Impact of CYP2C19 genotype on sertraline exposure in 1200 Scandinavian patients

Machine Learning-Based Prediction of Escitalopram and Sertraline Side Effects With Pharmacokinetic Data in Children and Adolescents

Selective serotonin reuptake inhibitors (SSRI) are the first-line pharmacologic treatment for anxiety and depressive disorders in children and adolescents. Many patients experience side effects that are difficult to predict, are associated with significant morbidity, and can lead to treatment discontinuation. Variation in SSRI pharmacokinetics could explain differences in treatment outcomes, but this is often overlooked as a contributing factor to SSRI tolerability. This study evaluated data from 288 escitalopram-treated and 255 sertraline-treated patients ≤ 18 years old to develop machine learning models to predict side effects using electronic health record data and Bayesian estimated pharmacokinetic parameters. Trained on a combined cohort of escitalopram- and sertraline-treated patients, a penalized logistic regression model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% confidence interval (CI): 0.66-0.88), with 0.69 sensitivity (95% CI: 0.54-0.86), and 0.82 specificity (95% CI: 0.72-0.87). Medication exposure, clearance, and time since the last dose increase were among the top features. Individual escitalopram and sertraline models yielded an AUROC of 0.73 (95% CI: 0.65-0.81) and 0.64 (95% CI: 0.55-0.73), respectively. Post hoc analysis showed sertraline-treated patients with activation side effects had slower clearance (P = 0.01), which attenuated after accounting for age (P = 0.055). These findings raise the possibility that a machine learning approach leveraging pharmacokinetic data can predict escitalopram- and sertraline-related side effects. Clinicians may consider differences in medication pharmacokinetics, especially during dose titration and as opposed to relying on dose, when managing side effects. With further validation, application of this model to predict side effects may enhance SSRI precision dosing strategies in youth.

Next-generation sequencing in pharmacogenomics - fit for clinical decision support?

INTRODUCTION

The technological advances of sequencing methods during the past 20 years have fuelled the generation of large amounts of sequencing data that comprise common variations, as well as millions of rare and personal variants that would not be identified by conventional genotyping. While comprehensive sequencing is technically feasible, its clinical utility for guiding personalized treatment decisions remains controversial.

AREAS COVERED

We discuss the opportunities and challenges of comprehensive sequencing compared to targeted genotyping for pharmacogenomic applications. Current pharmacogenomic sequencing panels are heterogeneous and clinical actionability of the included genes is not a major focus. We provide a current overview and critical discussion of how current studies utilize sequencing data either retrospectively from biobanks, databases or repurposed diagnostic sequencing, or prospectively using pharmacogenomic sequencing.

EXPERT OPINION

While sequencing-based pharmacogenomics has provided important insights into genetic variations underlying the safety and efficacy of a multitude pharmacological treatments, important hurdles for the clinical implementation of pharmacogenomic sequencing remain. We identify gaps in the interpretation of pharmacogenetic variants, technical challenges pertaining to complex loci and variant phasing, as well as unclear cost-effectiveness and incomplete reimbursement. It is critical to address these challenges in order to realize the promising prospects of pharmacogenomic sequencing.

Artificial intelligence-based model for dose prediction of sertraline in adolescents: a real-world study

BACKGROUND

Variability exists in sertraline pharmacokinetic parameters in individuals, especially obvious in adolescents. We aimed to establish an individualized dosing model of sertraline for adolescents with depression based on artificial intelligence (AI) techniques.

METHODS

Data were collected from 258 adolescent patients treated at the First Hospital of Hebei Medical University between December 2019 to July 2022. Nine different algorithms were used for modeling to compare the prediction abilities on sertraline daily dose, including XGBoost, LGBM, CatBoost, GBDT, SVM, ANN, TabNet, KNN, and DT. Performance of four dose subgroups (50 mg, 100 mg, 150 mg, and 200 mg) were analyzed.

RESULTS

CatBoost was chosen to establish the individualized medication model with the best performance. Six important variables were found to be correlated with sertraline dose, including plasma concentration, PLT, MPV, GL, A/G, and LDH. The ROC curve and confusion matrix exhibited the good prediction performance of CatBoost model in four dose subgroups (the AUC of 50 mg, 100 mg, 150 mg, and 200 mg were 0.93, 0.81, 0.93, and 0.93, respectively).

CONCLUSION

The AI-based dose prediction model of sertraline in adolescents with depression had a good prediction ability, which provides guidance for clinicians to propose the optimal medication regimen.

