CYP2C19 Phenotype Research Articles

CYP2C19 genotypes and osteoporotic fractures in long-term users of proton pump inhibitors: A hospital-based study.

Proton pump inhibitors (PPIs) are commonly prescribed medications. The existing data suggest that individuals at a high risk of fractures have been exposed to high doses of PPIs for prolonged durations. CYP2C19 plays a pivotal role in metabolism of PPIs and thereby influences their pharmacokinetic profile. Hence, we hypothesize that CYP2C19 genotypes may be associated with fragility fracture among PPIs users due to PPI exposure. This study aimed to investigate the association between CYP2C19 genotypes, bone mineral density (BMD), and osteoporotic fracture in a hospital-based population. This retrospective cohort study enrolled patients who were prescribed long-term PPIs at Taichung Veterans General Hospital using data extracted from the Taiwan Precision Medicine Initiative between January 2010 and April 2021. Associations between CYP2C19 phenotypes, comorbidities, and fractures in PPI users were analyzed. We enrolled 1518 long-term PPI users; 571 (38%), 727 (48%), and 220 (14%) CYP2C19 normal metabolizers (NMs), intermediate metabolizers (IMs), and poor metabolizers (PMs), respectively. Among them, 49 (3.2%) patients developed fractures within the 1-year follow-up period; 20 (3.5%) fractures in NMs, 24 (3.3%) in IMs, and 5 (2.3%) in PMs, respectively. No significant difference was observed among CYP2C19 genotypes and fracture. Additionally, BMD measurements during the 1-year follow-up period were made available among 75 participants. No significant difference in BMD between CYP2C19 PMs and non-PMs was found. This real-world, hospital-based study concludes that CYP2C19 PMs/IMs are not associated with an increased risk for fractures or reduced BMD in individuals on long-term PPI therapy.

Escitalopram Personalized Dosing: A Population Pharmacokinetics Repository Method.

Escitalopram (SCIT) represents a first-line antidepressant and antianxiety medication. Pharmacokinetic studies of SCIT have demonstrated considerable interindividual variability, emphasizing the need for personalized dosing. Accordingly, we aimed to create a repository of parametric population pharmacokinetic (PPK) models of SCIT to facilitate model-informed precision dosing. In November 2022, we searched PubMed, Embase, and Web of Science for published PPK models and identified eight models. All the structural models reported in the literature were either one- or two-compartment models. In order to investigate the variances in model performance, the parameters of all PPK models were derived from the literature published. A representative virtual population, characterized by an age of 30, a body weight of 70 kg, and a BMI of 23 kg/m2, was generated for the purpose of replicating these models. To accomplish this, the rxode2 package in the R programming language was employed. Subsequently, we compared simulated concentration-time profiles and evaluated the impact of covariates on clearance. The most significant covariates were CYP2C19 phenotype, weight, and age, indicating that dosing regimens should be tailored accordingly. Additionally, among Chinese psychiatric patients, SCIT showed nearly double the exposure compared to other populations, specifically when considering the same CYP2C19 population restriction, which is a knowledge gap that needs further investigation. Furthermore, this repository of parametric PPK models for SCIT has a wide range of potential applications, like design miss or delay dose remedy strategies and external PPK model validation.

Pharmacogenetics and phenoconversion: the influence on side effects experienced by psychiatric patients.

Introduction: Preventing side effects is important to ensure optimal psychopharmacotherapy and therapeutic adherence among psychiatric patients. Obtaining the pharmacogenetic profile of CYP2C19 and CYP2D6 can play an important role in this. When the genotype-predicted phenotype shifts because of the use of co-medication, this is called phenoconversion. The aim was to study the influence of the pharmacogenetic (PGx) profile and phenoconversion on side effects experienced by psychiatric patients. Methods: A retrospective cohort study was performed using data from 117 patients from a psychiatric outpatient clinic. Patients were genotyped with a psychiatric PGx panel and side effects were evaluated using the Udvalg for Kliniske Undersølgelser side effects rating scale (UKU). Results: Of all patients, 10.3% and 9.4% underwent phenoconversion (any shift in predicted phenotype) for CYP2C19 and CYP2D6 respectively. No significant associations were found between the phenotype and UKU-score. 75% of the patients with an Intermediate metabolizer (IM) or Poor metabolizer (PM) phenoconverted phenotype of CYP2C19 experienced nausea and vomiting compared to 9.1% of the Normal metabolizer (NM) and Ultrarapid metabolizer (UM) patients (p = 0.033). 64% of the patients with an IM or PM phenoconverted phenotype of CYP2D6 experienced the side effect depression compared to 30.4% NMs and UMs (p = 0.020). CYP2D6 IM and PM patients had a higher concentration-dose ratio than NM patients (p < 0.05). Discussion: This study underlines the importance to consider phenoconversion when looking at a patient's genotype. This is important for a better prediction of the phenotype and preventing possible side effects under a specific psychopharmacotherapy.

