Genetic polymorphisms of cytochrome P450 enzymes and antidepressant metabolism

A machine learning approach to predict treatment efficacy and adverse effects in major depression using CYP2C19 and clinical-environmental predictors

BACKGROUND

Major depressive disorder (MDD) is among the leading causes of disability worldwide and treatment efficacy is variable across patients. Polymorphisms in cytochrome P450 2C19 (CYP2C19) play a role in response and side effects to medications; however, they interact with other factors. We aimed to predict treatment outcome in MDD using a machine learning model combining CYP2C19 activity and nongenetic predictors.

METHODS

A total of 1410 patients with MDD were recruited in a cross-sectional study. We extracted the subgroup treated with psychotropic drugs metabolized by CYP2C19. CYP2C19 metabolic activity was determined by the combination of *1, *2, *3, and *17 alleles. We tested if treatment response, treatment-resistant depression, and side effects could be inferred from CYP2C19 activity in combination with clinical-demographic and environmental features. The model used for the analysis was based on a decision tree algorithm using five-fold cross-validation.

RESULTS

A total of 820 patients were treated with CYP2C19 metabolized drugs. The predictive performance of the model showed at best.70 accuracy for the classification of treatment response (average accuracy = 0.65, error = ±0.047) and an average accuracy of approximately 0.57 across all the tested outcomes. Age, BMI, and baseline depression severity were the main features influencing prediction across all the tested outcomes. CYP2C19 metabolizing status influenced both response and side effects but to a lower extent than the previously indicated features.

CONCLUSION

Predictive modeling could contribute to precision psychiatry. However, our study underlines the difficulty in selecting variables with sufficient impact on complex outcomes.

Association between polygenic risk scores combined with clinical characteristics and antidepressant efficacy

BACKGROUND

While millions of people suffer from major depressive disorder (MDD), research has shown that individual differences in antidepressant efficacy exist, potentially attributable to various factors. Polygenic risk scores (PRSs) carry clinical potential, but associations with treatment response are seldom reported. Here, we examined whether PRSs for MDD and schizophrenia (SCZ) are associated with antidepressant effectiveness and the influence of other factors.

METHODS

A total of 999 patients were included, and the PRSs for the MDD and SCZ were calculated. The main outcome was a change in the 17-item Hamilton Depression Rating Scale (HAMD17) scores from before to after 2-week treatment. The Mann-Whitney test, Spearman correlation analysis, multiple stepwise linear regression analysis, and interaction analysis were used for statistical analysis.

RESULTS

In the 912 subjects passing quality control, a difference in the HAM-D17 score reduction rate between the MDD phenotype PRS (MDD-PRS) high-risk and the low-risk groups was discovered (P = 0.009), and a correlation was found between the MDD-PRS and the HAM-D17 score reduction rate (r = -0.075, P = 0.024). Moreover, antidepressant efficacy was related to MDD-PRS (β = -4.086, P = 0.039), the Snaith-Hamilton Pleasure Scale-total score (β = -0.009, P = 0.005), and non-first episode (β = -0.039, P < 0.001). However, the result of the interaction analysis was nonsignificant.

LIMITATIONS

The main limitation was that only 1309 targeted genes were selected based on pathways known to be involved in MDD and/or antidepressant effects.

CONCLUSION

These findings suggest a difference in antidepressant efficacy between patients in different MDD-PRS groups. Moreover, the MDD-PRS combined with clinical characteristics partially explained inter-individual differences in antidepressant efficacy.

A multi-ancestry cerebral cortex transcriptome-wide association study identifies genes associated with smoking behaviors

Transcriptome-wide association studies (TWAS) have provided valuable insight in identifying genes that may impact cigarette smoking. Most of previous studies, however, mainly focused on European ancestry. Limited TWAS studies have been conducted across multiple ancestries to explore genes that may impact smoking behaviors. In this study, we used cis-eQTL data of cerebral cortex from multiple ancestries in MetaBrain, including European, East Asian, and African samples, as reference panels to perform multi-ancestry TWAS analyses on ancestry-matched GWASs of four smoking behaviors including smoking initiation, smoking cessation, age of smoking initiation, and number of cigarettes per day in GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Multiple-ancestry fine-mapping approach was conducted to identify credible gene sets associated with these four traits. Enrichment and module network analyses were further performed to explore the potential roles of these identified gene sets. A total of 719 unique genes were identified to be associated with at least one of the four smoking traits across ancestries. Among those, 249 genes were further prioritized as putative causal genes in multiple ancestry-based fine-mapping approach. Several well-known smoking-related genes, including PSMA4, IREB2, and CHRNA3, showed high confidence across ancestries. Some novel genes, e.g., TSPAN3 and ANK2, were also identified in the credible sets. The enrichment analysis identified a series of critical pathways related to smoking such as synaptic transmission and glutamate receptor activity. Leveraging the power of the latest multi-ancestry GWAS and eQTL data sources, this study revealed hundreds of genes and relevant biological processes related to smoking behaviors. These findings provide new insights for future functional studies on smoking behaviors.

Association of CYP2D6 and CYP2C19 metabolizer status with switching and discontinuing antidepressant drugs: an exploratory study

BACKGROUND

Tailoring antidepressant drugs (AD) to patients' genetic drug-metabolism profile is promising. However, literature regarding associations of ADs' treatment effect and/or side effects with drug metabolizing genes CYP2D6 and CYP2C19 has yielded inconsistent results. Therefore, our aim was to longitudinally investigate associations between CYP2D6 (poor, intermediate, and normal) and CYP2C19 (poor, intermediate, normal, and ultrarapid) metabolizer-status, and switching/discontinuing of ADs. Next, we investigated whether the number of perceived side effects differed between metabolizer statuses.

METHODS

Data came from the multi-site naturalistic longitudinal cohort Netherlands Study of Depression and Anxiety (NESDA). We selected depression- and/or anxiety patients, who used AD at some point in the course of the 9 years follow-up period (n = 928). Medication use was followed to assess patterns of AD switching/discontinuation over time. CYP2D6 and CYP2C19 alleles were derived using genome-wide data of the NESDA samples and haplotype data from the PharmGKB database. Logistic regression analyses were conducted to investigate the association of metabolizer status with switching/discontinuing ADs. Mann-Whitney U-tests were conducted to compare the number of patient-perceived side effects between metabolizer statuses.

