Metabolomic Profiles in Patients with Cervical Cancer Undergoing Cisplatin and Radiation Therapy.

This study was aimed to evaluate endogenous metabolic changes before and after cisplatin and radiation therapy in patients with cervical cancer via untargeted metabolomic analysis using plasma samples. A total of 13 cervical cancer patients were enrolled in this study. Plasma samples were collected from each patient on two occasions: approximately one week before therapy (P1) and after completion of cisplatin and radiation therapy (P2). Of the 13 patients, 12 patients received both cisplatin and radiation therapy, whereas one patient received radiation therapy alone. The samples were analyzed using the Ultimate 3000 coupled with Q ExactiveTM Focus Hybrid Quadrupole-OrbitrapTM mass spectrometry (Thermo Fisher Scientific, Waltham, MA, USA). Chromatographic separation utilized a Kinetex C18 column 2.1×100 mm (2.6 µm) (Phenomenex, Torrance, CA, USA), and the temperature was maintained at 40°C. Following P2, there were statistically significant increases in the concentrations of indoxyl sulfate, phenylacetylglutamine, Lysophosphatidyethanolamine (LysoPE) (18:1), and indole-3-acetic acid compared with the concentrations observed at P1. Specifically, in the human papillomavirus (HPV) noninfection group, indoxyl sulfate, LysoPE (18:1), and phenylacetylglutamine showed statistically significant increases at P2 compared with P1. No significant changes in metabolite concentrations were observed in the HPV infection group. Indoxyl sulfate, LysoPE (18:1), phenylacetylglutamine, and indole-3-acetic acid were significantly increased following cisplatin and radiation therapy.

Primary and secondary prevention of stroke and cardiovascular events using evolocumab and alirocumab: Meta-analysis of randomized controlled trials.

OBJECTIVES: Although the clinical role of protein convertase subtilisin kexin type 9 (PCSK9) inhibitors for cholesterol management is increasing, the post-marketing period of use is short compared to other lipid-lowering drugs, so there is still insufficient evidence for their efficacy and safety. In this meta-analysis, we evaluated preventive effects of stroke and cardiovascular (CV) events using evolocumab and alirocumab. MATERIALS AND METHODS: We assessed the relative risk of stroke and CV events after alirocumab or evolocumab treatment in individuals with or without clinical CV diseases compared with that in controls. In addition, we evaluated the relative risk of hemorrhagic stroke. RESULTS: A total of 25 articles were included (median of study duration = 52 weeks). The risk of stroke was significantly decreased after treatment with alirocumab or evolocumab (primary prevention in patients without CV diseases: RR = 0.733; 95% CI, 0.618 - 0.870; secondary prevention in patients with CV diseases: RR = 0.703; 95% CI, 0.562 - 0.880). The risk of CV events also significantly decreased in patients treated with alirocumab or evolocumab (primary prevention: RR = 0.818; 95% CI, 0.777 - 0.861; secondary prevention: RR = 0.725; 95% CI, 0.578 - 0.910). The relative risk of hemorrhagic stroke was not significantly different between PCSK9 inhibitor-treated patients and controls (RR = 1.041; 95% CI, 0.690 - 1.573). CONCLUSION: Our findings indicate that evolocumab and alirocumab are significantly effective without increasing the risk of hemorrhagic stroke. Based on this, the PCSK9 inhibitors can be highly recommended for cholesterol management.

Profiling of endogenous metabolites and changes in intestinal microbiota distribution after GEN-001 (Lactococcus lactis) administration.