Pharmacogenomic implications of the differential distribution of CYP2C9 metabolic phenotypes among Latin American populations.

The CYP2C9 gene encodes the major drug metabolism enzyme CYP2C9. This gene is highly polymorphic, and no-function (CYP2C9*3) plus decreased function (CYP2C9*2, *5, *8 and *11) star alleles (haplotypes) are commonly used to predict CYP2C9 metabolic phenotypes. This study explores the pharmacogenomic implications of the differential distribution of genotype-predicted CYP2C9 phenotypes across Latin American populations. Data from 1,404 individuals from the South American countries Brazil, Colombia and Peru, from Puerto Rico in the Caribbean and from persons with Mexican ancestry living in North America were analysed. The results showed that the distribution of CYP2C9 alleles and diplotypes, and diplotype-predicted CYP2C9 phenotypes vary significantly across the distinct country cohorts, as well as among self-identified White, Brown and Black Brazilians. Differences in average proportions of biogeographical ancestry across the study groups, especially Native American and African ancestry, are the likely explanation for these results. The differential distribution of genotype-predicted CYP2C9 phenotypes has potentially clinically-relevant pharmacogenomic implications, through its influence on the proportion of individuals at high risk for adverse response to medications that are CYP2C9 substrates, the proportion on individuals with CPIC therapeutic recommendations for dosing and choice of nonsteroidal antinflammatory drugs (NSAIDs) and the number of individuals that need to be genotyped in order to prevent adverse effects of NSAIDs. Collectively, these findings are likely to impact the perceived benefits, cost-effectiveness and clinical adoption of pharmacogenomic screening for drugs that are predominantly metabolized by CYP2C9.

Physiologically Based Pharmacokinetic Modeling and Simulation of Mavacamten Exposure with Drug-Drug Interactions from CYP Inducers and Inhibitors by CYP2C19 Phenotype.

Mavacamten is a first-in-class, oral, selective, allosteric, reversible cardiac myosin inhibitor approved by the US Food and Drug Administration for the treatment of adults with symptomatic New York Heart Association functional class II-III obstructive hypertrophic cardiomyopathy. Mavacamten is metabolized in the liver, predominantly via cytochrome P450 (CYP) enzymes CYP2C19 (74%), CYP3A4 (18%), and CYP2C9 (8%). A physiologically-based pharmacokinetic (PBPK) model was developed using Simcyp version 19 (Certara, Princeton, NJ). Following model verification, the PBPK model was used to explore the effects of strong CYP3A4 and CYP2C19 inducers, and strong, moderate, and weak CYP2C19 and CYP3A4 inhibitors on mavacamten pharmacokinetics (PK) in a healthy population, with the effect of CYP2C19 phenotype predicted for poor, intermediate, normal, and ultrarapid metabolizers. The PBPK model met the acceptance criteria for all verification simulations (> 80% of model-predicted PK parameters within 2-fold of those observed clinically). A weak induction effect was predicted when mavacamten was administered with a strong CYP3A4 inducer in poor metabolizers. Moderate reductions in mavacamten exposure were predicted with a strong CYP2C19/CYP3A4 inducer in all CYP2C19 phenotypes. Except for the effect of strong CYP2C19 inhibitors on ultrarapid metabolizers, steady-state area under plasma concentration-time curve and maximum plasma concentration values were weakly affected (< 2-fold) or not affected (< 1.25-fold), regardless of CYP2C19 phenotype. In conclusion, a fit-for-purpose PBPK model was developed and verified, which accurately predicted the available clinical data and was used to simulate the potential impact of CYP induction and inhibition on mavacamten PKs, stratified by CYP2C19 phenotype.

Drug-Drug Interaction Potential of Mavacamten with Oral Contraceptives: Results from a Clinical Pharmacokinetic Study and a Physiologically Based Pharmacokinetic Model.