RESULTS

No significant associations were observed of CYP metabolizer status with switching/discontinuing ADs, nor with the number of perceived side effects.

CONCLUSIONS

We found no evidence for associations between CYP metabolizer statuses and switching/discontinuing AD, nor with side effects of ADs, suggesting that metabolizer status only plays a limited role in switching/discontinuing ADs. Additional studies with larger numbers of PM and UM patients are needed to further determine the potential added value of pharmacogenetics to guide pharmacotherapy.

Selective Serotonin Reuptake Inhibitors and Other Treatment Modalities for Deep Space Missions

INTRODUCTION: As humankind ventures further into the depths of space, planning is already underway for long-duration exploration missions that will test the bounds of human performance. Deep space travel will include added risk related to stressors from the isolated, confined, and extreme environment that lies outside the boundaries of low Earth orbit. Currently, selective serotonin reuptake inhibitors (SSRIs) are considered the standard of care for many mental health diagnoses, including anxiety and depression; however, SSRIs are also associated with several undesired side effects. The utility of nonpharmacological therapies for the management of behavioral health conditions has not yet been fully explored.METHODS: A comprehensive literature search was performed using PubMed. Relevant articles pertaining to the psychological impacts of isolated, confined, and extreme environments, use of SSRIs in spaceflight, side effects associated with SSRIs, and nonpharmacological treatments for anxiety and depression were reviewed. Over 70 studies were reviewed in total.RESULTS: Reduced bone mineral density, impaired hemostatic function, significant individual variability resulting from gene polymorphisms, and drug-drug interactions are well described adverse effects of SSRIs that may complicate their operational use in the deep space environment. Four alternative therapies for the treatment of anxiety and depression may show promise for long duration missions.DISCUSSION: Although SSRIs have long been considered standard of care treatment for many behavioral health conditions, we cannot trivialize the risk that prolonged pharmacological therapy may pose. The need to mitigate these risks by exploring alternative therapies has never been more relevant.El-Khoury BB, Ray KL, Altchuler SI, Reichard JF, Dukes CH. Selective serotonin reuptake inhibitors and other treatment modalities for deep space missions. Aerosp Med Hum Perform. 2023; 94(11):843-851.

Biomarkers as predictors of treatment response to tricyclic antidepressants in major depressive disorder: A systematic review

Tricyclic antidepressants (TCAs) are frequently prescribed in case of non-response to first-line antidepressants in Major Depressive Disorder (MDD). Treatment of MDD often entails a trial-and-error process of finding a suitable antidepressant and its appropriate dose. Nowadays, a shift is seen towards a more personalized treatment strategy in MDD to increase treatment efficacy. One of these strategies involves the use of biomarkers for the prediction of antidepressant treatment response. We aimed to summarize biomarkers for prediction of TCA specific (i.e. per agent, not for the TCA as a drug class) treatment response in unipolar nonpsychotic MDD. We performed a systematic search in PubMed and MEDLINE. After full-text screening, 36 papers were included. Seven genetic biomarkers were identified for nortriptyline treatment response. For desipramine, we identified two biomarkers; one genetic and one nongenetic. Three nongenetic biomarkers were identified for imipramine. None of these biomarkers were replicated. Quality assessment demonstrated that biomarker studies vary in endpoint definitions and frequently lack power calculations. None of the biomarkers can be confirmed as a predictor for TCA treatment response. Despite the necessity for TCA treatment optimization, biomarker studies reporting drug-specific results for TCAs are limited and adequate replication studies are lacking. Moreover, biomarker studies generally use small sample sizes. To move forward, larger cohorts, pooled data or biomarkers combined with other clinical characteristics should be used to improve predictive power.

Candidate genetic variants and antidepressant-related fall risk in middle-aged and older adults

BACKGROUND

Antidepressant use has been associated with increased fall risk. Antidepressant-related adverse drug reactions (e.g. orthostatic hypotension) depend partly on genetic variation. We hypothesized that candidate genetic polymorphisms are associated with fall risk in older antidepressant users.

METHODS

The association between antidepressant use and falls was cross-sectionally investigated in a cohort of Dutch older adults by logistic regression analyses. In case of significant interaction product term of antidepressant use and candidate polymorphism, the association between the variant genotype and fall risk was assessed within antidepressant users and the association between antidepressant use and fall risk was investigated stratified per genotype. Secondly, a look-up of the candidate genes was performed in an existing genome-wide association study on drug-related falls in antidepressant users within the UK Biobank. In antidepressant users, genetic associations for our candidate polymorphisms for fall history were investigated.

RESULTS

In antidepressant users(n = 566), for rs28371725 (CYP2D6*41) fall risk was decreased in TC/variant allele carriers compared to CC/non-variant allele carriers (OR = 0.45, 95% CI 0.26-0.80). Concerning rs1057910 (CYP2C9*3), fall risk was increased in CA/variant allele carriers compared to AA/non-variant allele carriers (OR = 1.95, 95% CI 1.17-3.27). Regarding, rs1045642 (ABCB1), fall risk was increased in AG/variant allele carriers compared to GG/non-variant allele carriers (OR = 1.69, 95% CI 1.07-2.69). Concerning the ABCB1-haplotype (rs1045642/rs1128503), fall risk was increased in AA-AA/variant allele carriers compared to GG-GG/non-variant allele carriers (OR = 1.86, 95% CI 1.05-3.29). In the UK Biobank, in antidepressant users(n = 34,000) T/variant-allele of rs28371725 (CYP2D*41) was associated with increased fall risk (OR = 1.06, 95% CI 1.01-1.12). G/non-variant-allele of rs4244285 (CY2C19*2) was associated with decreased risk (OR = 0.96, 95% CI 0.92-1.00).

CONCLUSION

This is the first study showing that certain genetic variants modify antidepressant-related fall risk. The results were not always consistent across the studies and should be validated in a study with a prospective design. However, pharmacogenetics might have value in antidepressant (de)prescribing in falls prevention.