This study aimed to identify metabolic biomarkers and investigate changes in intestinal microbiota in the feces of healthy participants following administration of Lactococcus lactis GEN-001. GEN-001 is a single-strain L. lactis strain isolated from the gut of a healthy human volunteer. The study was conducted as a parallel, randomized, phase 1, open design trial. Twenty healthy Korean males were divided into five groups according to the GEN-001 dosage and dietary control. Groups A, B, C, and D1 received 1, 3, 6, and 9 GEN-001 capsules (1 × 1011 colony forming units), respectively, without dietary adjustment, whereas group D2 received 9 GEN-001 capsules with dietary adjustment. All groups received a single dose. Fecal samples were collected 2 days before GEN-001 administration to 7 days after for untargeted metabolomics and gut microbial metagenomic analyses; blood samples were collected simultaneously for immunogenicity analysis. Levels of phenylalanine, tyrosine, cholic acid, deoxycholic acid, and tryptophan were significantly increased at 5-6 days after GEN-001 administration when compared with predose levels. Compared with predose, the relative abundance (%) of Parabacteroides and Alistipes significantly decreased, whereas that of Lactobacillus and Lactococcus increased; Lactobacillus and tryptophan levels were negatively correlated. A single administration of GEN-001 shifted the gut microbiota in healthy volunteers to a more balanced state as evidenced by an increased abundance of beneficial bacteria, including Lactobacillus, and higher levels of the metabolites that have immunogenic properties.

Drug-Drug interactions of docetaxel in patients with breast cancer based on insurance claims data.

Despite an increase in the use of targeted anticancer drugs and immunotherapy, cytotoxic anticancer drugs such as docetaxel continue to play a clinically important role. The aim of this study was to evaluate drug-drug interactions between docetaxel and coadministered medicines in patients with breast cancer a claims database. The Health Insurance Review and Assessment Service (HIRA) database (2017 to 2019) was used in this study. We evaluated the risk of neutropenia (defined using receipt of granulocyte colony-stimulating factor (G-CSF) prescriptions) under docetaxel administration or the coadministration of docetaxel and an interacting anticancer drug (predefined based on approval information obtained from the Korean Ministry of Food and Drug Safety and the Lexicomp electronic database). The propensity score matching method was applied to balance covariates in the case (patients with G-CSF prescriptions) and control (patients without G-CSF prescriptions) groups. We identified 947 female patients with breast cancer prescribed with docetaxel and excluded 321 patients based on inclusion criteria. Of the remaining 626 patients, 280 were assigned to the case group and 346 to the control group. Predefined drugs were coadministered to 71 (11.3%) patients during the 7-day period before and after the administration of docetaxel. Adjusted odds ratios (ORs) calculated using the logistic regression model applied to the propensity score matching showed no significant difference between the administration of docetaxel alone and docetaxel coadministration (adjusted OR, 2.010; 95% confidence interval, 0.906, 4.459). In conclusion, we suggest that coadministration of docetaxel and a predefined interacting drug are not associated with G-CSF prescription.

Strong inhibition of organic cation transporter 2 by flavonoids and attenuation effects on cisplatin-induced cytotoxicity.

Organic cation transporter 2 (OCT2) is predominantly expressed in the basolateral membrane of renal proximal tubule cells and contributes to the renal excretion of various drugs such as metformin, cisplatin, oxaliplatin, cimetidine, and lamivudine. Cisplatin, an anticancer agent for various cancers, is a substrate of OCT2, and cisplatin-induced nephrotoxicity is in part attributed to OCT2 activity in the kidney, which increases the renal accumulation of cisplatin. In this study, we aimed to identify flavone derivatives with strong inhibitory effects on OCT2 transport. Among the 80 flavonoids tested, 24 showed moderate to strong inhibitory effects against OCT2 transport activity. The IC50 values were less than 5 µM for 10 flavonoids. All 10 compounds alleviated cisplatin-induced cytotoxicity in cells expressing OCT2, even though the magnitude of the effects varied depending on the functional moieties in each position. Multiple factor analysis revealed that the methyl group at the R1 position and methoxy group at the R6 position of the flavonol backbone are important for OCT2 inhibition. Information on the functional moieties in the flavonol backbone would help develop effective OCT2 inhibitors by providing a structural association with OCT2 inhibitory effects. In addition, the compounds with strong inhibitory effects on OCT2 identified in this study may be potential candidates for clinical use to mitigate cisplatin-induced nephrotoxicity.

Prediction of the Drug-Drug Interaction Potential between Tegoprazan and Amoxicillin/Clarithromycin Using the Physiologically Based Pharmacokinetic and Pharmacodynamic Model.

