Cytochrome P450 2D6 genotype affects the pharmacokinetics of controlled-release paroxetine in healthy Chinese subjects: comparison of traditional phenotype and activity score systems

Effectiveness and tolerability in people with narcolepsy transitioning from sodium oxybate to low-sodium oxybate: Data from the real-world TENOR study

OBJECTIVES

The Transition Experience of persons with Narcolepsy taking Oxybate in the Real-world (TENOR) study was conducted to provide real-world insight into the experience of people with narcolepsy switching from sodium oxybate (SXB) to low-sodium oxybate (LXB; 92% less sodium than SXB).

METHODS

TENOR is a patient-centric, prospective, observational, virtual-format study. Participants were adults with narcolepsy (type 1 or 2) who were transitioning from SXB to LXB treatment (±7 days from LXB initiation). Effectiveness and tolerability data were collected online from baseline (taking SXB) through 21 weeks (taking LXB) via daily and weekly diaries and questionnaires, including the Epworth Sleepiness Scale (ESS), the Functional Outcomes of Sleep Questionnaire, short version (FOSQ-10), and the British Columbia Cognitive Complaints Inventory (BC-CCI).

RESULTS

TENOR participants (N = 85) were 73% female with a mean (SD) age of 40.3 (13.0) years. Mean (SD) ESS scores decreased numerically throughout the transition from SXB to LXB (baseline: 9.9 [5.2]; week 21: 7.5 [4.7]), with 59.5% and 75.0% of participants having scores in the normal range (≤10) at baseline and week 21, respectively. Mean (SD) FOSQ-10 scores (baseline: 14.4 [3.4]; week 21: 15.2 [3.2]) and BC-CCI scores (baseline: 6.1 [4.4]; week 21: 5.0 [4.3]) also remained stable. The most common symptoms related to tolerability reported by participants at baseline were sleep inertia, hyperhidrosis, and dizziness (45.2%, 40.5%, and 27.4%, respectively), which decreased in prevalence by week 21 (33.8%, 13.2%, and 8.8%, respectively).

CONCLUSIONS

Findings from TENOR confirm maintenance of effectiveness and tolerability when transitioning from SXB to LXB treatment.

Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim^®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUC_last ratios and 9/12, 5/5 and 7/7 of DGI C_max ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUC_ss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.

The Influence of CYP2D6 and CYP2C19 Genetic Variation on Diabetes Mellitus Risk in People Taking Antidepressants and Antipsychotics

CYP2D6 and CYP2C19 enzymes are essential in the metabolism of antidepressants and antipsychotics. Genetic variation in these genes may increase risk of adverse drug reactions. Antidepressants and antipsychotics have previously been associated with risk of diabetes. We examined whether individual genetic differences in CYP2D6 and CYP2C19 contribute to these effects. We identified 31,579 individuals taking antidepressants and 2699 taking antipsychotics within UK Biobank. Participants were classified as poor, intermediate, or normal metabolizers of CYP2D6, and as poor, intermediate, normal, rapid, or ultra-rapid metabolizers of CYP2C19. Risk of diabetes mellitus represented by HbA1c level was examined in relation to the metabolic phenotypes. CYP2D6 poor metabolizers taking paroxetine had higher Hb1Ac than normal metabolizers (mean difference: 2.29 mmol/mol; p < 0.001). Among participants with diabetes who were taking venlafaxine, CYP2D6 poor metabolizers had higher HbA1c levels compared to normal metabolizers (mean differences: 10.15 mmol/mol; p < 0.001. Among participants with diabetes who were taking fluoxetine, CYP2D6 intermediate metabolizers and decreased HbA1c, compared to normal metabolizers (mean difference -7.74 mmol/mol; p = 0.017). We did not observe any relationship between CYP2D6 or CYP2C19 metabolic status and HbA1c levels in participants taking antipsychotic medication. Our results indicate that the impact of genetic variation in CYP2D6 differs depending on diabetes status. Although our findings support existing clinical guidelines, further research is essential to inform pharmacogenetic testing for people taking antidepressants and antipsychotics.

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.

No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study

Background

The use of pharmacogenomics data is increasing in clinical practice. However, it is unknown if pharmacogenomics data can be used more broadly to predict outcomes like hospitalization or emergency department (ED) visit. We aim to determine the association between selected pharmacogenomics phenotypes and hospital utilization outcomes (hospitalization and ED visits).