Precision medicine in mood disorders

The choice of the most appropriate psychoactive medication for each of our patients is always a challenge. We can use more than 100 psychoactive drugs in the treatment of mood disorders, which can be prescribed either alone or in combination. Response and tolerability problems are common, and much trial and error is often needed before achieving a satisfactory outcome. Precision medicine is therefore needed for tailoring treatment to optimize outcome. Pharmacological, clinical, and demographic factors are important and informative, but biological factors may further inform and refine prediction. Twenty years after the first reports of gene variants modulating antidepressant response, we are now confronted with the prospect of routine clinical pharmacogenetic applications in the treatment of depression. The scientific community is divided into two camps: those who are enthusiastic and those who are skeptical. Although it appears clear that the benefit of existing tools is still not completely defined, at least in the case of central nervous system gene variants, this is not the case for metabolic gene variants, which is generally accepted. Cumulative scores encompassing many variants across the entire genome will soon predict psychiatric disorder liability and outcome. At present, precision medicine in mood disorders may be implemented using clinical and pharmacokinetic factors. In the near future, a genome-wide composite genetic score in conjunction with clinical factors within each patient is the most promising approach for developing a more effective way to target treatment for patients suffering from mood disorders.

Acceptability of Pharmacogenetic Testing among French Psychiatrists, a National Survey

Psychiatric disorder management is based on the prescription of psychotropic drugs. Response to them remains often insufficient and varies from one patient to another. Pharmacogenetics explain part of this variability. Pharmacogenetic testing is likely to optimize the choice of treatment and thus improve patients’ care, even if concerns and limitations persist. This practice of personalized medicine is not very widespread in France. We conducted a national survey to evaluate the acceptability of this tool by psychiatrists and psychiatry residents in France, and to identify factors associated with acceptability and previous use. The analysis included 397 observations. The mean acceptability score was 10.70, on a scale from 4 to 16. Overall acceptability score was considered as low for 3.0% of responders, intermediate for 80.1% and high for 16.9%. After regression, the remaining factors influencing acceptability independently of the others were prescription and training history and theoretical approach. The attitude of our population seems to be rather favorable, however, obvious deficiencies have emerged regarding perceived skills and received training. Concerns about the cost and delays of tests results also emerged. According to our survey, one of the keys to overcoming the barriers encountered in the integration of pharmacogenetics seems to be the improvement of training and the provision of information to practitioners.

Precision psychiatry in clinical practice

The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance criteria is an error-prone process. Recently, pharmacogenetic biomarkers entered in prescribing guidelines, giving clinicians the possibility to use this additional tool to guide prescription and improve therapeutic outcomes. This marked an important step towards precision psychiatry, which aim is to integrate biological and environmental information to personalise treatments. Only genetic variants in cytochrome enzymes are endorsed by prescribing guidelines, but in the future polygenic predictors of treatment outcomes may be translated into the clinic. The integration of genetics with other relevant information (e.g., concomitant diseases and treatments, drug plasma levels) could be managed in a standardised way through ad hoc software. The overcoming of the current obstacles (e.g., staff training, genotyping and informatics facilities) can lead to a broad implementation of precision psychiatry and represent a revolution for psychiatric care.Key pointsPrecision psychiatry aims to integrate biological and environmental information to personalise treatments and complement clinical judgementPharmacogenetic biomarkers in cytochrome genes were included in prescribing guidelines and represented an important step towards precision psychiatryTherapeutic drug monitoring is an important and cost-effective tool which should be integrated with genetic testing and clinical evaluation in order to optimise pharmacotherapyOther individual factors relevant to pharmacotherapy response (e.g., individual's symptom profile, concomitant diseases) can be integrated with genetic information through artificial intelligence to provide treatment recommendationsThe creation of pharmacogenetic services within healthcare systems is a challenging and multi-step process, education of health professionals, promotion by institutions and regulatory bodies, economic and ethical barriers are the main issues.

Pharmacogenomics guided versus standard antidepressant treatment in a community pharmacy setting: A randomized controlled trial

The literature on pharmacogenomics as a tool to support antidepressant precision is burgeoning. Recently, a more active role has been argued for pharmacists in pharmacogenomic testing, with both pharmacists and family physicians perceiving pharmacist‐led testing as a valuable method by which to scale this innovation for depression treatment. In this prospective, single‐blind randomized controlled design, we evaluated the impact of pharmacogenomics guided versus standard antidepressant treatment of depression and anxiety, implemented in three large community pharmacies. Participants were 213 outpatients diagnosed with major depressive disorder and/or generalized anxiety disorder, randomized to receive pharmacogenomics guided (n = 105) or standard antidepressant treatment (n = 108); participants were blinded to the study. Patient reported outcomes of depression, anxiety, disability, and treatment satisfaction were assessed at months 0, 1, 3, and 6. Hypotheses were investigated using mixed effect models on the full data. All clinical outcomes improved significantly. The primary outcome (depression) and two secondary outcomes (generalized anxiety and disability) exhibited significant time by group interactions indicating that they improved for participants who received pharmacogenomics guided treatment more so than they did for participants who received standard treatment. Treatment satisfaction improved similarly for both groups. Results contribute to a growing body of work evaluating the impact of pharmacogenomics testing to inform antidepressant medication treatment for depression and anxiety, and provides important initial evidence for the role of pharmacists in care delivery. Pharmacogenomic testing may be a valuable tool to allow pharmacists to more effectively collaborate in facilitating clinical treatment decisions. ClinicalTrials.gov registration: (NCT03591224).

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.

How to Utilize Clinical and Genetic Information for Personalized Treatment of Major Depressive Disorder: Step by Step Strategic Approach

Depression is the single largest contributor to non-fatal health loss and affects 322 million people globally. The clinical heterogeneity of this disorder shows biological correlates and it makes the personalization of antidepressant prescription an important pillar of treatment. There is increasing evidence of genetic overlap between depression, other psychiatric and non-psychiatric disorders, which varies across depression subtypes. Therefore, the first step of clinical evaluation should include a careful assessment of psychopathology and physical health, not limited to previously diagnosed disorders. In part of the patients indeed the pathogenesis of depression may be strictly linked to inflammatory and metabolic abnormalities, and the treatment should target these as much as the depressive symptoms themselves. When the evaluation of the symptom and drug tolerability profile, the concomitant biochemical abnormalities and physical conditions is not enough and at least one pharmacotherapy failed, the genotyping of variants in CYP2D6/CYP2C19 (cytochromes responsible for antidepressant metabolism) should be considered. Individuals with altered metabolism through one of these enzymes may benefit from some antidepressants rather than others or need dose adjustments. Finally, if available, the polygenic predisposition towards cardio-metabolic disorders can be integrated with non-genetic risk factors to tune the identification of patients who should avoid medications associated with this type of side effects. A sufficient knowledge of the polygenic risk of complex medical and psychiatric conditions is becoming relevant as this information can be obtained through direct-to-consumer genetic tests and in the future it may provided by national health care systems.