Escitalopram and Sertraline Population Pharmacokinetic Analysis in Pediatric Patients

BACKGROUND AND OBJECTIVE

Escitalopram and sertraline are commonly prescribed for anxiety and depressive disorders in children and adolescents. The pharmacokinetics (PK) of these medications have been evaluated in adults and demonstrate extensive variability, but studies in pediatric patients are limited. Therefore, we performed a population PK analysis for escitalopram and sertraline in children and adolescents to characterize the effects of demographic, clinical, and pharmacogenetic factors on drug exposure.

METHODS

A PK dataset was generated by extracting data from the electronic health record and opportunistic sampling of escitalopram- and sertraline-treated psychiatrically hospitalized pediatric patients aged 5-18 years. A population PK analysis of escitalopram and sertraline was performed using NONMEM. Concentration-time profiles were simulated using MwPharm++ to evaluate how covariates included in the final models influence medication exposure and compared to adult therapeutic reference ranges.

RESULTS

The final escitalopram cohort consisted of 315 samples from 288 patients, and the sertraline cohort consisted of 265 samples from 255 patients. A one-compartment model with a proportional residual error model best described the data for both medications. For escitalopram, CYP2C19 phenotype and concomitant CYP2C19 inhibitors affected apparent clearance (CL/F), and normalizing CL/F and apparent volume of distribution (V/F) to body surface area (BSA) improved estimations. The final escitalopram model estimated CL/F and V/F at 14.2 L/h/1.73 m2 and 428 L/1.73 m2, respectively. For sertraline, CYP2C19 phenotype and concomitant CYP2C19 inhibitors influenced CL/F, and empirical allometric scaling of patient body weight on CL/F and V/F was significant. The final sertraline model estimated CL/F and V/F at 124 L/h/70 kg and 4320 L/70 kg, respectively. Normalized trough concentrations (Ctrough) for CYP2C19 poor metabolizers taking escitalopram were 3.98-fold higher compared to normal metabolizers (151.1 ng/mL vs 38.0 ng/mL, p < 0.0001), and normalized Ctrough for CYP2C19 poor metabolizers taking sertraline were 3.23-fold higher compared to normal, rapid, and ultrarapid metabolizers combined (121.7 ng/mL vs 37.68 ng/mL, p < 0.0001). Escitalopram- and sertraline-treated poor metabolizers may benefit from a dose reduction of 50-75% and 25-50%, respectively, to normalize exposure to other phenotypes.

CONCLUSION

To our knowledge, this is the largest population PK analysis of escitalopram and sertraline in pediatric patients. Significant PK variability for both medications was observed and was largely explained by CYP2C19 phenotype. Slower CYP2C19 metabolizers taking escitalopram or sertraline may benefit from dose reductions given increased exposure.

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.

Case report: application of pharmacogenetics in the personalized treatment of an elderly patient with a major depressive episode

Background

Pharmacogenetic analyses can predict interpersonal differences in response to psychopharmacotherapy, which greatly facilitates the selection of the most effective medication at optimal doses. By personalizing therapy in this way, we can minimize adverse drug reactions (ADR) and prevent polypharmacy. Most psychotropic medications are metabolized by the cytochrome P450 enzymes CYP2D6, CYP2C19, and CYPA3A4, which influence drug metabolism and concentration, affecting both efficacy and the occurrence of ADR. The relationships between genetic variations and enzymatic activity allow pharmacogenetic analysis to provide important data for optimal drug selection. The following case report illustrates the impact of pharmacogenetic analysis on the course of pharmacologic treatment in an elderly patient with a major depressive episode.

Methods

We present a case of a 79-year-old patient treated for severe depression with psychotic symptoms. We collected data on treatment selection and response to treatment before and after pharmacogenetic analysis. For pharmacogenetic analysis, common functional variants in CYP1A2, CYP3A4, CYP2B6, CYP2C19, and CYP2D6 were genotyped, and corresponding evidence-based treatment recommendations were prepared.

Results

The patient suffered from lack of efficacy and serious ADR of several medications, resulting in worsening depression and treatment resistance over the course of several months of treatment. Pharmacogenetic analysis provided important insights into the patient's pharmacokinetic phenotype and allowed us to personalize treatment and achieve remission of the depressive episode.

Conclusion

In the case presented, we have shown how consideration of pharmacogenetic characteristics in an individual patient can improve treatment outcome and patient well-being. Knowledge of the patient's pharmacogenetic characteristics helped us to personalize treatment, resulting in complete remission of psychopathology. Due to the complexity of psychiatric disorders, the efficacy of combinations of different medications, which are often required in individual patients, cannot be clearly explained. Therefore, it is of great importance to conduct further pharmacokinetic and pharmacogenetic studies to better assess gene-drug interactions in psychopharmacotherapy.

