.229; escitalopram: n = 340, p = 0.9627, β = -0.004, SE = 0.085), study discontinuation (nortriptyline n = 284, hazard ratio (HR) = 1.300, p = 0.174; escitalopram n = 376, HR = 0.870, p = 0.118) or specific side effects. Serum concentrations of antidepressant were only related to a minority of the specific side effects measured: dry mouth, dizziness and diarrhoea.

CONCLUSIONS

In this sample where antidepressant dosage is titrated using clinical judgement, P450 genotypes do not explain differences between patients in side effects with antidepressants. Serum drug concentrations appear to only explain variability in the occurrence of a minority of specific side effects.

ABCB1 gene variants and antidepressant treatment outcome: A meta-analysis

The efflux pump P-glycoprotein (P-gp), a gene product of the ABCB1 gene, plays a pivotal role in the transfer of various molecules across the blood-brain barrier. P-gp protects the brain by selectively extruding its substrates, including certain antidepressive drugs, thereby limiting their uptake into the brain. Uhr et al. [2008] first showed that ABCB1 variants predicted the remission to antidepressants with P-gp substrate properties in patients suffering from major depression (MD). Other studies investigating the influence of ABCB1 polymorphisms on antidepressant treatment response produced inconclusive results. In this meta-analysis, we systematically summarized 16 pharmacogenetic studies focused on the association of ABCB1 variants and antidepressant treatment outcome in patients with MD (overall n = 2695). We investigated the association of treatment outcome and six ABCB1 single nucleotide polymorphisms (SNPs): rs2032583, rs2235015, rs2235040, rs1045642, rs2032582, rs1128503. We stratified for admission status, ethnicity, and prescription of concomitant medication. SNP rs2032583 showed a nominally significant association across all studies (P = 0.035, SNP was studied in a total of 2,037 patients) and a significant Bonferroni-corrected association among inpatients (P = 1.5 × 10(-05) , n = 485). Also SNP rs2235015 was significantly associated with antidepressant treatment outcome withstanding Bonferroni correction (P = 3.0 × 10(-04) ) among inpatients in a smaller subsample (n = 195). There were no significant associations of the other SNPs tested with antidepressant treatment outcome. Future pharmacogenetic association studies should focus on the role of the ABCB1 SNP rs2032583 in antidepressant outcome prediction.

Effect of Cytochrome P450 polymorphism on the action and metabolism of selective serotonin reuptake inhibitors

INTRODUCTION

The aim of this article is to review the field of clinically relevant pharmacogenetic effects of cytochrome P450 polymorphisms on metabolism, kinetics, and action of selective serotonin reuptake inhibitors (SSRIs).

AREAS COVERED

The relevant literature in humans on the implications of genetic variation on SSRI drug exposure, drug safety, and efficacy was systematically evaluated. There is a large amount of evidence on the influences of CYP polymorphisms on the pharmacokinetics of SSRIs. Regulatory agencies have issued warnings or advice considering dose adjustments in the presence of affected metabolic phenotypes for several SSRIs. Evidence-based dose adjustments for drugs dependent on CYP genotype are available to clinicians. However, few data on the relationship between genetically determined elevated plasma concentrations of SSRIs and specific side effects or therapeutic failure are currently available.

EXPERT OPINION

Genetic polymorphisms in CYP2D6 and CYP2C19 exert large influences on the individual exposure to SSRIs, leading to the aim to achieve similar concentration time courses in different metabolizer phenotypes. The implementation of a stratified approach to medication with SSRIs in different metabolic phenotypes on a rational basis will require new studies assessing the association between clinical outcomes (such as adverse reactions) and genetically determined elevated plasma concentrations.

Evidence-based guidelines for treating depressive disorders with antidepressants: A revision of the 2008 British Association for Psychopharmacology guidelines

A revision of the 2008 British Association for Psychopharmacology evidence-based guidelines for treating depressive disorders with antidepressants was undertaken in order to incorporate new evidence and to update the recommendations where appropriate. A consensus meeting involving experts in depressive disorders and their management was held in September 2012. Key areas in treating depression were reviewed and the strength of evidence and clinical implications were considered. The guidelines were then revised after extensive feedback from participants and interested parties. A literature review is provided which identifies the quality of evidence upon which the recommendations are made. These guidelines cover the nature and detection of depressive disorders, acute treatment with antidepressant drugs, choice of drug versus alternative treatment, practical issues in prescribing and management, next-step treatment, relapse prevention, treatment of relapse and stopping treatment. Significant changes since the last guidelines were published in 2008 include the availability of new antidepressant treatment options, improved evidence supporting certain augmentation strategies (drug and non-drug), management of potential long-term side effects, updated guidance for prescribing in elderly and adolescent populations and updated guidance for optimal prescribing. Suggestions for future research priorities are also made.