Tegoprazan is a novel potassium-competitive acid blocker. This study investigated the effect of drug-drug interaction on the pharmacokinetics and pharmacodynamics of tegoprazan co-administered with amoxicillin and clarithromycin, the first-line therapy for the eradication of Helicobacter pylori, using physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) modeling. The previously reported tegoprazan PBPK/PD model was modified and applied. The clarithromycin PBPK model was developed based on the model provided by the SimCYP® compound library. The amoxicillin model was constructed using the middle-out approach. All of the observed concentration-time profiles were covered well by the predicted profiles with the 5th and 95th percentiles. The mean ratios of predicted to observed PK parameters, including the area under the curve (AUC), maximum plasma drug concentration (Cmax), and clearance, were within the 30% intervals for the developed models. Two-fold ratios of predicted fold-changes of Cmax and AUC from time 0 to 24 h to observed data were satisfied. The predicted PD endpoints, including median intragastric pH and percentage holding rate at pH above 4 or 6 on day 1 and day 7, were close to the corresponding observed data. This investigation allows evaluation of the effects of CYP3A4 perpetrators on tegoprazan PK and PD changes, thus providing clinicians with the rationale for co-administration dosing adjustment.

Application of tree-based machine learning classification methods to detect signals of fluoroquinolones using the Korea Adverse Event Reporting System (KAERS) database.

BACKGROUND: Safety issues for fluoroquinolones have been provided by regulatory agencies. This study was conducted to identify signals of fluoroquinolones reported in the Korea Adverse Event Reporting System (KAERS) using tree-based machine learning (ML) methods. RESEARCH DESIGN AND METHODS: All adverse events (AEs) associated with the target drugs reported in the KAERS from 2013 to 2017 were matched with drug label information. A dataset containing label-positive and -negative AEs was arbitrarily divided into training and test sets. Decision tree, random forest (RF), bagging, and gradient boosting machine (GBM) were fitted on the training set with hyperparameters tuned using five-fold cross-validation and applied to the test set. The ML method with the highest area under the curve (AUC) scores was selected as the final ML model. RESULTS: Bagging was selected as the final ML model for gemifloxacin (AUC score: 1) and levofloxacin (AUC: 0.9987). RF was selected in ciprofloxacin, moxifloxacin, and ofloxacin (AUC scores: 0.9859, 0.9974, and 0.9999 respectively). We found that the final ML methods detected additional signals that were not detected using the disproportionality analysis (DPA) methods. CONCLUSIONS: The bagging-or-RF-based ML methods performed better than DPA and detected novel AE signals previously unidentified using the DPA methods.

Incorporation of real-world data to a clinical trial: use of external controls.

As real-world data (RWD) becomes more available and the methodology for handling RWD evolves, the use of RWD in drug development and drug approval is drawing interest. One of the ways RWD can be applied to a clinical trial is using an external control, a cohort of patients established separately serving as a control group for the clinical trial's treatment group. Although external controls have the possibility of bias as a result of differences in baseline characteristics between the external control and experimental groups, selecting an appropriate data source and ensuring comparability through proper handling of the data can increase the utility of external controls, raising the efficiency of drug development. This article discusses several topics relevant to using external controls in clinical trials, including the definition of external control, the selection of data sources, the strategy ensuring comparability, current regulatory circumstances, and future directions.

A compatibility evaluation between the physiologically based pharmacokinetic (PBPK) model and the compartmental PK model using the lumping method with real cases.

Pharmacokinetic (PK) modeling is a useful method for investigating drug absorption, distribution, metabolism, and excretion. The most commonly used mathematical models in PK modeling are the compartment model and physiologically based pharmacokinetic (PBPK) model. Although the theoretical characteristics of each model are well known, there have been few comparative studies of the compatibility of the models. Therefore, we evaluated the compatibility of PBPK and compartment models using the lumping method with 20 model compounds. The PBPK model was theoretically reduced to the lumped model using the principle of grouping tissues and organs that show similar kinetic behaviors. The area under the concentration-time curve (AUC) based on the simulated concentration and PK parameters (drug clearance [CL], central volume of distribution [Vc], peripheral volume of distribution [Vp]) in each model were compared, assuming administration to humans. The AUC and PK parameters in the PBPK model were similar to those in the lumped model within the 2-fold range for 17 of 20 model compounds (85%). In addition, the relationship of the calculated Vd/fu (volume of distribution [Vd], drug-unbound fraction [fu]) and the accuracy of AUC between the lumped model and compartment model confirmed their compatibility. Accordingly, the compatibility between PBPK and compartment models was confirmed by the lumping method. This method can be applied depending on the requirement of compatibility between the two models.