Effect of CYP2C19 polymorphisms on antidepressant prescription patterns and treatment emergent mania in bipolar disorder

Antidepressant medication is used extensively to treat bipolar depression despite uncertain efficacy. The cytochrome P450 (CYP) 2C19 enzyme metabolize several antidepressants, and polymorphisms in the corresponding gene CYP2C19 influence plasma concentration and hence treatment outcomes in major depressive disorder. Here, we investigate if CYP2C19 polymorphisms are associated with antidepressant treatment patterns and the risk of mania when antidepressants are used in bipolar disorder. Two single nucleotide polymorphisms (rs4244285 and rs12248560) were used to classify 5019 bipolar disorder patients into CYP2C19 metabolic phenotypes ranging from poor to ultra-rapid metabolizers. We used Swedish national registry data 2005-2017 on dispensed medications and inpatient care to estimate risks for early-treatment persistence, treatment discontinuation, switching to a new antidepressant medication, and mania within 3 months of treatment initiation in patients treated with citalopram, escitalopram, sertraline, amitriptyline, and clomipramine. Metabolic phenotypes of CYP2C19 were not robustly associated with the investigated treatment outcomes based on dispense patterns. Slower metabolism was associated with an increased risk of treatment emergent mania for sertraline (hazard ratio [HR] = 1.3, 95% CI = 1.04-1.62, p = 0.02) and the tricyclic antidepressants amitriptyline and clomipramine (HR = 1.46, 95% CI = 1.05-2.02, p = 0.024). In a large study of the impact of CYP2C19 metabolic phenotypes on antidepressant treatment of bipolar depression, we found an association between slower CYP2C19 metabolism and higher risk of treatment emergent mania, which is a step towards personalized risk assessments. There were, however, no clear associations with early treatment persistence, treatment discontinuation, and switching to a new antidepressant.

Precision Medicine in Antidepressants Treatment

Precision medicine uses innovative approaches to improve disease prevention and treatment outcomes by taking into account people's genetic backgrounds, environments, and lifestyles. Treatment of depression is particularly challenging, given that 30-50% of patients do not respond adequately to antidepressants, while those who respond may experience unpleasant adverse drug reactions (ADRs) that decrease their quality of life and compliance. This chapter aims to present the available scientific data that focus on the impact of genetic variants on the efficacy and toxicity of antidepressants. We compiled data from candidate gene and genome-wide association studies that investigated associations between pharmacodynamic and pharmacokinetic genes and response to antidepressants regarding symptom improvement and ADRs. We also summarized the existing pharmacogenetic-based treatment guidelines for antidepressants, used to guide the selection of the right antidepressant and its dose based on the patient's genetic profile, aiming to achieve maximum efficacy and minimum toxicity. Finally, we reviewed the clinical implementation of pharmacogenomics studies focusing on patients on antidepressants. The available data demonstrate that precision medicine can increase the efficacy of antidepressants and reduce the occurrence of ADRs and ultimately improve patients' quality of life.

Pharmacogenetic Testing and Therapeutic Drug Monitoring Of Sertraline at a Residential Treatment Center for Children and Adolescents: A Pilot Study

Background: Sertraline is commonly prescribed to children for the treatment of anxiety and major depressive disorder and is metabolized in part by CYP2C19. While dosing recommendations based on CYP2C19 genotype exist, there is sparse data in children on the relationship between sertraline concentrations and CYP2C19 genotype. Additionally, although rarely utilized in the United States, therapeutic drug monitoring can also help to guide dosing. The primary objective of this pilot study was to compare sertraline concentrations with CYP2C19 genotype. Secondary objectives included exploring the feasibility of using pharmacogenetic testing and therapeutic drug monitoring in a residential treatment center for children and adolescents. Methods: This study was a prospective, open-label study of children prescribed sertraline being treated at a residential treatment center for children and adolescents. Individuals were included if they were < 18 years of age, taking sertraline for at least 2 weeks allowing them to reach steady-state concentrations, being treated through the residential treatment program, and able to understand and speak English. Results: A total of 20 participants (80% female) completed all study procedures, including pharmacogenetic testing and therapeutic drug monitoring, with an average age of 15.4 years (range: 9-17 years). Forty percent (n=8) of participants had a diagnosis of Generalized Anxiety Disorder, while 30% (n=6) had a diagnosis of Major Depressive Disorder. Overall, average sertraline and desmethylsertraline concentrations were 21.1 ng/ml (range: 1-78 ng/ml) and 52.4 ng/ml (range: 1-258 n/ml). Based on CYP2C19 genotypes, 60% (n=12) were normal metabolizers, 10% (n=2) were intermediate metabolizers, and 30% (n=6) were rapid metabolizers. Daily sertraline dose (mg/day) accounted for a significant amount of the observed variability in sertraline (p<0.0001; r2=0.62) and desmethylsertraline concentrations (p<0.001; r2=0.45). When comparing weight-based dosing by sertraline and desmethylsertraline concentrations, sertraline daily dose by weight (mg/kg/day) also accounted for a significant amount of the observed variability in sertraline (p<0.0001; r2=0.60) and desmethylsertraline (p<0.0001; r2=0.59) concentrations. Average daily and weight-based doses for CYP2C19 intermediate, normal, and rapid metabolizers were 75 mg/day, 87.5 mg/day, and 79.2 mg/day and 1.5 mg/kg/day, 1.3 mg/kg/day, and 1.1 mg/kg/day, though these were not significantly different. Conclusion: This small, pilot study showed sertraline dose to be significantly associated with sertraline and desmethylsertraline concentrations. No differences were noted between CYP2C19 metabolizer groups, likely due to the limited sample size. These results also suggest that ordering pharmacogenetic testing and therapeutic drug monitoring in the setting of a child and adolescent residential treatment center is feasible.

