PBPK modeling to predict the pharmacokinetics of venlafaxine and its active metabolite in different CYP2D6 genotypes and drug-drug interactions with clarithromycin and paroxetine.

Venlafaxine, a serotonin-norepinephrine reuptake inhibitor (SNRI), is indicated for the treatment of major depressive disorder, social anxiety disorder, generalized anxiety disorder, and panic disorder. Venlafaxine is metabolized to the active metabolite desvenlafaxine mainly by CYP2D6. Genetic polymorphism of CYP2D6 and coadministration with other medications can significantly affect the pharmacokinetics and/or pharmacodynamics of venlafaxine and its active metabolite. This study aimed to establish the PBPK models of venlafaxine and its active metabolite related to CYP2D6 genetic polymorphism and to predict drug-drug interactions (DDIs) with clarithromycin and paroxetine in different CYP2D6 genotypes. Clinical pharmacogenomic data for venlafaxine and desvenlafaxine were collected to build the PBPK model. Physicochemical and absorption, distribution, metabolism, and excretion (ADME) characteristics of respective compounds were obtained from previously reported data, predicted by the PK-Sim® software, or optimized to capture the plasma concentration-time profiles. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and plasma concentration-time profiles to the observed data. Predicted plasma concentration-time profiles of venlafaxine and its active metabolite were visually similar to the observed profiles and all predicted AUC and Cmax values for respective compounds were included in the twofold error range of observed values in non-genotyped populations and different CYP2D6 genotypes. When clarithromycin or clarithromycin plus paroxetine was concomitantly administered, predicted plasma concentration-time profiles of venlafaxine properly captured the observed profiles in two different CYP2D6 genotypes and all predicted DDI ratios for AUC and Cmax were included within the acceptance range. Consequently, the present model successfully captured the pharmacokinetic alterations of venlafaxine and its active metabolite according to CYP2D6 genetic polymorphism as well as the DDIs between venlafaxine and two CYP inhibitors. The present model can be used to predict the pharmacokinetics of venlafaxine and its active metabolite considering different races, ages, coadministered drugs, and CYP2D6 activity of individuals and it can contribute to individualized pharmacotherapy of venlafaxine.

A chitosan/alginate coated nano-liposome to improve intestinal absorption of curcumin for oral administration.

Attempts to improve low absorption and rapid metabolic conversion of curcumin were made by developing curcumin-loaded bilayer nanoliposomes coated with chitosan and alginate for intestinal-specific drug delivery. A curcumin-loaded nano-liposome was prepared with optimized formulations with phosphatidylcholine, curcumin, chitosan, and alginate. The particle size of the optimized formulation was approximately 400 nm, and the encapsulation efficiency was more than 99%. In the in vitro release study, curcumin release from the curcumin-loaded nanoliposome with double layers of chitosan/alginate (CNL-CH/AL) was suppressed in the simulated gastric fluid (SGF, pH 1.2) and enhanced in the simulated intestinal fluid (SIF, pH 6.8). In the in vivo pharmacokinetic study in rats, the CNL-CH/AL-treated group showed a prolonged absorption pattern of curcumin and the area under the plasma concentration-time curve from 0 to 24 h (AUC0-24) was improved 109-fold compared to the control group treated with a curcumin solution without a nanocarrier.

Skp1 proteins are structural components of the synaptonemal complex in C. elegans.

The synaptonemal complex (SC) is a zipper-like protein assembly that links homologous chromosomes to regulate recombination and segregation during meiosis. The SC has been notoriously refractory to in vitro reconstitution, thus leaving its molecular organization largely unknown. Here, we report a moonlighting function of two paralogous S-phase kinase-associated protein 1 (Skp1)-related proteins (SKR-1 and SKR-2), well-known adaptors of the Skp1-Cul1-F-box (SCF) ubiquitin ligase, as the key missing components of the SC in Caenorhabditis elegans. SKR proteins repurpose their SCF-forming interfaces to dimerize and interact with meiosis-specific SC proteins, thereby driving synapsis independent of SCF activity. SKR-1 enables the formation of the long-sought-after soluble complex with previously identified SC proteins in vitro, which we propose it to represent a complete SC building block. Our findings demonstrate how a conserved cell cycle regulator has been co-opted to interact with rapidly evolving meiotic proteins to construct the SC and provide a foundation for understanding its structure and assembly mechanisms.