Pathways Connecting Late-Life Depression and Dementia

Late-life depression is associated with significant cognitive impairment. Meta-analyses showed that depression is associated with an increased risk for Alzheimer’s disease (AD) and it might be an etiological factor for AD. Since late-life depression is often connected with cognitive impairment and dementia is usually associated with depressive symptoms, a simple diagnostic approach to distinguish between the disorders is challenging. Several overlapping pathophysiological substrates might explain the comorbidity of both syndromes. Firstly, a stress syndrome, i.e., elevated cortisol levels, has been observed in up to 70% of depressed patients and also in AD pathology. Stress conditions can cause hippocampal neuronal damage as well as cognitive impairment. Secondly, the development of a depression and dementia after the onset of vascular diseases, the profile of cerebrovascular risk factors in both disorders and the impairments depending on the location of cerebrovascular lesions, speak in favor of a vascular hypothesis as a common factor for both disorders. Thirdly, neuroinflammatory processes play a key role in the etiology of depression as well as in dementia. Increased activation of microglia, changes in Transforming-Growth-Factor beta1 (TGF-beta1) signaling, production of pro-inflammatory cytokines as well as reduction of anti-inflammatory molecules are examples of common pathways impaired in dementia and depression. Fourthly, the neurotrophin BDNF is highly expressed in the central nervous system, especially in the hippocampus, where it plays a key role in the proliferation, differentiation and the maintenance of neuronal integrity throughout lifespan. It has been associated not only with antidepressant properties but also a reduction of cognitive impairment and therefore could be involved also in AD. Another etiologic factor is amyloid accumulation, as plasma amyloid beta-42 independently predicts both late-onset depression and AD. Higher plasma amyloid beta-42 predicts the development of late onset depression and conversion to possible AD. However, clinical trials with antibodies against beta amyloid recently failed, i.e., Solanezumab, Aducanumab, and Crenezumab. An overproduction of amyloid-beta might simply reflect a form of synaptic plasticity to compensate for neuronal dysfunction in different kind of neurological and psychiatric diseases of multiple etiologies. The tau hypothesis, sex/gender specific differences, epigenetics and the gut microbiota-brain axis imply other potential common pathways connecting late-life depression and dementia. In conclusion, different potential pathophysiological links between dementia and depression highlight several specific synergistic and multifaceted treatment possibilities, depending on the individual risk profile of the patient.

Genetics of Treatment Outcomes in Major Depressive Disorder: Present and Future

Pharmacogenetic testing is a useful and increasingly widespread tool to assist in antidepressant prescription. More than ten antidepressants (including tricyclics, selective serotonin reuptake inhibitors and venlafaxine) have already genetic biomarkers of response/side effects in clinical guidelines and drug labels. These are represented by functional genetic variants in genes coding for cytochrome enzymes (CYP2D6 and CYP2C19). Depending on the predicted metabolic activity, guidelines provide recommendations on drug choice and dosing. Despite not conclusive, the current evidence suggests that testing can be useful in patients who did not respond or tolerate at least one previous pharmacotherapy. However, the current recommendations are based on pharmacokinetic genes only (CYP450 enzymes), while pharmacodynamic genes (modulating antidepressant mechanisms of action in the brain) are still being studied because of their greater complexity. This may be captured by polygenic risk scores, which reflect the cumulative contribution of many genetic variants to a trait, and they may provide future clinical applications of pharmacogenetics. A more extensive use of genotyping in clinical practice may lead to improvement in treatment outcomes thanks to personalized treatments, but possible ethical issues and disparities should be taken into account and prevented.

Pharmacogenetics and Depression: A Critical Perspective

Depression leads the higher personal and socio-economical burden within psychiatric disorders. Despite the fact that over 40 antidepressants (ADs) are available, suboptimal response still poses a major challenge and is thought to be partially a result of genetic variation. Pharmacogenetics studies the effects of genetic variants on treatment outcomes with the aim of providing tailored treatments, thereby maximizing efficacy and tolerability. After two decades of pharmacogenetic research, variants in genes coding for the cytochromes involved in ADs metabolism (CYP2D6 and CYP2C19) are now considered biomarkers with sufficient scientific support for clinical application, despite the lack of conclusive cost/effectiveness evidence. The effect of variants in genes modulating ADs mechanisms of action (pharmacodynamics) is still controversial, because of the much higher complexity of ADs pharmacodynamics compared to ADs metabolism. Considerable progress has been made since the era of candidate gene studies: the genomic revolution has made possible to assess genetic variance on an unprecedented scale, throughout the whole genome, and to analyze the cumulative effect of different variants. The results have revealed key information on the biological mechanisms mediating ADs effect and identified hypothetical new pharmacological targets. They also paved the way for future availability of polygenic pharmacogenetic panels to predict treatment outcome, which are expected to explain much higher variance in ADs response compared to CYP2D6 and CYP2C19 only. As the demand and availability of AD pharmacogenetic testing is projected to increase, it is important for clinicians to keep abreast of this evolving area to facilitate informed discussions with their patients.

Duloxetine plasma level and antidepressant response

BACKGROUND

Major Depressive Disorder (MDD) is associated with a high rate of inadequate treatment response, which is mainly due to the large inter-individual genetic variability in pharmacokinetic and pharmacodynamic targets of antidepressant drugs. Little is still known about the exact association between plasma level of first-line antidepressants and clinical response. This is particularly true for duloxetine, a dual serotonin and norepinephrine reuptake inhibitor recommended as first-line treatment for MDD. The aim of this study was to investigate the association between serum concentration of duloxetine (SCD) and antidepressant response (AR).

METHODS

66 MDD patients treated with duloxetine 60 mg/day monotherapy were recruited in an outpatient setting and followed for three months. Hamilton Depression Rating Scale - 21 (HAMD-21) was administrated at baseline, at month 1, and at month 3 to assess AR. SCD was measured at steady state. Linear regression analysis and nonlinear least-squares regression were used to estimate association between SCD and AR.