Methods

This cohort study utilized 10,078 patients from the Mayo Clinic Biobank in the RIGHT protocol with sequence and interpreted phenotypes for 10 selected pharmacogenes including CYP2D6, CYP2C9, CYP2C19, CYP3A5, HLA B 5701, HLA B 5702, HLA B 5801, TPMT, SLCO1B1, and DPYD. The primary outcome was hospitalization with ED visits as a secondary outcome. We used Cox proportional hazards model to test the association between each pharmacogenomics phenotype and the risk of the outcomes.

Results

During the follow-up period (median [in years] = 7.3), 13% (n=1354) and 8% (n=813) of the subjects experienced hospitalization and ED visits, respectively. Compared to subjects who did not experience hospitalization, hospitalized patients were older (median age [in years]: 67 vs 65), poorer self-rated health (15% vs 4.7% for fair/poor), and higher disease burden (median number of chronic conditions: 7 vs 4) at baseline. There was no association of hospitalization with any of the pharmacogenomics phenotypes. The pharmacogenomics phenotypes were not associated with disease burden, a well-established risk factor for hospital utilization outcomes. Similar findings were observed for patients with ED visits during the follow-up period.

Conclusion

We found no association of 10 well-established pharmacogenomics phenotypes with either hospitalization or ED visits in this relatively large biobank population and outside the context of specific drug use related to these genes. Traditional risk factors for hospitalization like age and self-rated health were much more likely to predict hospitalization and/or ED visits than this pharmacogenomics information.

A genome-wide association study of tramadol metabolism from post-mortem samples

Phase I tramadol metabolism requires cytochrome p450 family 2, subfamily D, polypeptide 6 (CYP2D6) to form O-desmethyltramadol (M1). CYP2D6 genetic variants may infer metabolizer phenotype; however, drug ADME (absorption, distribution, metabolism, and excretion) and response depend on protein pathway(s), not CYP2D6 alone. There is a paucity of data regarding the contribution of trans-acting proteins to idiosyncratic phenotypes following drug exposure. A genome-wide association study identified five markers (rs79983226/kgp11274252, rs9384825, rs62435418/kgp10370907, rs72732317/kgp3743668, and rs184199168/exm1592932) associated with the conversion of tramadol to M1 (M1:T). These SNPs reside within five genes previously implicated with adverse reactions. Analysis of accompanying toxicological meta-data revealed a significant positive linear relationship between M1:T and degree of sample polypharmacy. Taken together, these data identify candidate loci for potential clinical inferences of phenotype following exposure to tramadol and highlight sample polypharmacy as a possible diagnostic covariate in post-mortem genetic studies.

Influence of the cytochrome P450 2D6 *10/*10 genotype on the pharmacokinetics of paroxetine in Japanese patients with major depressive disorder: a population pharmacokinetic analysis

OBJECTIVE

Although the reduced function of the cytochrome P450 2D6*10 (CYP2D6*10) allele is common among Asian populations, existing evidence does not support paroxetine therapy adjustments for patients who have the CYP2D6*10 allele. In this study, we attempted to evaluate the degree of the impact of different CYP2D6 genotypes on the pharmacokinetic (PK) variability of paroxetine in a Japanese population using a population PK approach.

METHODS

This retrospective study included 179 Japanese patients with major depressive disorder who were being treated with paroxetine. CYP2D6*1, *2, *5, *10, and *41 polymorphisms were observed. A total of 306 steady-state concentrations for paroxetine were collected from the patients. A nonlinear mixed-effects model identified the apparent Michaelis-Menten constant (Km) and the maximum velocity (Vmax) of paroxetine; the covariates included CYP2D6 genotypes, patient age, body weight, sex, and daily paroxetine dose.

RESULTS

The allele frequencies of CYP2D6*1, *2, *5, *10, and *41 were 39.4, 14.5, 4.5, 41.1, and 0.6%, respectively. There was no poor metabolizer who had two nonfunctional CYP2D6*5 alleles. A one-compartment model showed that the apparent Km value was decreased by 20.6% in patients with the CYP2D6*10/*10 genotype in comparison with the other CYP2D6 genotypes. Female sex also influenced the apparent Km values. No PK parameters were affected by the presence of one CYP2D6*5 allele.

CONCLUSION

Unexpectedly, elimination was accelerated in individuals with the CYP2D6*10/*10 genotype. Our results show that the presence of one CYP2D6*5 allele or that of any CYP2D6*10 allele may have no major effect on paroxetine PKs in the steady state.