PBPK modeling to predict the pharmacokinetics of pantoprazole in different CYP2C19 genotypes.

Pantoprazole is used to treat gastroesophageal reflux disease (GERD), maintain healing of erosive esophagitis (EE), and control symptoms related to Zollinger-Ellison syndrome (ZES). Pantoprazole is mainly metabolized by cytochrome P450 (CYP) 2C19, converting to 4'-demethyl pantoprazole. CYP2C19 is a genetically polymorphic enzyme, and the genetic polymorphism affects the pharmacokinetics and/or pharmacodynamics of pantoprazole. In this study, we aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of pantoprazole in populations with various CYP2C19 metabolic activities. A comprehensive investigation of previous reports and drug databases was conducted to collect the clinical pharmacogenomic data, physicochemical data, and disposition properties of pantoprazole, and the collected data were used for model establishment. The model was evaluated by comparing the predicted plasma concentration-time profiles and/or pharmacokinetic parameters (AUC and Cmax) with the clinical observation results. The predicted plasma concentration-time profiles in different CYP2C19 phenotypes properly captured the observed profiles. All fold error values for AUC and Cmax were included in the two-fold range. Consequently, the minimal PBPK model for pantoprazole related to CYP2C19 genetic polymorphism was properly established and it can predict the pharmacokinetics of pantoprazole in different CYP2C19 phenotypes. The present model can broaden the insight into the individualized pharmacotherapy for pantoprazole.

Physiologically based pharmacokinetic (PBPK) modeling of pitavastatin in relation to SLCO1B1 genetic polymorphism.

Pitavastatin, a potent 3-hydroxymethylglutaryl coenzyme A reductase inhibitor, is indicated for the treatment of hypercholesterolemia and mixed dyslipidemia. Hepatic uptake of pitavastatin is predominantly occupied by the organic anion transporting polypeptide 1B1 (OATP1B1) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, which is a polymorphic gene that encodes OATP1B1. SLCO1B1 genetic polymorphism significantly alters the pharmacokinetics of pitavastatin. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict pitavastatin pharmacokinetics according to SLCO1B1 genetic polymorphism. PK-Sim® version 10.0 was used to establish the whole-body PBPK model of pitavastatin. Our pharmacogenomic data and a total of 27 clinical pharmacokinetic data with different dose administration and demographic properties were used to develop and validate the model, respectively. Physicochemical properties and disposition characteristics of pitavastatin were acquired from previously reported data or optimized to capture the plasma concentration-time profiles in different SLCO1B1 diplotypes. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and profiles to the observed data. Predicted plasma concentration-time profiles were visually similar to the observed profiles in the non-genotyped populations and different SLCO1B1 diplotypes. All fold error values for AUC and Cmax were included in the two fold range of observed values. Thus, the PBPK model of pitavastatin in different SLCO1B1 diplotypes was properly established. The present study can be useful to individualize the dose administration strategy of pitavastatin in individuals with various ages, races, and SLCO1B1 diplotypes.

Alcohol perturbed locomotor behavior, metabolism, and pharmacokinetics of gamma-hydroxybutyric acid in rats.