Bioanalytical methods for the detection of duloxetine and thioctic acid in plasma using ultra performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS).

Duloxetine and thioctic acid (TA) are standard drugs for treating diabetic neuropathy, a primary complication associated with diabetes. In this study, ultra performance liquid chromatography coupled with tandem mass spectrometry methods was successfully developed and validated for quantifying duloxetine and TA in biological samples. The protein precipitation method was used to extract duloxetine, TA and their internal standards from beagle dog plasma. A Hypersil Gold C18 column (150 × 2.1 mm, 1.9 µm) was used for the experiment. Isocratic elution with 0.1% formic acid in acetonitrile (A) and 0.1% formic acid (B) was used for duloxetine, whereas a gradient elution with 0.03% acetic acid (A) and acetonitrile (B) was used for TA. The validated parameters included linearity, sensitivity, accuracy, precision, selectivity, matrix effect, stability, and recovery under different conditions. The linear ranges of the calibration curves for duloxetine and TA were 5-800 ng/mL and 5-1,000 ng/mL, respectively. An intra- and inter-run precision of ± 15% can be observed in all quality control samples. These methods were successfully used for pharmacokinetics (PKs) studies in beagle dogs to compare PK differences in a fixed-dose combination including duloxetine and TA and co-administration of the 2 drugs.

Integration of a Physiologically Based Pharmacokinetic and Pharmacodynamic Model for Tegoprazan and Its Metabolite: Application for Predicting Food Effect and Intragastric pH Alterations.

A physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model for tegoprazan and its major metabolite M1 was developed to predict PK and PD profiles under various scenarios. The PBPK model for tegoprazan and M1 was developed and predicted using the SimCYP® simulator and verified using clinical study data obtained after a single administration of tegoprazan. The established PBPK/PD model was used to predict PK profiles after repeated administrations of tegoprazan, postprandial PK profiles, and intragastric pH changes. The predicted tegoprazan and M1 concentration-time profiles fit the observed profiles well. The arithmetic mean ratios (95% confidence intervals) of the predicted to observed values for the area under the curve (AUC0-24 h), maximum plasma drug concentration (Cmax), and clearance (CL) for tegoprazan and M1 were within a 30% interval. Delayed time of maximum concentration (Tmax) and decreased Cmax were predicted in the postprandial PK profiles compared with the fasted state. This PBPK/PD model may be used to predict PK profiles after repeated tegoprazan administrations and to predict differences in physiological factors in the gastrointestinal tract or changes in gastric acid pH after tegoprazan administration.

Comparison of Efficacy and Safety of Statin-Ezetimibe Combination Therapy with Statin Monotherapy in Patients with Diabetes: A Meta-Analysis of Randomized Controlled Studies.

INTRODUCTION: Dyslipidemia in diabetes mellitus is characterized by hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), and elevated low-density lipoprotein cholesterol (LDL-C). Additionally, the potentially increased risk of morbidity and mortality following atherosclerotic cardiovascular diseases should be considered in the treatment of dyslipidemia in patients with diabetes. METHODS: We performed a meta-analysis of the published data to compare the effects of HMG-CoA reductase inhibitor (statin)-ezetimibe combination therapy and statin monotherapy on lipid and glucose parameters in patients with diabetes. We also compared safety based on the adverse events reported for the two groups. RESULTS: In total, 17 articles were included in this meta-analysis. In the efficacy assessment, the combination treatment afforded a significantly greater reduction in LDL-C than did statin monotherapy (standard difference in means 0.691; 95% confidence interval 0.534-0.847). A significantly greater improvement effect was observed in the levels of HDL-C, total cholesterol, triglyceride, and apolipoprotein B, but not apolipoprotein A1, with combination therapy than with statin monotherapy. Additionally, combination therapy reduced fasting blood glucose levels more significantly than did statin monotherapy. In terms of safety, there were no significant differences in treatment-related adverse events between the two treatments. CONCLUSIONS: Statin-ezetimibe combination therapy enhances levels of LDL-C and other lipids without increasing the risk of adverse events compared with statin monotherapy. The present meta-analysis presents valid evidence for appropriate drug regimens to treat dyslipidemia in patients with diabetes. REGISTRATION: PROSPERO Identifier Number CRD42021244578.