Mavacamten is a potential inducer of cytochrome P450 (CYP) 3A4 and, as such, could reduce the exposure of the active components of oral contraceptives, ethinyl estradiol (EE) and norethindrone (NOR), where CYP3A4 is involved in metabolism. This study assessed if repeat doses of mavacamten led to a drug-drug interaction with EE and/or NOR. This was an open-label study in healthy women. In Period 1, participants received 35mcg of EE and 1 mg of NOR. In Period 2, participants received oral loading doses of mavacamten 25 mg on Days 1-2, 15 mg/day on Days 3-17, and 35 mcg of EE and 1 mg of NOR on Day 15. Plasma concentrations of mavacamten, EE, and NOR were obtained before dosing and up to 72 hours after dosing. For EE only, a physiologically based pharmacokinetic model was used to simulate mavacamten-mediated CYP3A4 induction with EE for various CYP2C19 phenotypes. In total, 13 women were enrolled (mean age, 38.9 [standard deviation, 9.65] years). After mavacamten administration, modest increases in area under the concentration-time curves were observed for both EE and NOR. The maximum concentrations and half-lives for EE and NOR were not affected by coadministration with mavacamten. Criteria for bioequivalence were met or nearly met for EE and NOR exposure with geometric mean ratios between 0.8 and 1.25. All adverse events were mild. The physiologically based pharmacokinetic model predicted a less than 15% decrease in EE exposure across CYP2C19 phenotypes. Coadministration of mavacamten at a therapeutically relevant dose with EE and NOR did not decrease the exposure to either EE or NOR to a level that may lead to reduced effectiveness.

The impact of CYP2C19 genotype on phenoconversion by concomitant medication

Introduction: Pharmacogenetics-informed drug prescribing is increasingly applied in clinical practice. Typically, drug metabolizing phenotypes are determined based on genetic test results, whereupon dosage or drugs are adjusted. Drug-drug-interactions (DDIs) caused by concomitant medication can however cause mismatches between predicted and observed phenotypes (phenoconversion). Here we investigated the impact of CYP2C19 genotype on the outcome of CYP2C19-dependent DDIs in human liver microsomes.Methods: Liver samples from 40 patients were included, and genotyped for CYP2C19*2, *3 and *17 variants. S-mephenytoin metabolism in microsomal fractions was used as proxy for CYP2C19 activity, and concordance between genotype-predicted and observed CYP2C19 phenotype was examined. Individual microsomes were subsequently co-exposed to fluvoxamine, voriconazole, omeprazole or pantoprazole to simulate DDIs.Results: Maximal CYP2C19 activity (Vmax) in genotype-predicted intermediate metabolizers (IMs; *1/*2 or *2/*17), rapid metabolizers (RMs; *1/*17) and ultrarapid metabolizers (UMs; *17/*17) was not different from Vmax of predicted normal metabolizers (NMs; *1/*1). Conversely, CYP2C19*2/*2 genotyped-donors exhibited Vmax rates ∼9% of NMs, confirming the genotype-predicted poor metabolizer (PM) phenotype. Categorizing CYP2C19 activity, we found a 40% concordance between genetically-predicted CYP2C19 phenotypes and measured phenotypes, indicating substantial phenoconversion. Eight patients (20%) exhibited CYP2C19 IM/PM phenotypes that were not predicted by their CYP2C19 genotype, of which six could be linked to the presence of diabetes or liver disease. In subsequent DDI experiments, CYP2C19 activity was inhibited by omeprazole (−37% ± 8%), voriconazole (−59% ± 4%) and fluvoxamine (−85% ± 2%), but not by pantoprazole (−2 ± 4%). The strength of CYP2C19 inhibitors remained unaffected by CYP2C19 genotype, as similar percental declines in CYP2C19 activity and comparable metabolism-dependent inhibitory constants (Kinact/KI) of omeprazole were observed between CYP2C19 genotypes. However, the consequences of CYP2C19 inhibitor-mediated phenoconversion were different between CYP2C19 genotypes. In example, voriconazole converted 50% of *1/*1 donors to a IM/PM phenotype, but only 14% of *1/*17 donors. Fluvoxamine converted all donors to phenotypic IMs/PMs, but *1/*17 (14%) were less likely to become PMs than *1/*1 (50%) or *1/*2 and *2/*17 (57%).Conclusion: This study suggests that the differential outcome of CYP2C19-mediated DDIs between genotypes are primarily dictated by basal CYP2C19 activity, that may in part be predicted by CYP2C19 genotype but likely also depends on disease-related factors.

Optimization of Voriconazole Dosing Regimens Against Aspergillus Species and Candida Species in Pediatric Patients After Hematopoietic Cell Transplantation: A Theoretical Study Based on Pharmacokinetic/Pharmacodynamic Analysis.