Gamma-hydroxybutyric acid (GHB), both a metabolic precursor and product of gamma-aminobutyric acid (GABA), is a central nervous system depressant used for the treatment of narcolepsy-associated cataplexy and alcohol withdrawal. However, administration of GHB with alcohol (ethanol) is a major cause of hospitalizations related to GHB intoxication. In this study, we investigated locomotor behavior as well as metabolic and pharmacokinetic interactions following co-administration of GHB and ethanol in rats. The locomotor behavior of rats was evaluated following the intraperitoneal administration of GHB (sodium salt, 500 mg/kg) and/or ethanol (2 g/kg). Further, time-course urinary metabolic profiling of GHB and its biomarker metabolites glutamic acid, GABA, succinic acid, 2,4-dihydroxybutyric acid (OH-BA), 3,4-OH-BA, and glycolic acid as well as pharmacokinetic analysis were performed. GHB/ethanol co-administration significantly reduced locomotor activity, compared to the individual administration of GHB or ethanol. The urinary and plasma concentrations of GHB and other target compounds, except for 2,4-OH-BA, were significantly higher in the GHB/ethanol co-administration group than the group administered only GHB. The pharmacokinetic analysis results showed that the co-administration of GHB and ethanol significantly increased the half-life of GHB while the total clearance decreased. Moreover, a comparison of the metabolite-to-parent drug area under the curve ratios demonstrated that the metabolic pathways of GHB, such α- and ß-oxidation, were inhibited by ethanol. Consequently, the co-administration of GHB and ethanol aggravated the metabolism and elimination of GHB and enhanced its sedative effect. These findings will contribute to clinical interpretation of GHB intoxication.

Identification of Potential Biomarkers for Diagnosis of Patients with Methamphetamine Use Disorder.

The current method for diagnosing methamphetamine use disorder (MUD) relies on self-reports and interviews with psychiatrists, which lack scientific rigor. This highlights the need for novel biomarkers to accurately diagnose MUD. In this study, we identified transcriptome biomarkers using hair follicles and proposed a diagnostic model for monitoring the MUD treatment process. We performed RNA sequencing analysis on hair follicle cells from healthy controls and former and current MUD patients who had been detained in the past for illegal use of methamphetamine (MA). We selected candidate genes for monitoring MUD patients by performing multivariate analysis methods, such as PCA and PLS-DA, and PPI network analysis. We developed a two-stage diagnostic model using multivariate ROC analysis based on the PLS-DA method. We constructed a two-step prediction model for MUD diagnosis using multivariate ROC analysis, including 10 biomarkers. The first step model, which distinguishes non-recovered patients from others, showed very high accuracy (prediction accuracy, 98.7%). The second step model, which distinguishes almost-recovered patients from healthy controls, showed high accuracy (prediction accuracy, 81.3%). This study is the first report to use hair follicles of MUD patients and to develop a MUD prediction model based on transcriptomic biomarkers, which offers a potential solution to improve the accuracy of MUD diagnosis and may lead to the development of better pharmacological treatments for the disorder in the future.

Effects of CYP2C9 and CYP2C19 genetic polymorphisms on the pharmacokinetics and pharmacodynamics of gliclazide in healthy subjects.

Gliclazide metabolism is mediated by genetically polymorphic CYP2C9 and CYP2C19 enzymes. We investigated the effects of CYP2C9 and CYP2C19 genetic polymorphisms on the pharmacokinetics and pharmacodynamics of gliclazide. Twenty-seven Korean healthy volunteers were administered a single oral dose of gliclazide 80 mg. The plasma concentration of gliclazide was quantified for the pharmacokinetic analysis and plasma concentrations of glucose and insulin were measured as pharmacodynamic parameters. The pharmacokinetics of gliclazide showed a significant difference according to the number of defective alleles of combined CYP2C9 and CYP2C19. The two defective alleles group (group 3) and one defective allele group (group 2) showed 2.34- and 1.46-fold higher AUC0-∞ (P < 0.001), and 57.1 and 32.3% lower CL/F (P < 0.001), compared to those of the no defective allele group (group 1), respectively. The CYP2C9IM-CYP2C19IM group had AUC0-∞ increase of 1.49-fold (P < 0.05) and CL/F decrease by 29.9% (P < 0.01), compared with the CYP2C9 Normal Metabolizer (CYP2C9NM)-CYP2C19IM group. The CYP2C9NM-CYP2C19PM group and CYP2C9NM-CYP2C19IM group showed 2.41- and 1.51-fold higher AUC0-∞ (P < 0.001), and 59.6 and 35.4% lower CL/F (P < 0.001), compared to those of the CYP2C9NM-CYP2C19NM group, respectively. The results represented that CYP2C9 and CYP2C19 genetic polymorphisms significantly affected the pharmacokinetics of gliclazide. Although the genetic polymorphism of CYP2C19 had a greater effect on the pharmacokinetics of gliclazide, the genetic polymorphism of CYP2C9 also had a significant effect. On the other hand, plasma glucose and insulin responses to gliclazide were not significantly affected by the CYP2C9-CYP2C19 genotypes, requiring further well-controlled studies with long-term dosing of gliclazide in diabetic patients.