RESULTS

SCD showed a high inter-individual variability in our sample, despite the duloxetine fixed oral dosage. We found a strong association between SCD and AR following a bell-shaped function at month 1 and at month 3. Nonetheless, within the recommended SCD range of 30-120 ng/mL a more linear correlation between SCD and AR was observed.

DISCUSSION

Our results suggest that for duloxetine the association between SCD and AR likely follows a bell-shaped quadratic function with poor AR at subtherapeutic SCD and progressive decrease of AR at higher SCD. The maximum antidepressant efficacy seems to require SCD values next to the highest recommended SCD (30-120 ng/mL), probably because of the optimal saturation of both serotonin and norepinephrine transporters. Thus, taking into account the observed high interindividual variability of SCD, our findings suggest that for MDD patients treated with duloxetine, SCD could be a useful tool to guide the treatment by optimizing the oral dosage in order to increase the AR rate.

Effects of Cytochrome P450 2C19 Genetic Polymorphisms on Responses to Escitalopram and Levels of Brain-Derived Neurotrophic Factor in Patients With Panic Disorder

PURPOSE/BACKGROUND

The purpose of this study was to examine the relationships between ytochrome P450 family 2 subfamily C member 19 (CYP2C19) polymorphisms, brain-derived neurotrophic factor (BDNF) plasma levels, and treatment responses to escitalopram in Chinese patients with panic disorder (PD).

METHODS/PROCEDURES

Ninety patients with PD were administered the Panic Disorder Severity Scale-Chinese Version (PDSS-CV) and Hamilton Anxiety Scale (HAMA-14) from baseline to 8 weeks. Escitalopram treatment (10 mg/d) was administered for 8 consecutive weeks. Three CYP2C19 metabolizers, including extensive metabolizers, intermediate metabolizers, and poor metabolizers (PMs), and 5 CYP2C19 genotypes were detected by polymerase chain reaction-genotyping microarray analysis. Baseline plasma BDNF levels were tested using human BDNF enzyme-linked immunosorbent assay kits.

FINDINGS/RESULTS

Our findings showed no significant differences in demographic data, baseline PDSS-CV scores, or HAMA-14 scores between the 3 CYP2C19 metabolizer groups (P's > 0.05). Repeated-measures analysis showed a significant reduction in PDSS-CV (F = 221.49, df = 3, P < 0.001) and HAMA-14 (F = 260.47, df = 3, P < 0.001) scores over 8 weeks in PD patients. In addition, patients with PMs had a greater reduction in HAMA-14 scores (F = 2.14, P = 0.049) than did those with extensive metabolizers and intermediate metabolizers. Moreover, our findings showed that patients with *2/*2 genotypes had a greater reduction in PDSS-CV scores than did those with other genotypes (F = 2.14, df = 12, P = 0.015).

IMPLICATIONS/CONCLUSIONS

Our study provides preliminary evidence of the effects of CYP2C19 PMs on treatment responses to escitalopram in Chinese PD patients, but no significant correlation between treatment responses and BDNF levels was found.

CYP1A2 Genetic Polymorphism Is Associated With Treatment Remission to Antidepressant Venlafaxine in Han Chinese Population

Major depressive disorder (MDD) is a common mental disorder. Venlafaxine (VEN) is used to treat patients with MDD as an antidepressant of serotonin-norepinephrine reuptake inhibitor. In addition, current reports reveal that CYP enzymes mediate its metabolism, thereby affecting the treatment efficacy. The aim of this study was to test whether the genetic polymorphisms of CYP1A2 are associated with remission after VEN treatment for MDD. A total of 175 Han Chinese depressed patients have been recruited to accept a 6-week treatment with VEN. Three single-nucleotide polymorphisms of CYP1A2 were selected from dbSNP and previous literature to compare the allele and genotype frequencies between remitters and nonremitters. The A 17-item Hamilton Depression Scale was used to access the improvement of patients' depressive symptoms from the baseline to endpoint. A logistic regression analysis for remission was conducted. Between remitters and nonremitters, the allele and genotype frequencies of single-nucleotide polymorphism rs2470890 demonstrated significant differences. They still had significant differences between remitters and nonremitters after controlling baseline Hamilton Depression Scale scores, sex, and age in logistic regression. Our results suggest that the single-nucleotide polymorphism rs2470890 of CYP1A2 gene might be associated with treatment remission after VEN treatment in patients with MDD.

Does obtaining CYP2D6 and CYP2C19 pharmacogenetic testing predict antidepressant response or adverse drug reactions?

Treatment non-response and adverse reactions are common in patients receiving antidepressants. Personalizing psychiatric treatment based on pharmacogenetic testing has been proposed to help clinicians guide antidepressant selection and dosing. This systematic literature review assesses the two most robustly studied drug-metabolizing enzymes, CYP2D6 and CYP2C19, and examines whether obtaining CYP2D6 and CYP2C19 testing can be used to predict antidepressant response or adverse drug reactions in order to improve clinical outcomes. In general, literature reviews published prior to 2013 indicated that results have been inconsistent linking CYP2D6 and CYP2C19 to antidepressant treatment outcomes, suggesting that more evidence is required to support the clinical implementation of genotyping to predict outcomes. We thus performed an extensive and systematic literature review, focusing on studies published from 2013 through 2018. Sixteen studies were found to be relevant. The results yielded inconsistent findings, suggesting that CYP2D6 and CYP2C19 testing may predict response in certain individuals, but it remains unclear if this will translate to improved clinical outcomes. Further research is required to determine when pharmacogenetic testing should be utilized and in which populations it is indicated. Randomized, controlled, prospective trials with adequate sample sizes would best clarify whether genotype-guided antidepressant selection will ultimately improve clinical outcomes.

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%).

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.