This study aimed to optimize the dosing regimens of voriconazole (VRC) for pediatric patients after hematopoietic cell transplantation with different cytochrome P450 (CYP) 2C19 phenotypes and body weights, based on pharmacokinetic (PK)/pharmacodynamic (PD) analysis. The PK parameters of VRC were derived from previous literature. Combined with key factors affecting VRC, patients were categorized into 9 subgroups based on different CYP2C19 phenotypes (poor metabolizer/intermediate metabolizer, normal metabolizer, and rapid metabolizer/ultrarapid metabolizer) and typical body weights (15, 40, and 65kg). Monte Carlo simulation was used to investigate dosing regimens for different groups. The area under the 24-hour free drug concentration-time curve to the minimum inhibitory concentration (MIC) >25 was used as the target value for effective treatment. The probability of target achievement and the cumulative fraction of response were determined on the basis of the assumed MICs and MICs distribution frequency of Aspergillus species and Candida species. When the MIC was ≤1mg/L, 4mg/kg every 12 hours was sufficient for optimal effects in groups 1-3 and groups 5 and 6; however, 6mg/kg every 12 hours was required for group 4, and 8mg/kg every 12 hours was required for groups 7-9. In empirical treatment, lower (2-6mg/kg every 12 hours) and higher (6-12mg/kg every 12 hours) dosing regimens were recommended for Candida spp. and Aspergillus spp., respectively. Our findings will assist in selecting appropriate dosing regimens of VRC for pediatric patients after hematopoietic cell transplantation with different CYP2C19 phenotypes and body weights. Clinically, it is better to continuously adjust the dosing on the basis of the therapeutic drug monitoring.

Physiologically Based Pharmacokinetic Modeling for Multiple Oral Administration Labetalol in Pregnant Women.

Labetalol has an irreplaceable role in treating Hypertensive disorders of pregnancy (HDP), a common disease during pregnancy with a prevalence of 5.2-8.2%. However, there were big differences in dosage regimens between various guidelines. A physiologically-based pharmacokinetics (PBPK) model was established and validated to evaluate the existing oral dosage regimens, and to compare the difference in plasma concentration between pregnant and non-pregnant women. First, non-pregnant woman models with specific plasma clearance or enzymatic metabolism (UGT1A1, UGT2B7, CYP2C19) were established and validated. For CYP2C19, slow, intermediate, and rapid metabolic phenotypes were considered. Then, a pregnant model with proper structure and parameters adjustment was established and validated against the multiple oral administration data. The predicted labetalol exposure captured the experimental data well. The following simulations with criteria lowering 15mmHg blood pressure (corresponding to around 108ng/ml plasma labetalol) found that the maximum daily dosage in the Chinese guideline may be insufficient for some severe HDP patients. Moreover, similar predicted steady-state trough plasma concentration was found between the maximum daily dosage in the American College of Obstetricians and Gynecologists (ACOG) guideline, 800mg Q8h and a regimen of 200mg Q6h. Simulations comparing non-pregnant and pregnant women found that the difference in labetalol exposure highly depended on the CYP2C19 metabolic phenotype. In summary, this work initially established a PBPK model for multiple oral administration of labetalol for pregnant women. This PBPK model may lead to personalized labetalol medication in the future.

Distribution of a novel CYP2C haplotype in Native American populations.

The CYP2C19 gene, located in the CYP2C cluster, encodes the major drug metabolism enzyme CYP2C19. This gene is highly polymorphic and no-function (CYP2C19*2 and CYP2C19*3), reduced function (CYP2C19*9) and increased function (CYP2C19*17) star alleles (haplotypes) are commonly used to predict CYP2C19 metabolic phenotypes. CYP2C19*17 and the genotype-predicted rapid (RM) and ultrarapid (UM) CYP2C19 metabolic phenotypes are absent or rare in several Native American populations. However, discordance between genotype-predicted and pharmacokinetically determined CYP2C19 phenotypes in Native American cohorts have been reported. Recently, a haplotype defined by rs2860840T and rs11188059G alleles in the CYP2C cluster has been shown to encode increased rate of metabolism of the CYP2C19 substrate escitalopram, to a similar extent as CYP2C19*17. We investigated the distribution of the CYP2C:TG haplotype and explored its potential impact on CYP2C19 metabolic activity in Native American populations. The study cohorts included individuals from the One Thousand Genomes Project AMR superpopulation (1KG_AMR), the Human Genome Diversity Project (HGDP), and from indigenous populations living in Brazil (Kaingang and Guarani). The frequency range of the CYP2C:TG haplotype in the study cohorts, 0.469 to 0.598, is considerably higher than in all 1KG superpopulations (range: 0.014-to 0.340). We suggest that the high frequency of the CYP2C:TG haplotype might contribute to the reported discordance between CYP2C19-predicted and pharmacokinetically verified CYP2C19 metabolic phenotypes in Native American cohorts. However, functional studies involving genotypic correlations with pharmacokinetic parameters are warranted to ascertain the importance of the CYP2C:TG haplotype.