Effects of CYP2D6*10 allele on the pharmacokinetics of tolperisone.

Tolperisone, a muscle relaxant used for post-stroke spasticity, has been reported to have a very wide interindividual pharmacokinetic variability. It is metabolized mainly by CYP2D6 and, to a lesser extent, by CYP2C19 and CYP1A2. CYP2D6 is a highly polymorphic enzyme, and CYP2D6*wt/*wt, CYP2D6*wt/*10 and CYP2D6*10/*10 genotypes constitute more than 90% of the CYP2D6 genotypes in the Korean population. Thus, effects of the CYP2D6*10 on tolperisone pharmacokinetics were investigated in this study to elucidate the reasons for the wide interindividual variability. Oral tolperisone 150 mg was given to sixty-four healthy Koreans, and plasma concentrations of tolperisone were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The CYP2D6*10/*10 and CYP2D6*wt/*10 groups had significantly higher Cmax and lower CL/F values than the CYP2D6*wt/*wt group. The AUCinf of CYP2D6*10/*10 and CYP2D6*wt/*10 groups were 5.18-fold and 2.25-fold higher than the CYP2D6*wt/*wt group, respectively. There were considerable variations in the Cmax and AUC values within each genotype group, and the variations were greater as the activity of CYP2D6 decreased. These results suggest that the genetic polymorphism of CYP2D6 significantly affected tolperisone pharmacokinetics and factor(s) other than CYP2D6 may also have significant effects on the pharmacokinetics of tolperisone.

A self-emulsifying omega-3 fatty acids delivery system for enhanced gastro-intestinal absorption in rats.

Omega-3 fatty acids have many health benefits as they help to prevent and treat coronary artery disease, hypertension, diabetes mellitus, arthritis, and autoimmune disorders. Omega-3 fatty acids miscible in lecithin were found to spontaneously form microemulsions in water. The particle sizes of emulsions ranged from 300 to 800 nm and their morphologies were observed by optical microscopy. In vitro testing showed that the amounts of omega-3 fatty acids released by self-emulsifying delivery (SED) formulations containing lecithin, were higher than that released by a commercial formulation without lecithin. The Cmax values of docosahexaenoic acid (DHA) or eicosapentaenoic acid (EPA) were approximately 1.38-1.40-fold for the optimized SED formulation than for the control group (P < 0.01). Similarly, the mean AUC0 - 48 values of DHA or EPA in the SED group were 1.27-1.29-fold higher than in the control group (P < 0.05). Phospholipids and lecithin were found to have considerable potentials as bioavailability enhancing excipients for SED systems.

Physiologically based pharmacokinetic (PBPK) modeling of flurbiprofen in different CYP2C9 genotypes.

The aim of this study was to establish the physiologically based pharmacokinetic (PBPK) model of flurbiprofen related to CYP2C9 genetic polymorphism and describe the pharmacokinetics of flurbiprofen in different CYP2C9 genotypes. PK-Sim® software was used for the model development and validation. A total of 16 clinical pharmacokinetic data for flurbiprofen in different CYP2C9 genotypes, dose regimens, and age groups were used for the PBPK modeling. Turnover number (kcat) of CYP2C9 values were optimized to capture the observed profiles in different CYP2C9 genotypes. In the simulation, predicted fraction metabolized by CYP2C9, fraction excreted to urine, bioavailability, and volume of distribution were similar to previously reported values. Predicted plasma concentration-time profiles in different CYP2C9 genotypes were visually similar to the observed profiles. Predicted AUCinf in CYP2C9*1/*2, CYP2C9*1/*3, and CYP2C9*3/*3 genotypes were 1.44-, 2.05-, and 3.67-fold higher than the CYP2C9*1/*1 genotype. The ranges of fold errors for AUCinf, Cmax, and t1/2 were 0.84-1.00, 0.61-1.22, and 0.74-0.94 in development and 0.59-0.98, 0.52-0.97, and 0.61-1.52 in validation, respectively, which were within the acceptance criterion. Thus, the PBPK model was successfully established and described the pharmacokinetics of flurbiprofen in different CYP2C9 genotypes, dose regimens, and age groups. The present model could guide the decision-making of tailored drug administration strategy by predicting the pharmacokinetics of flurbiprofen in various clinical scenarios.