Pharmacogenetic Dose Modeling Based on CYP2C19 Allelic Phenotypes

Pharmacogenetic variability in drug metabolism leads to patient vulnerability to side effects and to therapeutic failure. Our purpose was to introduce a systematic statistical methodology to estimate quantitative dose adjustments based on pharmacokinetic differences in pharmacogenetic subgroups, addressing the concerns of sparse data, incomplete information on phenotypic groups, and heterogeneity of study design. Data on psychotropic drugs metabolized by the cytochrome P450 enzyme CYP2C19 were used as a case study. CYP2C19 activity scores were estimated, while statistically assessing the influence of methodological differences between studies, and used to estimate dose adjustments in genotypic groups. Modeling effects of activity scores in each substance as a population led to prudential predictions of adjustments when few data were available ('shrinkage'). The best results were obtained with the regularized horseshoe, an innovative Bayesian approach to estimate coefficients viewed as a sample from two populations. This approach was compared to modeling the population of substance as normally distributed, to a more traditional "fixed effects" approach, and to dose adjustments based on weighted means, as in current practice. Modeling strategies were able to assess the influence of study parameters and deliver adjustment levels when necessary, extrapolated to all phenotype groups, as well as their level of uncertainty. In addition, the horseshoe reacted sensitively to small study sizes, and provided conservative estimates of required adjustments.

Pediatric considerations for pharmacogenetic selective serotonin reuptake inhibitors clinical decision support

Pharmacogenetic testing for psychiatry is growing at a rapid pace, with multiple sites utilizing results to help clinical decision-making. Genotype-guided dosing and drug selection have been implemented at several sites, including Vanderbilt University Medical Center, where clinical decision support (CDS) based on pharmacogenetic results went live for selective serotonin reuptake inhibitors in 2020 for both adult and pediatric patients. Effective and appropriate implementation of CYP2D6- and CYP2C19-guided CDS for the pediatric population requires consideration of the evidence for the pharmacogenetic associations, medication indications, and appropriate alternative therapies to be used when a pharmacogenetic contraindication is identified. In this article, we review these pediatric pharmacogenetic considerations for selective serotonin reuptake inhibitor CDS. We include a case study, the current literature supporting clinical recommendations, considerations when designing pediatric CDS, future implications, and examples of sertraline, (es)citalopram, paroxetine, and fluvoxamine alerts.

Impact of the novel CYP2C:TG haplotype and CYP2B6 variants on sertraline exposure in a large patient population