Global distribution of functionally important CYP2C9 alleles and their inferred metabolic consequences

BackgroundGenetic variability in the cytochrome P450 CYP2C9 constitutes an important predictor for efficacy and safety of various commonly prescribed drugs, including coumarin anticoagulants, phenytoin and multiple non-steroidal anti-inflammatory drugs (NSAIDs). A global map of CYP2C9 variability and its inferred functional consequences has been lacking.ResultsFrequencies of eight functionally relevant CYP2C9 alleles (*2, *3, *5, *6, *8, *11, *13 and *14) were analyzed. In total, 108 original articles were identified that included genotype data from a total of 81,662 unrelated individuals across 70 countries and 40 unique ethnic groups. The results revealed that CYP2C9*2 was most abundant in Europe and the Middle East, whereas CYP2C9*3 was the main reason for reduced CYP2C9 activity across South Asia. Our data show extensive variation within superpopulations with up to tenfold differences between geographically adjacent populations in Malaysia, Thailand and Vietnam. Translation of genetic CYP2C9 variability into functional consequences indicates that up to 40% of patients in Southern Europe and the Middle East might benefit from warfarin and phenytoin dose reductions, while 3% of patients in Southern Europe and Israel are recommended to reduce starting doses of NSAIDs.ConclusionsThis study provides a comprehensive map of the genetic and functional variability of CYP2C9 with high ethnogeographic resolution. The presented data can serve as a useful resource for CYP2C9 allele and phenotype frequencies and might guide the optimization of genotyping strategies, particularly for indigenous and founder populations with distinct genetic profiles.

The pharmacogenetics of CYP2D6 and CYP2C19 in a case series of antidepressant responses.

Pharmacogenetics has potential for optimizing use of psychotropics. CYP2D6 and CYP2C19 are two clinically relevant pharmacogenes in the prescribing of antidepressants. Using cases recruited from the Understanding Drug Reactions Using Genomic Sequencing (UDRUGS) study, we aimed to evaluate the clinical utility of genotyping CYP2D6 and CYP2C19 in antidepressant response. Genomic and clinical data for patients who were prescribed antidepressants for mental health disorders, and experienced adverse reactions (ADRs) or ineffectiveness, were extracted for analysis. Genotype-inferred phenotyping of CYP2D6 and CYP2C19 was carried out as per Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. A total of 52 patients, predominantly New Zealand Europeans (85%) with a median age (range) of 36 years (15-73), were eligible for analysis. Thirty-one (60%) reported ADRs, 11 (21%) ineffectiveness, and 10 (19%) reported both. There were 19 CYP2C19 NMs, 15 IMs, 16 RMs, one PM and one UM. For CYP2D6, there were 22 NMs, 22 IMs, four PMs, three UMs, and one indeterminate. CPIC assigned a level to each gene-drug pair based on curated genotype-to-phenotype evidence. We analyzed a subgroup of 45 cases, inclusive of response type (ADRs/ineffectiveness). Seventy-nine (N = 37 for CYP2D6, N = 42 for CYP2C19) gene-drug/antidepressant-response pairs with CPIC evidence levels of A, A/B, or B were identified. Pairs were assigned as 'actionable' if the CYP phenotypes potentially contributed to the observed response. We observed actionability in 41% (15/37) of CYP2D6-antidepressant-response pairs and 36% (15/42) of CYP2C19-antidepressant-response pairs. In this cohort, CYP2D6 and CYP2C19 genotypes were actionable for a total of 38% pairs, consisting of 48% in relation to ADRs and 21% in relation to drug ineffectiveness.

Associations of antidepressants and antipsychotics with lipid parameters: Do CYP2C19/CYP2D6 genes play a role? A UK population-based study.