Physiologically based pharmacokinetic modelling to predict the pharmacokinetics of metoprolol in different CYP2D6 genotypes.

Metoprolol, a selective ß1-adrenoreceptor blocking agent used in the treatment of hypertension, angina, and heart failure, is primarily metabolized by the CYP2D6 enzyme, which catalyzes α-hydroxylation and O-desmethylation. As CYP2D6 is genetically highly polymorphic and the enzymatic activity differs greatly depending on the presence of the mutant allele(s), the pharmacokinetic profile of metoprolol is highly variable depending on the genotype of CYP2D6. The aim of study was to develop the physiologically based pharmacokinetic (PBPK) model of metoprolol related to CYP2D6 genetic polymorphism for personalized therapy with metoprolol. For PBPK modelling, our previous pharmacogenomic data were used. To obtain kinetic parameters (Km, Vmax, and CLint) of each genotype, the recombinant CYP enzyme of each genotype was incubated with metoprolol and metabolic rates were assayed. Based on these data, the PBPK model of metoprolol was developed and validated in different CYP2D6 genotypes using PK-Sim® software. As a result, the input values for various parameters for the PBPK model were presented and the PBPK model successfully described the pharmacokinetics of metoprolol in each genotype group. The simulated values were within the acceptance criterion (99.998% confidence intervals) compared with observed values. The PBPK model developed in this study can be used for personalized pharmacotherapy with metoprolol in individuals of various races, ages, and CYP2D6 genotypes.

Physiologically based pharmacokinetic (PBPK) modeling of piroxicam with regard to CYP2C9 genetic polymorphism.

Piroxicam is a non-steroidal anti-inflammatory drug used to alleviate symptoms of osteoarthritis and rheumatoid arthritis. CYP2C9 genetic polymorphism significantly influences the pharmacokinetics of piroxicam. The objective of this study was to develop and validate the piroxicam physiologically based pharmacokinetic (PBPK) model related to CYP2C9 genetic polymorphism. PK-Sim® version 10.0 was used for the PBPK modeling. The PBPK model was evaluated by predicted and observed plasma concentration-time profiles, fold errors of predicted to observed pharmacokinetic parameters, and a goodness-of-fit plot. The turnover number (kcat) of CYP2C9 was adjusted to capture the pharmacokinetics of piroxicam in different CYP2C9 genotypes. The population PBPK model overall accurately described and predicted the plasma concentration-time profiles in different CYP2C9 genotypes. In our simulations, predicted AUCinf in CYP2C9*1/*2, CYP2C9*1/*3, and CYP2C9*3/*3 genotypes were 1.83-, 2.07-, and 6.43-fold higher than CYP2C9*1/*1 genotype, respectively. All fold error values for AUC, Cmax, and t1/2 were included in the acceptance criterion with the ranges of 0.57-1.59, 0.63-1.39, and 0.65-1.51, respectively. The range of fold error values for predicted versus observed plasma concentrations was 0.11-3.13. 93.9% of fold error values were within the two-fold range. Average fold error, absolute average fold error, and root mean square error were 0.93, 1.27, and 0.72, respectively. Our model accurately captured the pharmacokinetic alterations of piroxicam according to CYP2C9 genetic polymorphism.

Association between Serum Prolactin Levels and Neurodegenerative Diseases: Systematic Review and Meta-Analysis.

