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

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.

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.

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 to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes

Irbesartan, a potent and selective angiotensin II type-1 (AT1) receptor blocker (ARB), is one of the representative medications for the treatment of hypertension. Cytochrome P450 (CYP) 2C9 is primarily involved in the oxidation of irbesartan. CYP2C9 is highly polymorphic, and genetic polymorphism of this enzyme is the leading cause of significant alterations in the pharmacokinetics of irbesartan. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes. The irbesartan PBPK model was established using the PK-Sim® software. Our previously reported pharmacogenomic data for irbesartan was leveraged in the development of the PBPK model and collected clinical pharmacokinetic data for irbesartan was used for the validation of the model. Physicochemical and ADME properties of irbesartan were obtained from previously reported data, predicted by the modeling software, or optimized to fit the observed plasma concentration-time profiles. Model evaluation was performed by comparing the predicted plasma concentration-time profiles and pharmacokinetic parameters to the observed results. Predicted plasma concentration-time profiles were visually similar to observed profiles. Predicted AUCinf in CYP2C9*1/*3 and CYP2C9*1/*13 genotypes were increased by 1.54- and 1.62-fold compared to CYP2C9*1/*1 genotype, respectively. All fold error values for AUC and Cmax in non-genotyped and CYP2C9 genotyped models were within the two-fold error criterion. We properly established the PBPK model of irbesartan in different CYP2C9 genotypes. It can be used to predict the pharmacokinetics of irbesartan for personalized pharmacotherapy in individuals of various races, ages, and CYP2C9 genotypes.

Effects of CYP2D6 and CYP2C19 genetic polymorphisms and cigarette smoking on the pharmacokinetics of tolperisone

Tolperisone, a muscle relaxant used for post-stroke spasticity, is metabolized to its main metabolite by CYP2D6 and to a lesser extent by CYP2C19 and CYP1A2. We investigated the effects of CYP2D6 and CYP2C19 genetic polymorphisms and cigarette smoking on tolperisone pharmacokinetics. A 150 mg oral dose of tolperisone was given to 184 healthy Korean subjects and plasma concentrations of tolperisone were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A 3.14-fold significant increase in AUC0-∞ was observed in the CYP2D6*10/*10 group compared with the CYP2D6*wt/*wt group, whereas a 3.59-fold increase in AUC0-∞ was observed in CYP2C19PMs compared to CYP2C19EMs. Smokers had a 38.5% decrease in AUC0-∞ when compared to non-smokers. When these effects were combined, CYP2D6*10/*10-CYP2C19PM-Non-smokers had a 25.9-fold increase in AUC0-∞ compared to CYP2D6*wt/*wt-CYP2C19EM-Smokers. Genetic polymorphisms of CYP2D6 and CYP2C19 and cigarette smoking independently and significantly affected tolperisone pharmacokinetics and these effects combined resulted in a much greater impact on tolperisone pharmacokinetics.

Liquid Chromatography-Tandem Mass Spectrometry Method for Detection and Quantification of Meloxicam and 5'-Carboxymeloxicam in Oral Fluid Samples

A sensitive, selective and particularly fast method of liquid chromatography-tandem mass spectrometry (LC-MS/MS) was developed and validated for the determination of meloxicam and its main metabolite, 5'-carboxymeloxicam, in oral fluid samples. Meloxicam and its major metabolite were separated using a Shim-Pack XR-ODS 75 L × 2.0 column and C18 pre-column at 40 °C using a mixture of methanol and 10 mM ammonium acetate (80:20, v/v) with an injection flow rate of 0.3 mL/min. The total time of the analytical run was 5 min. Sixteen volunteers had oral fluid samples collected sequentially before and after taking a meloxicam tablet (15 mg) for up to 96 h. With the concentrations obtained, the pharmacokinetic parameters were determined using the Phoenix WinNonlin software. The parameters evaluated for meloxicam and 5'-carboxymeloxicam in the oral fluid samples showed linearity, accuracy, precision, medium-quality control (MQC-78.12 ng/mL), high-quality control (HQC-156.25 ng/mL), lower limits of quantification (LLOQ-0.6103 ng/mL), low-quality control (LQC-2.44 ng/mL), stability and dilution. Prostaglandin E₂ (PGE₂) was also detected and quantified in the oral fluid samples, demonstrating the possibility of a pharmacokinetic/pharmacodynamic (PK/PD) study with this methodology. All the parameters evaluated in the validation of the methodology in the oral fluid samples proved to be stable and within the possible variations in each of the described parameters. Through the data presented, the possibility of a PK/PD study was demonstrated, detecting and quantifying meloxicam, its main metabolite and PGE₂ in oral fluid samples using LC-MS/MS.