Sertraline is a commonly used SSRI antidepressant drug, metabolized by CYP2C19 and CYP2B6, that exhibits a substantial interindividual variation in clinical response, of which only a part can be attributed to known genetic variants. In the current study we have examined the role of a newly discovered ultrarapid CYP2C:TG haplotype and CYP2B6 variants in order to identify the possible missing heritability for such variation in sertraline response in a large patient population (n = 840). Compared to the reference group (CYP2C19*1/*1, n = 160), sertraline exposure was increased by 128% in CYP2C19 PMs (n = 29, p < 0.001) but decreased by about 20% in CYP2C19 ultrarapid metabolizers (Ums) (homozygous carriers of CYP2C19*17 and/or CYP2C:TG haplotype) with the diplotypes CYP2C19*17/*17, CYP2C:TG/TG, or CYP2C19*17/CYP2C:TG (n = 135, p < 0.003, p = 0.022, p < 0.003, respectively). Interestingly, in patients carrying the increased function CYP2B6*4 allele, and also carrying the CYP2C19*17 and CYP2C:TG alleles (n = 10), sertraline exposure was 35.4% lower compared to the reference group, whereas in subjects being poor metabolizers (PM) in both the CYP2C19 and CYP2B6 gene, the sertraline concentrations were raised by 189%. In summary, the CYP2C19 variants including the CYP2C:TG haplotype had a significant impact on sertraline metabolism, as well as the CYP2B6*4, *6, and *9 alleles. Knowing the CYP2B6 and CYP2C19 genotype, including the CYP2C:TG haplotype status, can prospectively be useful to clinicians in making more appropriate sertraline dosing decisions.

Pharmacokinetic-pharmacodynamic modeling analysis for hydroxysafflor yellow A-calycosin in compatibility in normal and cerebral ischemic rats: A comparative study

OBJECTIVE

Astragalus and Safflower are commonly used in the treatment of stroke. Studies have shown that their two active components, hydroxysafflor yellow A (HSYA) and calycosin (CA), have protective effects on cerebral ischemia-reperfusion injury (I/R). However, the pharmacokinetic-pharmacodynamic (PK-PD) modeling study of the combination of the two components has not been reported in rats. The study aimed to perform combined PK-PD modeling of HSYA and CA in normal and cerebral ischemia model rats to explain quantitatively their time-concentration-effect relationship.

METHODS

To make the middle cerebral artery occlusion (MCAO) model. SD rats were randomly divided into normal treated group (NTG) (n = 6), model group (MDG) (n = 6) and model treated group (MTG) (n = 6). Plasma was collected from the mandibular vein after 0, 2, 5, 10, 15, 20, 30, 45, 60, 75, 90, 120, 180, and 240 min after intravenous administration. Rats in NTG and MTG were administered the same dose of HSYA (5 mg/kg) and CA (8 mg/kg) by tail vein injection. HPLC-VWD method was used for detection and analysis. Simultaneously, ELISA was performed to detect the levels of IL-1β and caspase-9 in rat plasma at different time points. The improvement in the above indicators was compared after administration. Lastly, after combining the pharmacokinetic parameters and pharmacodynamic indicators in vivo, DAS 3.2.6 software was used to fit the PK-PD model.

RESULTS

The MCAO model was successfully established. Compared to NTG, there was a significant difference (P < 0.05) in t_1/2α, t_1/2β, V₁, V₂, CL₁, CL₂, AUC_(0-t), AUC _(0-∞), and K₁₂ of MTG for HSYA, and there was a significant difference (P < 0.05) in t_1/2α, V₁, CL₁, AUC_(0-t), AUC _(0-∞), and K₁₀ of MTG for CA_. Compared to NTG, the PK parameters of t_1/2α, V₁, V₂, CL₁, and K₁₀ were higher for HSYA in MTG, while AUC_(0-t), AUC _(0-∞), K₁₂, and K₂₁ were lower; the PK parameters of t_1/2α, V₁, V₂, AUC_(0-t), and AUC_(0-∞) were higher for CA in MTG, while CL₁, CL₂, K₁₀, K₁₂, and K₂₁ were lower. Also, the results of PD showed extremely significant differences in the levels of caspase-9 and IL-1β at the different time points in MTG (P < 0.01) compared with 0 min. The levels of caspase-9 and IL-1β in NTG rats showed little fluctuation and were relatively stable; however, their levels in MTG showed a downward trend with time. There were highly significant differences in the levels of each of the pharmacodynamic indicators at every time point between NTG and MTG (P < 0.01).

CONCLUSION

The PK-PD model of the combined administration of HSYA and CA was successfully established in rats, and the differences in pharmacodynamic and pharmacokinetic properties between the normal and cerebral ischemic rats were evaluated. Based on comprehensive data analysis, we found that the combination of HSYA and CA may exert protective effects against I/R injury in rats via anti-apoptotic and anti-inflammatory pathways. The study provided additional insights into the development of drugs for ischemic stroke as well as the design of appropriate dosing regimens.