INTRODUCTION: Prolactin (PRL) exerts inflammatory and anti-inflammatory properties and is also thought to play an important role in the pathogenesis of neurodegenerative diseases (NDs). However, serum PRL levels in patients with NDs were inconsistent in the research literature. OBJECTIVE: We aimed to assess the serum PRL levels in patients with NDs. METHODS: Electronic databases, including MEDLINE, Embase, Cochrane Library database, clinicaltrials.gov, Web of Science, and Google Scholar, and reference lists of articles were searched up to December 31, 2020. Pooled standard mean difference (SMD) with 95% confidence interval (CI) was calculated by fixed-effect or random-effect model analysis. RESULTS: A total of 36 comparisons out of 29 studies (3 RCTs and 26 case controls) focusing on NDs (including Parkinson's disease, Alzheimer's disease, Huntington's disease [HD], multiple sclerosis [MS], and epilepsy) were reported. The meta-analysis showed that there was no statistically significant difference in serum PRL levels between patients with NDs and healthy controls (SMD = 0.40, 95% CI: -0.16 to 0.96, p = 0.16). Subgroup analysis showed that serum PRL levels in patients with HD and MS were higher than those of healthy controls. Furthermore, patients with NDs aged <45 years had higher serum PRL levels (SMD = 0.97, 95% CI: 0.16-1.78, p = 0.018) than healthy controls. High serum PRL levels were found in subgroups such as the microenzymatic method, Asia, and the Americas. CONCLUSIONS: Our meta-analysis showed serum PRL levels in patients with HD and MS were significantly higher than those in healthy controls. Serum PRL levels were associated with age, region, and detection method. Other larger sample studies using more uniform detection methods are necessary to confirm our results.

Effects of CYP2C9*3 and *13 alleles on the pharmacokinetics and pharmacodynamics of glipizide in healthy Korean subjects.

Glipizide is a second-generation sulfonylurea antidiabetic drug. It is principally metabolized to inactive metabolites by genetically polymorphic CYP2C9 enzyme. In this study, we investigated the effects of CYP2C9*3 and *13 variant alleles on the pharmacokinetics and pharmacodynamics of glipizide. Twenty-four healthy Korean volunteers (11 subjects with CYP2C9*1/*1, 8 subjects with CYP2C9*1/*3, and 5 subjects with CYP2C9*1/*13) were recruited for this study. They were administered a single oral dose of glipizide 5 mg. The plasma concentration of glipizide was quantified for pharmacokinetic analysis and plasma glucose and insulin concentrations were measured as pharmacodynamic parameters. The results represented that CYP2C9*3 and *13 alleles significantly affected the pharmacokinetics of glipizide. In subjects with CYP2C9*1/*3 and CYP2C9*1/*13 genotypes, the mean AUC0-∞ were increased by 44.8% and 58.2%, respectively (both P < 0.001), compared to those of subjects with CYP2C9*1/*1 genotype, while effects of glipizide on plasma glucose and insulin levels were not significantly different between CYP2C9 genotype groups. In conclusion, individuals carrying the defective CYP2C9*3 and CYP2C9*13 alleles have markedly elevated plasma concentrations of glipizide compared with CYP2C9*1/*1 wild-type.

Physiologically based pharmacokinetic (PBPK) modelling of tamsulosin related to CYP2D6*10 allele.