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 AUC_0-∞ (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 AUC_0-∞ 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 AUC_0-∞ (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 CYP2C19 genetic polymorphism on the pharmacokinetics of tolperisone in healthy subjects

Tolperisone hydrochloride is a centrally-acting muscle relaxant used for relieving spasticities of neurological origin and muscle spasms associated with painful locomotor diseases. It is metabolized to the inactive metabolite mainly by CYP2D6 and, to a lesser extent, by CYP2C19 and CYP1A2. In our previous study, the pharmacokinetics of tolperisone was significantly affected by the genetic polymorphism of CYP2D6, but the wide interindividual variation of tolperisone pharmacokinetics was not explained by genetic polymorphism of CYP2D6 alone. Thus, we studied the effects of CYP2C19 genetic polymorphism on tolperisone pharmacokinetics. Eighty-one subjects with different CYP2C19 genotypes received a single oral dose of 150 mg tolperisone with 240 mL of water, and blood samples were collected up to 12 h after dosing. The plasma concentration of tolperisone was measured by a liquid chromatography-tandem mass spectrometry system. The CYP2C19PM group had significantly higher C_max and lower CL/F values than the CYP2C19EM and CYP2C19IM groups. The AUC_inf of the CYP2C19PM group was 2.86-fold and 3.00-fold higher than the CYP2C19EM and CYP2C19IM groups, respectively. In conclusion, the genetic polymorphism of CYP2C19 significantly affected tolperisone pharmacokinetics.

Anti-Inflammatory, Neurotrophic, and Cytotoxic Oxylipins Isolated from Chaenomeles sinensis Twigs

Oxylipins are important biological molecules with diverse roles in human and plants such as pro-/anti-inflammatory, antimicrobial, and regulatory activity. Although there is an increasing number of plant-derived oxylipins, most of their physiological roles in humans remain unclear. Here, we describe the isolation, identification, and biological activities of four new oxylipins, chaenomesters A-D (1-4), along with a known compound (5), obtained from Chaenomeles sinensis twigs. Their chemical structures were determined by spectroscopic (i.e., NMR) and spectrometric (i.e., HRMS) data analysis including ¹H NMR-based empirical rules and homonuclear-decoupled ¹H NMR experiments. Chaenomester D (4), an omega-3 oxylipin, showed a potent inhibitory effect on nitric oxide (NO) production in lipopolysaccharide (LPS)-activated BV-2 cells (NO production, 8.46 ± 0.68 μM), neurotrophic activity in C6 cells through the induction of the secretion of nerve growth factor (NGF, 157.7 ± 2.4%), and cytotoxicity in A549 human cancer cell lines (IC₅₀ = 27.4 μM).

Predicting the Pharmacokinetics of Orally Administered Drugs across BCS Classes 1-4 by Virtual Bioequivalence Model

To evaluate the influence of solubility and permeability on the pharmacokinetic prediction performance of orally administered drugs using avirtual bioequivalence (VBE) model, a total of 23 orally administered drugs covering Biopharmaceutics Classification System (BCS) classes 1-4 were selected. A VBE model (i.e., a physiologically based pharmacokinetic model integrated with dissolution data) based on a B²O simulator was applied for pharmacokinetic (PK) prediction in a virtual population. Parameter sensitivity analysis was used for input parameter selection. The predictive performances of PK parameters (i.e., AUC_0-t, C_max, and T_max), PK profiles, and bioequivalence (BE) results were evaluated using the twofold error, average fold error (AFE), absolute average fold error (AAFE), and BE reassessment metrics. All models successfully simulated the mean PK profiles, with AAFE < 2 and AFE ranging from 0.58 to 1.66. As for the PK parameters, except for the time of peak concentration, T_max, of isosorbide mononitrate, other simulated PK parameters were all within a twofold error. The simulated PK behaviors were comparable to the observed ones, both for test (T) and reference (R) products, and the simulated T/R arithmetic mean ratios were all within 0.88-1.16 of the observed values. These four evaluation metrics were distributed equally among BCS class 1-4 drugs. The VBE model showed powerful performance to predict the PK behavior of orally administered drugs with various combinations of solubility and permeability, irrespective of the BCS category.

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 C_max and lower CL/F values than the CYP2D6*wt/*wt group. The AUC_inf 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 C_max 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.

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.

CYP2C9 Polymorphism Influence in PK/PD Model of Naproxen and 6-O-Desmethylnaproxen in Oral Fluid