Pharmacogenetics in Psychiatry

Mental illness represents a major health issue both at the individual and at the socioeconomical level. This is partly due to the current suboptimal treatment options: existing psychotropic medications, including antidepressants, antipsychotics, and mood stabilizers, are effective only in a subset of patients or produce partial response and they are often associated with debilitating side effects that discourage adherence. Pharmacogenetics is the study of how genetic information impacts on drug response/side effects with the goal to provide tailored treatments, thereby maximizing efficacy and tolerability. The first pharmacogenetic studies focused on candidate genes, previously known to be relevant to the pharmacokinetics and pharmacodynamics of psychotropic drugs. Results were mainly inconclusive, but some replicated candidates were identified and included as pharmacogenetic biomarkers in drug labeling and in some commercial kits. With the advent of the genomic revolution, it became possible to study the genetic variation on an unprecedented scale, throughout the whole genome with no need of a priori hypothesis. This may lead to the personalized prescription of existing medications and potentially to the development of innovative ones, thanks to new insights into the genetics of mental illness. Promising findings were obtained, but methods for the generation and analysis of genome-wide and sequencing data are still in evolution. Future pharmacogenetic tests may consist of hundreds/thousands of polymorphisms throughout the genome or selected pathways in order to take into account the complex interactions across variants in a number of genes.

The Present and Future of Precision Medicine in Psychiatry: Focus on Clinical Psychopharmacology of Antidepressants

Precision medicine is a concept which is recently gaining momentum in all branches of medicine. In particular in psychiatry it is greatly needed given the huge societal costs of psychiatric disorders and given the long time needed to observe benefit from treatments and the response variability. The future will be based on biological determinants, however until such an interesting but still futuristic aim will be reached, at present we may only rely on clinical features to guide our individualized prescription which is currently still frequently based on personal opinion and subjective previous experiences. The aim of this review is to offer an overview of the main aspects to take into consideration when prescribing an antidepressant treatment to reach the best precision medicine using clinical information. More than 40 compounds are available for treating depression and a similar amount of compounds for other psychiatric disorders. The process of matching the profile of the patient with all different profiles of available compounds is therefore quite complex. Our everyday prescribing procedure should take into consideration a number of factors such as the knowledge of the profile of available compounds versus the symptomatology profile of the subject, previous efficacy, medical comorbidities, tolerability profile, individual preferences, and family history. While we are waiting more complex algorithms including biological or genetic measures, it is possible to optimize our current prescription practice by using all available information in order to obtain as much as possible an evidence based precision medicine prescription.

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.

Escitalopram plasma levels and antidepressant response

Major Depression Disorder (MDD) has a highly variable treatment response due to the large inter-individual variation in the pharmacokinetics and pharmacodynamics of drug treatments. In detail the correlation between plasma level and efficacy has been much debated. Among first-line drugs for MDD, one of the most used is escitalopram. In the present study we investigated the association between serum concentration of escitalopram (SCE) and antidepressant response (AR). 70 MDD patients treated with escitalopram monotherapy were recruited and followed for three months. Hamilton Depression Rating Scale - 21 (HAMD-21) was administrated at baseline, month 1, and month 3 to assess AR. SCE was measured at steady state. Linear regression analysis and nonlinear least-squares regression were used to estimate association between SCE and AR. We found an association between SCE and AR both at month 1 (p<0.001) and month 3 (p=0.0003), which persists also excluding 3 patients with SCE equal to 0. Interestingly, by excluding patients with SCE < 20ng/mL, i.e. with a SCE lower than the putative therapeutic threshold, these associations disappeared. The curvilinear function AR = a + (SCE-SCE²) explained a higher proportion of variance compared to the linear other models (p<0.001). Our results suggest that for escitalopram the association between SCE and AR likely follows a nearly-asymptotic function, with poor AR at sub-therapeutic SCE and stable AR response at therapeutic SCE. Thus, when a patient reaches the therapeutic SCE range, further increase of escitalopram dosage seems to be useless, although further studies are needed to confirm our findings.

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.

Consensus paper of the WFSBP Task Force on Genetics: Genetics, epigenetics and gene expression markers of major depressive disorder and antidepressant response

Major depressive disorder (MDD) is a heritable disease with a heavy personal and socio-economic burden. Antidepressants of different classes are prescribed to treat MDD, but reliable and reproducible markers of efficacy are not available for clinical use. Further complicating treatment, the diagnosis of MDD is not guided by objective criteria, resulting in the risk of under- or overtreatment. A number of markers of MDD and antidepressant response have been investigated at the genetic, epigenetic, gene expression and protein levels. Polymorphisms in genes involved in antidepressant metabolism (cytochrome P450 isoenzymes), antidepressant transport (ABCB1), glucocorticoid signalling (FKBP5) and serotonin neurotransmission (SLC6A4 and HTR2A) were among those included in the first pharmacogenetic assays that have been tested for clinical applicability. The results of these investigations were encouraging when examining patient-outcome improvement. Furthermore, a nine-serum biomarker panel (including BDNF, cortisol and soluble TNF-α receptor type II) showed good sensitivity and specificity in differentiating between MDD and healthy controls. These first diagnostic and response-predictive tests for MDD provided a source of optimism for future clinical applications. However, such findings should be considered very carefully because their benefit/cost ratio and clinical indications were not clearly demonstrated. Future tests may include combinations of different types of biomarkers and be specific for MDD subtypes or pathological dimensions.

Efficacy and Tolerability of High-Dose Escitalopram in Posttraumatic Stress Disorder

BACKGROUND

Open-label trials suggest that escitalopram (up to 20 mg/d) is an effective treatment for some, but not all posttraumatic stress disorder (PTSD) patients. Higher doses of escitalopram effectively reduced major depression symptoms in patients who had not responded to regular doses. The current study examines the efficacy, tolerability, and adherence to high-dose escitalopram in PTSD.

METHODS

Forty-five PTSD patients received 12 weeks of gradually increasing doses of escitalopram reaching 40 mg daily at 4 weeks. Among those, 12 participants received regular doses of antidepressants at study onset including escitalopram (n = 7). The Clinician-Administered PTSD Scale (CAPS) evaluated PTSD symptoms severity before treatment, at 3 months (upon treatment termination), and at 6 months (maintenance effect). A 20% reduction in CAPS scores was deemed clinically significant.

RESULTS

Adverse events and medication adherence were monitored at each clinical session. Linear mixed-models analysis showed a significant reduction of mean CAPS scores (11.5 ± 18.1 points) at 3 months and maintenance of gains by 6 months (F2,34.56 = 8.15, P = 0.001). Eleven participants (34.3%) showed clinically significant improvement at 3 months. Only 9 participants (20%) left the study. There were no serious adverse events and few mild ones with only 2 adverse events (diarrhea, 11.1%; drowsiness, 11.1%) reported by more than 10% of participants.