Tamsulosin, a selective [Formula: see text]-adrenoceptor blocker, is commonly used for alleviation of lower urinary tract symptoms related to benign prostatic hyperplasia. Tamsulosin is predominantly metabolized by CYP3A4 and CYP2D6 enzymes, and several studies reported the effects of CYP2D6 genetic polymorphism on the pharmacokinetics of tamsulosin. This study aims to develop and validate the physiologically based pharmacokinetic (PBPK) model of tamsulosin in CYP2D6*wt/*wt, CYP2D6*wt/*10, and CYP2D6*10/*10 genotypes, using Simcyp® simulator. Physicochemical, and formulation properties and data for absorption, distribution, metabolism and excretion were collected from previous publications, predicted in the simulator, or optimized in different CYP2D6 genotypes. The tamsulosin PBPK model in CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes were developed based on the clinical pharmacokinetic study where a single oral dose of 0.2 mg tamsulosin was administered to 25 healthy Korean male volunteers with CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes. A previous pharmacokinetic study was used to develop the model in CYP2D6*10/*10 genotype. The developed model was validated using other clinical pharmacokinetic studies not used in development. The predicted exposures via the PBPK model in CYP2D6*wt/*10 and CYP2D6*10/*10 genotype was 1.23- and 1.76-fold higher than CYP2D6*wt/*wt genotype, respectively. The simulation profiles were visually similar to the observed profiles, and fold errors of all development and validation datasets were included within the criteria. Therefore, the tamsulosin PBPK model in different CYP2D6 genotypes with regards to CYP2D6*10 alleles was appropriately established. Our model can contribute to the implementation of personalized pharmacotherapy of patients, appropriately predicting the pharmacokinetics of tamsulosin reflecting their demographic and CYP2D6 genotype characteristics without unnecessary drug exposure.

Physiologically based pharmacokinetic (PBPK) modeling of meloxicam in different CYP2C9 genotypes.

Meloxicam, a non-steroidal anti-inflammatory drug, is used for the treatment of rheumatoid arthritis and osteoarthritis. Cytochrome P450 (CYP) 2C9 and CYP3A4 are major and minor enzymes involved in the metabolism of meloxicam. Impaired enzyme activity of CYP2C9 variants increases the plasma exposures of meloxicam and the risk of adverse events. The objective of our study is to develop and validate the physiologically based pharmacokinetic (PBPK) model of meloxicam related to CYP2C9 genetic polymorphism using the PK-Sim® software. In vitro kcat of CYP2C9 was optimized in different CYP2C9 genotypes. The demographic and pharmacokinetic dataset for the development of the PBPK model was extracted from two previous clinical pharmacokinetic studies. Thirty-one clinical datasets, representing different dose regimens and demographic characteristics, were utilized to validate the PBPK model. The shapes of simulated plasma concentration-time profiles in each CYP2C9 genotype were visually similar to observed profiles. The predicted exposures (AUCinf) of meloxicam in CYP2C9*1/*3, CYP2C9*1/*13, and CYP2C9*3/*3 genotypes were increased by 1.77-, 2.91-, and 8.35-fold compared to CYP2C9*1/*1 genotype, respectively. In all datasets for the development and validations, fold errors between predicted and observed pharmacokinetic parameters were within the two-fold error criteria. As a result, the PBPK model was appropriately established and properly described the pharmacokinetics of meloxicam in different CYP2C9 genotypes. This study is expected to contribute to reducing the risk of adverse events of meloxicam through optimization of meloxicam dosing in different CYP2C9 genotypes.

Physiologically based pharmacokinetic modeling of candesartan related to CYP2C9 genetic polymorphism in adult and pediatric patients.

Candesartan cilexetil is an angiotensin II receptor blocker and it is widely used to treat hypertension and heart failure. This drug is a prodrug that rapidly converts to candesartan after oral administration. Candesartan is metabolized by cytochrome P450 2C9 (CYP2C9) enzyme or uridine diphosphate glucurinosyltransferase 1A3, or excreted in an unchanged form through urine, biliary tract and feces. We investigated the effect of genetic polymorphism of CYP2C9 enzyme on drug pharmacokinetics using physiologically based pharmacokinetic (PBPK) modeling. In addition, by introducing the age and ethnicity into the model, we developed a model that can propose an appropriate dosage regimen taking into account the individual characteristics of each patient. To evaluate the suitability of the model, the results of a clinical trial on twenty-two healthy Korean subjects and their CYP2C9 genetic polymorphism data was applied. In this study, PK-Sim® was used to develop the PBPK model of candesartan.

Physiologically based pharmacokinetic (PBPK) modeling for prediction of celecoxib pharmacokinetics according to CYP2C9 genetic polymorphism.