Polymorphisms in CYP2C9 can significantly interfere with the pharmacokinetic (PK) and pharmacodynamic (PD) parameters of nonsteroidal anti-inflammatory drugs (NSAIDs), including naproxen. The present research aimed to study the PK/PD parameters of naproxen and its metabolite, 6-O-desmethylnaproxen, associated with allelic variations of CYP2C9. In our study, a rapid, selective, and sensitive Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) method was developed and validated for the determination of naproxen and its main metabolite, 6-O-desmethylnaproxen, in oral fluid. Naproxen and its main metabolite were separated using a Shim-Pack XR-ODS 75L × 2.0 column and C18 pre-column at 40 °C using a mixture of methanol and 10 mM ammonium acetate (70:30, v/v), with an injection flow of 0.3 mL/min. The total analytical run time was 3 min. The volunteers, previously genotyped for CYP2C9 (16 ancestral—CYP2C9 *1 and 12 with the presence of polymorphism—CYP2C9 *2 or *3), had their oral fluids collected sequentially before and after taking a naproxen tablet (500 mg) at the following times: 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5, 6 8, 11, 24, 48, 72 and 96 h. Significant differences in the PK parameters (* p < 0.05) of naproxen in the oral fluid were: Vd/F (L): 98.86 (55.58−322.07) and 380.22 (261.84−1097.99); Kel (1/h): 0.84 (0.69−1.34) and 1.86 (1.09−4.06), in ancestral and mutated CYP2C9 *2 and/or *3, respectively. For 6-O-desmethylnaproxen, no PK parameters were significantly different between groups. The analysis of prostaglandin E2 (PGE2) proved to be effective and sensitive for PD parameters analysis and showed higher levels in the mutated group (p < 0.05). Both naproxen and its main metabolite, 6-O-desmethylnaproxen, and PGE2 in oral fluid can be effectively quantified using LC-MS/MS after a 500 mg oral dose of naproxen. Our method proved to be effective and sensitive to determine the lower limit of quantification of naproxen and its metabolite, 6-O-desmethylnaproxen, in oral fluid (2.4 ng/mL). All validation data, such as accuracy, precision, and repeatability intra- and inter-assay, were less than 15%. Allelic variations of CYP2C9 may be considered relevant in the PK of naproxen and its main metabolite, 6-O-desmethylnaproxen.

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 (k_cat) 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 AUC_inf 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 AUC_inf, C_max, and t_1/2 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.

Uncovering the effects and mechanism of Danggui Shaoyao San intervention on primary dysmenorrhea by serum metabolomics approach

Danggui Shaoyao San (DSS) is a well-known prescription for relieving primary dysmenorrhea (PD) of women in China. However, its pharmacological mechanism has not been thoroughly uncovered. Here, an integrative UPLC-Q-TOF-MS-based serum metabolomics approach coupled with multivariate data analysis has been proposed to investigate the effects and mechanism of DSS on estradiol benzoate and oxytocin-induced PD rats. 31 potential biomarkers of PD were screened and identified, mainly involving phenylalanine, tyrosine and tryptophan biosynthesis, glycerophospholipid metabolism, primary bile acid biosynthesis, and the occurrence of PD could destroy biological homeostasis in vivo by monitoring these pathways. After DSS treatment, 18 identified different metabolites were restored to the nomal state in varying degrees and could be potential biomarkers contributing to the treatment of DSS. These findings implyed that DSS exhibited a therapeutic effect on PD rats through regulating multiple abnormal pathways. Of note, this study discovered some potential biomarkers related to PD for the first time, such as L-tyrosine, glycocholic acid, citric acid, palmitoylcarnitine, cholesterol. It preliminarily proved the pathophysiology of PD and action mechanisms of DSS on PD, and provided a novel insight into the effectiveness of DSS on PD.

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

Metoprolol, a selective β₁-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 (K_m, V_max, and CL_int) 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.

Pharmacogenetics and Pain Treatment with a Focus on Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Antidepressants: A Systematic Review

Background: This systematic review summarizes the impact of pharmacogenetics on the effect and safety of non-steroidal anti-inflammatory drugs (NSAIDs) and antidepressants when used for pain treatment. Methods: A systematic literature search was performed according to the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines regarding the human in vivo efficacy and safety of NSAIDs and antidepressants in pain treatment that take pharmacogenetic parameters into consideration. Studies were collected from PubMed, Scopus, and Web of Science up to the cutoff date 18 October 2021. Results: Twenty-five articles out of the 6547 initially detected publications were identified. Relevant medication–gene interactions were noted for drug safety. Interactions important for pain management were detected for (1) ibuprofen/CYP2C9; (2) celecoxib/CYP2C9; (3) piroxicam/CYP2C8, CYP2C9; (4) diclofenac/CYP2C9, UGT2B7, CYP2C8, ABCC2; (5) meloxicam/CYP2C9; (6) aspirin/CYP2C9, SLCO1B1, and CHST2; (7) amitriptyline/CYP2D6 and CYP2C19; (8) imipramine/CYP2C19; (9) nortriptyline/CYP2C19, CYP2D6, ABCB1; and (10) escitalopram/HTR2C, CYP2C19, and CYP1A2. Conclusions: Overall, a lack of well powered human in vivo studies assessing the pharmacogenetics in pain patients treated with NSAIDs or antidepressants is noted. Studies indicate a higher risk for partly severe side effects for the CYP2C9 poor metabolizers and NSAIDs. Further in vivo studies are needed to consolidate the relevant polymorphisms in NSAID safety as well as in the efficacy of NSAIDs and antidepressants in pain management.

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 (k_cat) 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 AUC_inf 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, C_max, and t_1/2 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.

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 AUC_0-∞ 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 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 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 k_cat 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 (AUC_inf) 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 (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.