CYP2C19 slow metabolizer phenotype is associated with lower antidepressant efficacy and tolerability

The inter-individual variability in CYP2C19-mediated metabolism may affect the antidepressant treatment. The aim of this study is to evaluate differences in antidepressant efficacy and tolerability between different CYP2C19 metabolizer categories in inpatients suffering from major depressive disorder. The cohort was divided into experimental groups based on CYP2C19 genotype and it contained 24 slow (SMs), 41 normal (NMs), and 37 fast metabolizers (FMs). Efficacy and tolerability were assessed at baseline, and after two and four weeks as a follow-up. The primary efficacy measurement was the change from baseline in Hamilton's Depression Rating Scale (HAMD), while the primary tolerability measurement was the Toronto Side-Effects Scale (TSES) intensity scores at the last visit. The reduction in HAMD score was 36% less pronounced and response rate was exceedingly less prevalent (75% lower) in SMs, compared with NMs. The TSES intensity score was increased in SMs, compared with NMs, by 43% for central nervous system and by 22% for gastrointestinal adverse drug reactions. No significant differences in measured parameters were observed between NMs and FMs. Compared with NM and RM, lower antidepressant efficacy and tolerability was observed in SMs; this association is likely connected with the lower SM capacity to metabolize antidepressant drugs.

Therapeutic drug monitoring of sertraline in children and adolescents: A naturalistic study with insights into the clinical response and treatment of obsessive-compulsive disorder

BACKGROUND

Sertraline is a selective serotonin reuptake inhibitor with specific indications in child and adolescent psychiatry. Notwithstanding its frequent use and clinical benefits, the relationship between pharmacokinetics, pharmacodynamics, efficacy, and tolerability of sertraline across indications, particularly in non-adult patients, is not fully understood.

METHOD

This naturalistic therapeutic drug monitoring (TDM) study was conducted in a transdiagnostic sample of children and adolescents treated with sertraline (n = 78; mean age, 14.22 ± 2.39; range, 7-18 years) within the prospective multicenter "TDM-VIGIL" project. Associations between dose, serum concentration, and medication-specific therapeutic and side effects based on the Clinical Global Impression scale were examined. Tolerability was measured qualitatively with the 56-item Pediatric Adverse Event Rating Scale.

RESULTS

A strong linear positive dose-serum concentration relationship (with dose explaining 45% of the variance in concentration) and significant effects of weight and co-medication were found. Neither dose nor serum concentration were associated with side effects. An overall mild-to-moderate tolerability profile of sertraline was observed. In contrast with the transdiagnostic analysis that did not indicate an effect of concentration, when split into depression (MDD) and obsessive-compulsive disorder (OCD) diagnoses, the probability of clinical improvement significantly increased as both dose and concentration increased for OCD, but not for MDD.

CONCLUSIONS

This TDM-flexible-dose study revealed a significant diagnosis-specific effect between sertraline serum concentration and clinical efficacy for pediatric OCD. While TDM already guides clinical decision-making regarding compliance, dose calibration, and drug-drug interactions, combining TDM with other methods, such as pharmacogenetics, may facilitate a personalized medicine approach in psychiatry.

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.

The Future of Personalized Medicine in Space: From Observations to Countermeasures

The aim of personalized medicine is to detach from a “one-size fits all approach” and improve patient health by individualization to achieve the best outcomes in disease prevention, diagnosis and treatment. Technological advances in sequencing, improved knowledge of omics, integration with bioinformatics and new in vitro testing formats, have enabled personalized medicine to become a reality. Individual variation in response to environmental factors can affect susceptibility to disease and response to treatments. Space travel exposes humans to environmental stressors that lead to physiological adaptations, from altered cell behavior to abnormal tissue responses, including immune system impairment. In the context of human space flight research, human health studies have shown a significant inter-individual variability in response to space analogue conditions. A substantial degree of variability has been noticed in response to medications (from both an efficacy and toxicity perspective) as well as in susceptibility to damage from radiation exposure and in physiological changes such as loss of bone mineral density and muscle mass in response to deconditioning. At present, personalized medicine for astronauts is limited. With the advent of longer duration missions beyond low Earth orbit, it is imperative that space agencies adopt a personalized strategy for each astronaut, starting from pre-emptive personalized pre-clinical approaches through to individualized countermeasures to minimize harmful physiological changes and find targeted treatment for disease. Advances in space medicine can also be translated to terrestrial applications, and vice versa. This review places the astronaut at the center of personalized medicine, will appraise existing evidence and future preclinical tools as well as clinical, ethical and legal considerations for future space travel.