Celecoxib is a non-steroidal anti-inflammatory drug (NSAID) and a representative selective cyclooxygenase (COX)-2 inhibitor, which is commonly prescribed for osteoarthritis, rheumatoid arthritis, ankylosing spondylitis, acute pain, and primary dysmenorrhea. It is mainly metabolized by CYP2C9 and partly by CYP3A4 after oral administration. Many studies reported that CYP2C9 genetic polymorphism has significant effects on the pharmacokinetics of celecoxib and the occurrence of adverse drug reactions. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model of celecoxib according to CYP2C9 genetic polymorphism for personalized pharmacotherapy. Initially, a clinical pharmacokinetic study was conducted where a single dose (200 mg) of celecoxib was administered to 39 healthy Korean subjects with CYP2C9*1/*1 or CYP2C9*1/*3 genotypes to obtain data for PBPK development. Based on the conducted pharmacokinetic study and a previous pharmacokinetic study involving subjects with CYP2C9*1/*13 and CYP2C9*3/*3 genotype, PBPK model for celecoxib was developed. A PBPK model for CYP2C9*1/*1 genotype group was developed and then scaled to other genotype groups (CYP2C9*1/*3, CYP2C9*1/*13 and CYP2C9*3/*3). After model development, model validation was performed with comparison of five pharmacokinetic studies. As a result, the developed PBPK model of celecoxib successfully described the pharmacokinetics of each CYP2C9 genotype group and its predicted values were within the acceptance criterion. Additionally, all the predicted values were within two-fold error range in comparison to the previous pharmacokinetic studies. This study demonstrates the possibility of determining the appropriate dosage of celecoxib for each individual through the PBPK modeling with CYP2C9 genomic information. This approach could contribute to the reduction of adverse drug reactions of celecoxib and enable precision medicine.

Effects of paroxetine on the pharmacokinetics of atomoxetine and its metabolites in different CYP2D6 genotypes.

The aim of this study was to investigate the effects of paroxetine, a potent inhibitor of CYP2D6, on the pharmacokinetics of atomoxetine and its two metabolites, 4-hydroxyatomoxetine and N-desmethylatomoxetine, in different CYP2D6 genotypes. Twenty-six healthy subjects were recruited and divided into CYP2D6*wt/*wt (*wt=*1 or *2, n = 10), CYP2D6*wt/*10 (n = 9), and CYP2D6*10/*10 groups (n = 7). In atomoxetine phase, all subjects received a single oral dose of atomoxetine (20 mg). In paroxetine phase, after administration of a single oral dose of paroxetine (20 mg) for six consecutive days, all subjects received a single oral dose of atomoxetine with paroxetine. Plasma concentrations of atomoxetine and its metabolites were determined up to 24 h after dosing. During atomoxetine phase, there were significant differences in Cmax and AUC0-24 of atomoxetine and N-desmethylatomoxetine among three genotype groups, whereas significant differences were not found in relation to CYP2D6*10 allele after administration of paroxetine. AUC ratios of 4-hydroxyatomoxetine and N-desmethylatomoxetine to atomoxetine were significantly different among three genotype groups during atomoxetine phase (all, P < 0.001), but after paroxetine treatment significant differences were not found. After paroxetine treatment, AUC0-24 of atomoxetine was increased by 2.3-, 1.7-, and 1.3-fold, in CYP2D6*wt/*wt, CYP2D6*wt/*10, and CYP2D6*10/*10 groups in comparison to atomoxetine phase, respectively. AUC ratio of 4-hydroxyatomoxetine to atomoxetine in each group was significantly decreased, whereas AUC ratio of N-desmethylatomoxetine to atomoxetine significantly increased after administration of paroxetine. In conclusion, paroxetine coadministration significantly affected pharmacokinetic parameters of atomoxetine and its two metabolites, 4-hydroxyatomoxetine and N-desmethylatomoxetine. When atomoxetine was administered alone, Cmax, AUC0-24 and CL/F of atomoxetine were significantly different among the three CYP2D6 genotype groups. However, after paroxetine coadministration, no significant differences in these pharmacokinetic parameters were observed among the CYP2D6 genotype groups.