CONCLUSION

High doses of escitalopram are tolerable and well adhered to in PTSD. Their beneficial effect at a group level is due to a particularly good response in a subset of patients.Variability in prior pharmacological treatment precludes a definite attribution of the results to high doses of escitalopram.

Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6

MOTIVATION

Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery.

RESULTS

We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

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.

Serotonin transporter gene: a new polymorphism may affect response to antidepressant treatments in major depressive disorder

BACKGROUND AND OBJECTIVES

Several gene variants have been related to major depressive disorder (MDD) treatment outcomes; however, few studies have investigated a possible different effect on pharmacotherapy and brief psychotherapy response.

METHODS

A total of 137 MDD patients were randomized to either interpersonal counseling (IPC; n = 40) or antidepressant pharmacological treatment (n = 97). Outcomes were remission, response, and symptom improvement at week 8. Five genetic variants were investigated (5HTR2A rs6314, BDNF rs6265, SLC6A4 rs8076005, CREB1 rs2253206, and TPH2 rs11179023) as possible modulators of outcomes.

RESULTS

The LC6A4 rs8076005 AA genotype and A allele were associated with response rate in the antidepressant group (p = 0.015 and 0.005, respectively) and in the whole sample (p = 0.03 and 0.02, respectively). In the IPC group a non-significant trend in the same direction was observed. The TPH2 rs11179023 A allele showed a marginal association with symptom improvement in the IPC group only. Other gene variants did not impact on outcomes in any treatment group.

CONCLUSION

Our study suggests that rs8076005 in the SLC6A4 gene may be a modulator of antidepressant response, especially when pharmacological treatment is used.

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.

Understanding the pharmacogenetics of selective serotonin reuptake inhibitors

INTRODUCTION

The genetic background of antidepressant response represents a unique opportunity to identify biological markers of treatment outcome. Encouraging results alternating with inconsistent findings made antidepressant pharmacogenetics a stimulating but often discouraging field that requires careful discussion about cumulative evidence and methodological issues.

AREAS COVERED

The present review discusses both known and less replicated genes that have been implicated in selective serotonin reuptake inhibitors (SSRIs) efficacy and side effects. Candidate genes studies and genome-wide association studies (GWAS) were collected through MEDLINE database search (articles published till January 2014). Further, GWAS signals localized in promising genetic regions according to candidate gene studies are reported in order to assess the general comparability of results obtained through these two types of pharmacogenetic studies. Finally, a pathway enrichment approach is applied to the top genes (those harboring SNPs with p < 0.0001) outlined by previous GWAS in order to identify possible molecular mechanisms involved in SSRI effect.

EXPERT OPINION

In order to improve the understanding of SSRI pharmacogenetics, the present review discusses the proposal of moving from the analysis of individual polymorphisms to genes and molecular pathways, and from the separation across different methodological approaches to their combination. Efforts in this direction are justified by the recent evidence of a favorable cost-utility of gene-guided antidepressant treatment.

Are there meaningful biomarkers of treatment response for depression?

During the past decades, the prevalence of affective disorders has been on the rise globally, with only one out of three patients achieving remission in acute treatment with antidepressants. The identification of physiological markers that predict treatment course proves useful in increasing therapeutic success. On the basis of well-documented, recent findings in depression research, we highlight and discuss the most promising biomarkers for antidepressant therapy response. These include genetic variants and gene expression profiles, proteomic and metabolomic markers, neuroendocrine function tests, electrophysiology and imaging techniques. Ultimately, this review proposes an integrative use of biomarkers for antidepressant treatment outcome.

From pharmacogenetics to pharmacogenomics: the way toward the personalization of antidepressant treatment

OBJECTIVE

Major depressive disorder is the most common psychiatric disorder, worldwide, yet response and remission rates are still unsatisfactory. The identification of genetic predictors of antidepressant (AD) response could provide a promising opportunity to improve current AD efficacy through the personalization of treatment. The major steps and findings along this path are reviewed together with their clinical implications and limitations.

METHOD

We systematically reviewed the literature through MEDLINE and Embase database searches, using any word combination of "antidepressant," "gene," "polymorphism," "pharmacogenetics," "genome-wide association study," "GWAS," "response," and "adverse drug reactions." Experimental works and reviews published until March 2012 were collected and compared.

RESULTS

Numerous genes pertaining to several functional systems were associated with AD response. The more robust findings were found for the following genes: solute carrier family 6 (neurotransmitter transporter), member 4; serotonin receptor 1A and 2A; brain-derived neurotrophic factor; and catechol-O-methyltransferase. Genome-wide association studies (GWASs) provided many top markers, even if none of them reached genome-wide significance.

CONCLUSIONS

AD pharmacogenetics have not produced any knowledge applicable to routine clinical practice yet, as results were mainly inconsistent across studies. Despite this, the rising awareness about methodological deficits of past studies could allow for the identication of more suitable strategies, such as the integration of the GWAS approach with the candidate gene approach, and innovative methodologies, such as pathway analysis and study of depressive endophenotypes.

Clinical features and drug induced side effects in early versus late antidepressant responders

Early antidepressant response (2nd week) has been reported as the result of a true antidepressant effect and a predictor of subsequent stable response. With the purpose to study the clinical profile of early response/remission (2nd week) compared to late response/remission (4th-6th weeks), two independent major depressive disorder (MDD) samples (the Sequenced Treatment Alternatives to Relieve Depression or STAR*D n=1922 and an Italian sample n=171) were investigated. Patients were treated with citalopram in the STAR*D while in a naturalistic setting in the Italian sample. Depressive symptomatology was assessed by the Hamilton Depressive Rating Scale weekly in the Italian sample and biweekly by the Quick Inventory of Depressive Symptomatology Clinician Rated in the STAR*D. Logistic regression was used to investigate possible predictors of early response and the Bonferroni correction was applied. In the STAR*D, higher levels of baseline core depressive symptoms (Bech subscale) were associated with early response (p=0.00017), as well as lower baseline insomnia (p=0.003) and higher work and social functioning (p=0.001). In the Italian sample none of these variables were associated with the phenotype, but a non significant trend of lower baseline quality of life (p=0.078) was observed in late remitters. In the STAR*D late responders reported higher levels of antidepressant induced side effects, especially difficulty in sleeping (p=5.68e-13), with a non significant trend in the same direction in the Italian sample (p=0.09). The identification of late versus early antidepressant responders at the beginning of the treatment may be useful to guide therapeutic choices in clinical settings.