Dyslipidaemia is an important cardiovascular risk factor for people with severe mental illness, contributing to premature mortality. The link between antipsychotics and dyslipidaemia is well established, while evidence on antidepressants is mixed. To investigate if antidepressant/antipsychotic use was associated with lipid parameters in UK Biobank participants and if CYP2C19 and CYP2D6 genetic variation plays a role. Review of self-reported prescription medications identified participants taking antidepressants/antipsychotics. Total, low-, and high-density lipoprotein cholesterol (L/HDL-C) and triglycerides derived from blood samples. CYP2C19 and CYP2D6 metabolic phenotypes were assigned from genetic data. Linear regression investigated aims, adjusted for key covariates. Of 469,739 participants, 36,043 took antidepressants (53% female, median age 58, 17% taking cholesterol-lowering medications) and 3255 took antipsychotics (58% female, median age 57, 27% taking cholesterol-lowering medications). Significant associations were found between use of each amitriptyline, fluoxetine, citalopram/escitalopram, sertraline, paroxetine and venlafaxine with higher total cholesterol, LDL-C, and triglycerides and lower HDL-C, compared to participants not taking each medication. Venlafaxine was associated with the worst lipid profile (total cholesterol, adjusted mean difference: 0.21 mmol/L, 95% confidence interval (CI): 0.17 to 0.26, p < 0.001). Antipsychotic use was significantly associated with lower HDL-C and higher triglycerides. In participants taking sertraline, CYP2C19 intermediate metabolisers had higher HDL-C (0.05 mmol/L, 95% CI: 0.01 to 0.09, p = 0.007) and lower triglycerides (-0.17 mmol/L, 95% CI: -0.29 to -0.05, p = 0.007), compared to normal metabolisers. Antidepressants were significantly associated with adverse lipid profiles, potentially warranting baseline and regular monitoring. Further research should investigate the mechanistic pathways underlying the protective effects of the CYP2C19 intermediate metaboliser phenotype on HDL-C and triglycerides in people taking sertraline.

Association of CYP2C19, CYP3A4 and ABCC2 polymorphisms and voriconazole plasma concentrations in Uygur pediatric patients.

Aim: To evaluate the association between CYP2C19, CYP3A4 and ABCC2 polymorphisms and voriconazole plasma concentrations in Uygur pediatric patients with allogeneic hematopoietic stem cell transplantation. Materials & methods: High performance liquid chromatography-mass spectrometry was employed to monitor voriconazole concentrations. First-generation sequencing was performed to detect gene polymorphisms. Results: Voriconazole concentrations of normal metabolizers were significantly higher than those of intermediate (p<0.05) and ultrafast (p<0.001) metabolizers. Patients with ABCC2 GG and GA genotypes exhibited significantly lower voriconazole concentrations compared with patients with the AA genotype (p<0.05). Conclusion: These results demonstrate a significant association between voriconazole concentrations and the CYP2C19 phenotype in Uygur pediatric patients with allogeneic hematopoietic stem cell transplantation.

Torsemide Pharmacometrics in Healthy Adult Populations Including CYP2C9 Genetic Polymorphisms and Various Patient Groups through Physiologically Based Pharmacokinetic-Pharmacodynamic Modeling.

Torsemide is a widely used diuretic in clinical practice. In this study, pharmacokinetic (PK) and pharmacodynamic (PD) simulations of torsemide for various population groups and exposure scenarios were performed through human-scale physiologically-based PK-PD (PBPK-PD) modeling of torsemide. For PBPK-PD modeling of torsemide, invitro and clinical data of torsemide reported previously were used. After exposure to clinical doses of torsemide, observed plasma (or serum) concentration and urine torsemide excretion profiles were used as PK-data, and observed urinary sodium excretion rate was used as PD-data. The model was then extended to take into account physiological and biochemical factors according to different CYP2C9 phenotypes or patient populations. The established model captured various torsemide clinical results well. Differences in torsemide PKs and PDs between patient groups or CYP2C9 genetic polymorphisms were modelologically identified. It was confirmed that degrees of differences in torsemide PKs and PDs by disease groups were greater than those according to different CYP2C9 phenotypes. According to torsemide administration frequency or dose change, it was confirmed that although the difference in plasma PKs between groups (healthy adult and patient groups) could increase to 14.80 times, the difference in PDs was reduced to 1.01 times. Results of this study suggested that it is very important to consider disease groups in the setting of torsemide clinical therapy and that it is difficult to predict PD proportionally with only differences in PKs of torsemide between population groups. The PBPK-PD model established in this study is expected to be utilized for various clinical cases involving torsemide application in the future, enabling optimal drug therapy.