Genophenotypic Factors and Pharmacogenomics in Adverse Drug Reactions

Adverse drug reactions (ADRs) rank as one of the top 10 leading causes of death and illness in developed countries. ADRs show differential features depending upon genotype, age, sex, race, pathology, drug category, route of administration, and drug–drug interactions. Pharmacogenomics (PGx) provides the physician effective clues for optimizing drug efficacy and safety in major problems of health such as cardiovascular disease and associated disorders, cancer and brain disorders. Important aspects to be considered are also the impact of immunopharmacogenomics in cutaneous ADRs as well as the influence of genomic factors associated with COVID-19 and vaccination strategies. Major limitations for the routine use of PGx procedures for ADRs prevention are the lack of education and training in physicians and pharmacists, poor characterization of drug-related PGx, unspecific biomarkers of drug efficacy and toxicity, cost-effectiveness, administrative problems in health organizations, and insufficient regulation for the generalized use of PGx in the clinical setting. The implementation of PGx requires: (i) education of physicians and all other parties involved in the use and benefits of PGx; (ii) prospective studies to demonstrate the benefits of PGx genotyping; (iii) standardization of PGx procedures and development of clinical guidelines; (iv) NGS and microarrays to cover genes with high PGx potential; and (v) new regulations for PGx-related drug development and PGx drug labelling.

Understanding genetic risk factors for common side effects of antidepressant medications

Background

Major depression is one of the most disabling health conditions internationally. In recent years, new generation antidepressant medicines have become very widely prescribed. While these medicines are efficacious, side effects are common and frequently result in discontinuation of treatment. Compared with specific pharmacological properties of the different medications, the relevance of individual vulnerability is understudied.

Methods

We used data from the Australian Genetics of Depression Study to gain insights into the aetiology and genetic risk factors to antidepressant side effects. To this end, we employed structural equation modelling, polygenic risk scoring and regressions.

Results

Here we show that participants reporting a specific side effect for one antidepressant are more likely to report the same side effect for other antidepressants, suggesting the presence of shared individual or pharmacological factors. Polygenic risk scores (PRS) for depression associated with side effects that overlapped with depressive symptoms, including suicidality and anxiety. Body Mass Index PRS are strongly associated with weight gain from all medications. PRS for headaches are associated with headaches from sertraline. Insomnia PRS show some evidence of predicting insomnia from amitriptyline and escitalopram.

Conclusions

Our results suggest a set of common factors underlying the risk for antidepressant side effects. These factors seem to be partly explained by genetic liability related to depression severity and the nature of the side effect. Future studies on the genetic aetiology of side effects will enable insights into their underlying mechanisms and the possibility of risk stratification and prophylaxis strategies.

Antidepressants are commonly prescribed medications, but adverse side effects are cause for treatment discontinuation. We analysed data from a large group of adults who have taken antidepressants to understand why some people experience specific side effects. Our results suggest that a person’s genetic characteristics play a role. For example, participants genetically predisposed to a higher body mass index were more likely to report weight gain from antidepressants. These results open up the possibility of predicting adverse side effects as we increase our knowledge on the genetics of related complex traits. Future studies can focus on performing large-scale genetic studies of antidepressant side effects to gain further insights into the mechanisms underlying antidepressant side effects and to identify genetic markers of side effects that could be used in the clinic.

Campos et al. study the genetic aetiology of antidepressant side effects. Using data from the Australian Genetics of Depression study, the authors show that polygenic risk scores for traits such as BMI, insomnia and headaches have a shared genetic basis with side effects to commonly used antidepressant drugs.

Therapy response prediction in major depressive disorder: current and novel genomic markers influencing pharmacokinetics and pharmacodynamics

Major depressive disorder is connected with high rates of functional disability and mortality. About a third of the patients are at risk of therapy failure. Several pharmacogenetic markers especially located in CYP450 genes such as CYP2D6 or CYP2C19 are of relevance for therapy outcome prediction in major depressive disorder but a further optimization of predictive tools is warranted. The article summarizes the current knowledge on pharmacogenetic variants, therapy effects and side effects of important antidepressive therapeutics, and sheds light on new methodological approaches for therapy response estimation based on genetic markers with relevance for pharmacokinetics, pharmacodynamics and disease pathology identified in genome-wide association study analyses, highlighting polygenic risk score analysis as a tool for further optimization of individualized therapy outcome prediction.

Pharmacogenomics in the era of next generation sequencing - from byte to bedside

Pharmacogenetic research has resulted in the identification of a multitude of genetic variants that impact drug response or toxicity. These polymorphisms are mostly common and have been included as actionable information in the labels of numerous drugs. In addition to common variants, recent advances in Next Generation Sequencing (NGS) technologies have resulted in the identification of a plethora of rare and population-specific pharmacogenetic variations with unclear functional consequences that are not accessible by conventional forward genetics strategies. In this review, we discuss how comprehensive sequencing information can be translated into personalized pharmacogenomic advice in the age of NGS. Specifically, we provide an update of the functional impacts of rare pharmacogenetic variability and how this information can be leveraged to improve pharmacogenetic guidance. Furthermore, we critically discuss the current status of implementation of pharmacogenetic testing across drug development and layers of care. We identify major gaps and provide perspectives on how these can be minimized to optimize the utilization of NGS data for personalized clinical decision-support.