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.

Effect of glial cell line-derived neurotrophic factor on behavior and key members of the brain serotonin system in mouse strains genetically predisposed to behavioral disorders

The effect of glial cell line-derived neurotrophic factor (GDNF) on behavior and on the serotonin (5-HT) system of a mouse strain predisposed to depressive-like behavior, ASC/Icg (Antidepressant Sensitive Cataleptics), in comparison with the parental "nondepressive" CBA/Lac mice was studied. Within 7 days after acute administration, GDNF (800 ng, i.c.v.) decreased cataleptic immobility but increased depressive-like behavioral traits in both investigated mouse strains and produced anxiolytic effects in ASC mice. The expression of the gene encoding the key enzyme for 5-HT biosynthesis in the brain, tryptophan hydroxylase-2 (Tph-2), and 5-HT1A receptor gene in the midbrain as well as 5-HT2A receptor gene in the frontal cortex were increased in GDNF-treated ASC mice. At the same time, GDNF decreased 5-HT1A and 5-HT2A receptor gene expression in the hippocampus of ASC mice. GDNF failed to change Tph2, 5-HT1A , or 5-HT2A receptor mRNA levels in CBA mice as well as 5-HT transporter gene expression and 5-HT1A and 5-HT2A receptor functional activity in both investigated mouse strains. The results show 1) a GDNF-induced increase in the expression of key genes of the brain 5-HT system, Tph2, 5-HT1A , and 5-HT2A receptors, and 2) significant genotype-dependent differences in the 5-HT system response to GDNF treatment. The data suggest that genetically defined cross-talk between neurotrophic factors and the brain 5-HT system underlies the variability in behavioral response to GDNF.

Pharmacogenetics of antidepressant drugs: an update after almost 20 years of research

Major depressive disorder (MDD) is an emergent cause of personal and socio-economic burden, both for the high prevalence of the disorder and the unsatisfying response rate of the available antidepressant treatments. No reliable predictor of treatment efficacy and tolerance in the single patient is available, thus drug choice is based on a trial and error principle with poor clinical efficiency. Among modulators of treatment outcome, genetic polymorphisms are thought to explain a significant share of the inter-individual variability. The present review collected the main pharmacogenetic findings primarily about antidepressant response and secondly about antidepressant induced side effects, and discussed the main strengths and limits of both candidate and genome-wide association studies and the most promising methodological opportunities and challenges of the field. Despite clinical applications of antidepressant pharmacogenetics are not available yet, previous findings suggest that genotyping may be applied in the clinical practice. In order to reach this objective, further rigorous pharmacogenetic studies (adequate sample size, study of better defined clinical subtypes of MDD, adequate covering of the genetic variability), their combination with the results obtained through complementary methodologies (e.g., pathway analysis, epigenetics, transcriptomics, and proteomics), and finally cost-effectiveness trials are required.

The impact of Cytochrome P450 CYP1A2, CYP2C9, CYP2C19 and CYP2D6 genes on suicide attempt and suicide risk-a European multicentre study on treatment-resistant major depressive disorder

Recently published data have reported associations between cytochrome P450 metabolizer status and suicidality. The aim of our study was to investigate the role of genetic polymorphisms of the cytochrome P450 genes on suicide risk and/or a personal history of suicide attempts. Two hundred forty-three major depressive disorder patients were collected in the context of a European multicentre resistant depression study and treated with antidepressants at adequate doses for at least 4 weeks. Suicidality was assessed using the Mini International Neuropsychiatric Interview and the Hamilton Rating Scale for Depression (HAM-D). Treatment response was defined as HAM-D ≤ 17 and remission as HAM-D ≤ 7 after 4 weeks of treatment with antidepressants at adequate dose. Genotyping was performed for all relevant variations of the CYP1A2 gene (*1A, *1F, *1C, *1 J, *1 K), the CYP2C9 gene (*2, *3), the CYP2C19 gene (*2, *17) and the CYP2D6 gene (*3, *4, *5, *6, *9, *19, *XN). No association between both suicide risk and personal history of suicide attempts, and the above mentioned metabolic profiles were found after multiple testing corrections. In conclusion, the investigated cytochrome gene polymorphisms do not seem to be associated with suicide risk and/or a personal history of suicide attempts, though methodological and sample size limitations do not allow definitive conclusions.

Advances in molecular modeling of human cytochrome P450 polymorphism

Cytochrome P450 (CYP) is a supergene family of metabolizing enzymes involved in the phase I metabolism of drugs and endogenous compounds. CYP oxidation often leads to inactive drug metabolites or to highly toxic or carcinogenic metabolites involved in adverse drug reactions (ADR). During the last decade, the impact of CYP polymorphism in various drug responses and ADR has been demonstrated. Of the drugs involved in ADR, 56% are metabolized by polymorphic phase I metabolizing enzymes, 86% among them being CYP. Here, we review the major CYP polymorphic forms, their impact for drug response and current advances in molecular modeling of CYP polymorphism. We focus on recent studies exploring CYP polymorphism performed by the use of sequence-based and/or protein-structure-based computational approaches. The importance of understanding the molecular mechanisms related to CYP polymorphism and drug response at the atomic level is outlined.

Cytochrome P450-mediated drug metabolism in the brain

Cytochrome P450 enzymes (CYPs) metabolize many drugs that act on the central nervous system (CNS), such as antidepressants and antipsychotics; drugs of abuse; endogenous neurochemicals, such as serotonin and dopamine; neurotoxins; and carcinogens. This takes place primarily in the liver, but metabolism can also occur in extrahepatic organs, including the brain. This is important for CNS-acting drugs, as variation in brain CYP-mediated metabolism may be a contributing factor when plasma levels do not predict drug response. This review summarizes the characterization of CYPs in the brain, using examples from the CYP2 subfamily, and discusses sources of variation in brain CYP levels and metabolism. Some recent experiments are described that demonstrate how changes in brain CYP metabolism can influence drug response, toxicity and drug-induced behaviours. Advancing knowledge of brain CYP-mediated metabolism may help us understand why patients respond differently to drugs used in psychiatry and predict risk for psychiatric disorders, including neurodegenerative diseases and substance abuse.