Identification of Escitalopram Metabolic Ratios as Potential Biomarkers for Predicting CYP2C19 Poor Metabolizers.

Escitalopram is metabolized by CYP2C19 to N-desmethyl escitalopram and escitalopram propionic acid. The primary aims of this study were to investigate the impact of the CYP2C19 phenotype on metabolic ratios of escitalopram in vivo and propose a biomarker for the CYP2C19 phenotype in patients treated with escitalopram. Median steady-state serum metabolite/parent drug ratio of N-desmethyl escitalopram and escitalopram propionic acid was investigated across CYP2C19 genotype-translated phenotype groups. The receiver operator characteristics method and the area-under-the-receiver-operator-characteristics curve was used to determine the best suited metabolic ratio for detecting CYP2C19 poor metabolizers (PMs). A total of 441 patients were included in the study. The N-desmethyl escitalopram/escitalopram ratio was 67% and 44% lower in CYP2C19 PMs and intermediate metabolizers (IMs), respectively, than normal metabolizers. Furthermore, the ability of the ratio to predict CYP2C19 PMs was 92%. A metabolic ratio of <0.24 was detected in 8 of 8 PMs in the study, indicating that it is a promising biomarker of reduced CYP2C19 activity. The escitalopram propionic acid/escitalopram ratio was 77% and 48% lower in CYP2C19 PMs and IMs, respectively; however, the ability of the ratio to detect CYP2C19 PMs was only 87%. These findings suggest that DECT/ECT reflects CYP2C19 activity, and a metabolic ratio of <0.24 strongly predicts CYP2C19 PM phenotype. The ratio could be a valuable alternative to genotyping in personalized dosing of escitalopram and possibly other CYP2C19 substrates. The escitalopram propionic acid/escitalopram ratio was also associated with CYP2C19 activity; however, the ratio was inferior to the DECT/ECT at predicting PMs.

Dosage optimization of voriconazole in children with haematological malignancies based on population pharmacokinetics.

Voriconazole has a complex pharmacokinetic profile and exhibits different pharmacokinetic characteristics in adults and children. Nevertheless, few studies have been conducted on the population pharmacokinetics (PPK) of voriconazole in children with haematological malignancies. This study aims to build a PPK model and propose a suitable voriconazole treatment scheme for children with haematological malignancies. We retrospectively collected 146 samples from 67 children aged from 1.08 to 17.92 years. The PPK model was established using nonlinear mixed effects modelling (NONMEM). Dosage simulations were conducted on the basis of the final model's covariates. Data were fully characterized by a one-compartment model with first-order absorption and elimination. The weight (WT), CYP2C19 phenotype, and Albumin (ALB) were notable covariates for clearance (CL). The typical values of CL, the volume of distribution (V), and oral bioavailability (F) were 2.29 L/h, 76 L, and 0.902, respectively. The proposed doses for different CYP2C19 genotypes were presented in this ranking: EM (extensive metabolizer) > IM (intermediate metabolizer) > PM (poor metabolizer). Furthermore, higher dosages for light WT patients were recommended while lower ALB levels required lower doses. The probability of achieving the target (PTA) for the recommended doses ranged from 72.2% to 99%. We successfully built a voriconazole PPK model for children with hematologic malignancies. Dosing regimens were developed for different patients based on the final model, which could enhance the rational use of voriconazole in children with haematological malignancies.

Performance Verification of CYP2C19 Enzyme Abundance Polymorphism Settings within the Simcyp Simulator v21.

Physiologically based pharmacokinetic (PBPK) modeling has a number of applications, including assessing drug–drug interactions (DDIs) in polymorphic populations, and should be iteratively refined as science progresses. The Simcyp Simulator is annually updated and version 21 included updates to hepatic and intestinal CYP2C19 enzyme abundance, including addition of intermediate and rapid metabolizer phenotypes and changes to the ultra-rapid metabolizer enzyme abundance, with implications for population clearance and DDI predictions. This work details verification of the updates with sensitive CYP2C19 substrates, omeprazole and lansoprazole, using available clinical data from literature. Multiple assessments were performed, including recovery of areas under the concentration-time curve (AUC) and Cmax from compiled datasets for each drug, recovery of victim DDI ratios with CYP2C19 and/or CYP3A4 inhibition and recovery of relative exposure between phenotypes. Simulated data were within respective acceptance criteria for >80% of omeprazole AUC values, >70% of lansoprazole AUC and Cmax, >60% of AUC and Cmax DDI ratios and >80% of exposure ratios between different phenotypes. Recovery of omeprazole Cmax was lower (>50–70% within 2-fold) and possibly attributed to the variety of formulations used in the clinical dataset. Overall, the results demonstrated that the updated data used to parameterize CYP2C19 phenotypes reasonably described the pharmacokinetics of omeprazole and lansoprazole in genotyped or phenotyped individuals.