The application of precision dosing in the use of sertraline throughout pregnancy for poor and ultrarapid metabolizer CYP 2C19 subjects: A virtual clinical trial pharmacokinetics study

Sertraline is known to undergo changes in pharmacokinetics during pregnancy. CYP 2C19 has been implicated in the interindividual variation in clinical effect associated with sertraline activity. However, knowledge of suitable dose titrations during pregnancy and within CYP 2C19 phenotypes is lacking. A pharmacokinetic modeling virtual clinical trials approach was implemented to: (i) assess gestational changes in sertraline trough plasma concentrations for CYP 2C19 phenotypes, and (ii) identify appropriate dose titration strategies to stabilize sertraline levels within a defined therapeutic range throughout gestation. Sertraline trough plasma concentrations decreased throughout gestation, with maternal volume expansion and reduction in plasma albumin being identified as possible causative reasons. All CYP 2C19 phenotypes required a dose increase throughout gestation. For extensive metabolizer (EM) and ultrarapid metabolizer (UM) phenotypes, doses of 100-150 mg daily are required throughout gestation. For poor metabolizers (PM), 50 mg daily during trimester 1 followed by a dose of 100 mg daily in trimesters 2 and 3 are required.

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

IMPORTANCE

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

OBJECTIVE

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

DATA SOURCES

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

STUDY SELECTION

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

DATA EXTRACTION AND SYNTHESIS

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

MAIN OUTCOMES AND MEASURES

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

RESULTS

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

CONCLUSIONS AND RELEVANCE

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

Validation of a quick and simple chromatographic method for simultaneous quantification of sertraline, escitalopram, risperidone and paliperidone levels in the human plasma

Simultaneous quantification of multiple psychiatric drugs is important for the therapeutic drug monitoring of psychiatric patients. In addition, it would be highly advantageous if the method could be simple, straightforward, and not time-consuming. A 200 µl plasma sample was deproteinized, drugs were separated by a ZORBAX Eclipse XDB-Phenyl column with the mobile phase composed of acetonitrile and 0.1 % formic acid in water (60:40, v/v), and recorded in the MRM mode by using a positive electrospray source with tandem mass spectrometry detection. The dynamic range was 2-256 ng/ml for all the analyzed drugs, except escitalopram (8-256 ng/ml). Quality control samples were prepared in quintuplicates in three relevant concentrations for each drug. Coefficients of determination (R2 ) were higher than 0.99, while the relative difference between nominal and measured concentrations (RE) and CV were lower than 15% for all targets. High performance liquid chromatography coupled with the mass detector (HPLC-MS/MS) method for simultaneous determination of sertraline, escitalopram, risperidone and paliperidone in human plasma was validated with respect to selectivity, linearity, accuracy, precision, matrix effect and stability. This method has significant advantages in terms of low sample volume (200 µl), short preparation time (3 hours) and short runtime per sample (4 minutes).

Review and Consensus on Pharmacogenomic Testing in Psychiatry

The implementation of pharmacogenomic (PGx) testing in psychiatry remains modest, in part due to divergent perceptions of the quality and completeness of the evidence base and diverse perspectives on the clinical utility of PGx testing among psychiatrists and other healthcare providers. Recognizing the current lack of consensus within the field, the International Society of Psychiatric Genetics assembled a group of experts to conduct a narrative synthesis of the PGx literature, prescribing guidelines, and product labels related to psychotropic medications as well as the key considerations and limitations related to the use of PGx testing in psychiatry. The group concluded that to inform medication selection and dosing of several commonly-used antidepressant and antipsychotic medications, current published evidence, prescribing guidelines, and product labels support the use of PGx testing for 2 cytochrome P450 genes (CYP2D6, CYP2C19). In addition, the evidence supports testing for human leukocyte antigen genes when using the mood stabilizers carbamazepine (HLA-A and HLA-B), oxcarbazepine (HLA-B), and phenytoin (CYP2C9, HLA-B). For valproate, screening for variants in certain genes (POLG, OTC, CSP1) is recommended when a mitochondrial disorder or a urea cycle disorder is suspected. Although barriers to implementing PGx testing remain to be fully resolved, the current trajectory of discovery and innovation in the field suggests these barriers will be overcome and testing will become an important tool in psychiatry.

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

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