SLCO1B1 c.521T>C gene polymorphism decreases hypoglycemia risk in sulfonylurea-treated type 2 diabetic patients.

Pharmacogenomics can explain some of the heterogeneity of sulfonylurea (SU)-related hypoglycemia risk. Recently, a role of OATP1B1, encoded by SLCO1B1 gene, on SU liver transport prior of metabolism has been uncovered. The aim of the present study was to explore the potential association of SLCO1B1 c.521T>C polymorphism, leading to reduced OATP1B1 function, with SU-related hypoglycemia risk. Study cohort consists of 176 type 2 diabetes patients treated with the SUs glimepiride or gliclazide. 92patients reported SU-related hypoglycemia, while 84 patients had never experienced a hypoglycemic event. Patients were previously genotyped for CYP2C9 *2 and *3 variant alleles that lead to decreased enzyme activity of the SU metabolizing enzyme CYP2C9 and have been associated with increased SU-related hypoglycemia risk. SLCO1B1 c.521T>C polymorphism was genotyped by use of PCR-RFLP analysis. SLCO1B1 c.521TC genotype frequency was significantly lower in hypoglycemic cases than non-hypoglycemic controls (15.2% vs. 32.1%, p=0.008). In an adjusted model, c.521TC genotype significantly reduced the risk of hypoglycemia (OR 0.371; 95% C.I. 0.167-0.822; p=0.015). InCYP2C9 intermediate metabolizers (n=54) c.521TC genotype frequency was significantly decreased in cases compared to controls (3 out of 36 cases, 8.3% vs. 7 out of 18 controls, 38.9%, p=0.012). A similar albeit not significant difference of SLCO1B1 c.521TC genotype was present in CYP2C9 extensive metabolizers (n=120) (18.2% in cases vs. 30.8% in controls, p=0.113). We have found a protective effect of SLCO1B1 c.521C variant on SU-related hypoglycemia risk both independently and in interaction with CYP2C9 phenotypes. Our results suggest a possible linkage of SLCO1B1 c.521T>C polymorphism with variants in other genes impairing OATPs expressed in pancreatic islets that could interfere with SU tissue distribution.

Association between CYP2C19 and CYP2B6 phenotypes and the pharmacokinetics and safety of diazepam

Diazepam is a benzodiazepine (BZD) used worldwide for a variety of conditions. Long-term use of diazepam increases the risk for developing tolerance and dependence and for the occurrence of adverse drug reactions (ADRs). CYP3A4 and CYP2C19 mainly metabolize diazepam and are therefore the primary pharmacogenetic candidate biomarkers. In this work, we aimed to explore the impact of CYP3A4 and CYP2C19 phenotypes and of 99 additional variants in other 31 pharmacogenes (including other CYP, UGT, NAT2 and CES enzymes, ABC and SLC transporters) on diazepam pharmacokinetic variability and safety. 30 healthy volunteers that had participated in a single-dose bioequivalence clinical trial of two diazepam formulations were enrolled in the present candidate gene pharmacogenetic study. CYP2C19 poor metabolizers (PMs) showed an almost 2-fold increase in AUC0-∞/DW compared to rapid (RMs) or normal (NM) metabolizers, and a 1.46-fold increase compared to intermediate metabolizers (IMs). CYP2B6 PMs showed a 2,74-fold higher AUC0-∞/DW compared to RMs, and 2.10-fold compared to NMs (p < 0.007). A dose reduction of 25–50 % may be appropriate for CYP2C19 or CYP2B6 PMs to avoid ADRs, dependence and tolerance. Combined CYP2C19 +CYP2B6 PMs may not use diazepam or sharper dose adjustments (e.g., a dose reduction of 50–70 %) may be advisable. To our knowledge, this is the first work to report a strong relationship between CYP2B6 phenotype and diazepam pharmacokinetics. Additional nominal associations (i.e., 0.007 <p < 0.05) between ABCG2, ABCB1, NAT2 and UGT1A4 polymorphisms and pharmacokinetic variability were observed; further research should elaborate on the clinical relevance of the described associations.