Predicting the systemic exposure and lung concentration of nafamostat using physiologically-based pharmacokinetic modeling.

Nafamostat has been actively studied for its neuroprotective activity and effect on various indications, such as coronavirus disease 2019 (COVID-19). Nafamostat has low water solubility at a specific pH and is rapidly metabolized in the blood. Therefore, it is administered only intravenously, and its distribution is not well known. The main purposes of this study are to predict and evaluate the pharmacokinetic (PK) profiles of nafamostat in a virtual healthy population under various dosing regimens. The most important parameters were assessed using a physiologically based pharmacokinetic (PBPK) approach and global sensitivity analysis with the Sobol sensitivity analysis. A PBPK model was constructed using the SimCYP® simulator. Data regarding the in vitro metabolism and clinical studies were extracted from the literature to assess the predicted results. The model was verified using the arithmetic mean maximum concentration (Cmax), the area under the curve from 0 to the last time point (AUC0-t), and AUC from 0 to infinity (AUC0-∞) ratio (predicted/observed), which were included in the 2-fold range. The simulation results suggested that the 2 dosing regimens for the treatment of COVID-19 used in the case reports could maintain the proposed effective concentration for inhibiting severe acute respiratory syndrome coronavirus 2 entry into the plasma and lung tissue. Global sensitivity analysis indicated that hematocrit, plasma half-life, and microsomal protein levels significantly influenced the systematic exposure prediction of nafamostat. Therefore, the PBPK modeling approach is valuable in predicting the PK profile and designing an appropriate dosage regimen.

Prediction of Fluoxetine and Norfluoxetine Pharmacokinetic Profiles Using Physiologically Based Pharmacokinetic Modeling.

Fluoxetine is a selective serotonin reuptake inhibitor that is metabolized to norfluoxetine by cytochrome P450 (CYP) 2D6, CYP2C19, and CYP3A4. A physiologically based pharmacokinetic model for fluoxetine and norfluoxetine metabolism was developed to predict and investigate changes in concentration-time profiles according to fluoxetine dosage in the Korean population. The model was developed based on the Certara repository model and information gleaned from the literature. Digitally extracted clinical study data were used to develop and verify the model. Simulations for plasma concentrations of fluoxetine and norfluoxetine after a single dose of 60 or 80 mg fluoxetine were made based on 1000 virtual healthy Korean individuals using the SimCYP version 19 simulator. The mean ratios (simulated/observed) after a single administration of 80 mg fluoxetine for maximum plasma concentration, area under the plasma concentration-time curve, and apparent clearance were 1.12, 1.08, and 0.93 for fluoxetine; the ratios of maximum plasma concentration and area under the plasma concentration-time curve were 1.08 and 1.08, respectively, for norfluoxetine, indicating that the simulated concentration-time profiles of fluoxetine and norfluoxetine fitted the observed profiles well. The developed model was used to predict plasma fluoxetine and norfluoxetine concentration-time profiles after repeated administrations of fluoxetine in Korean volunteers. This physiologically based pharmacokinetic model could provide basic understanding of the pharmacokinetic profiles of fluoxetine and its metabolite under various situations.

Physiologically-based pharmacokinetic model for clozapine in Korean patients with schizophrenia.

Clozapine has been used as a treatment of schizophrenia. Despite its large interindividual variability, few reports addressed the physiologically-based pharmacokinetic modeling and simulation (PBPK M&S) of clozapine in patients. This study aimed to develop a PBPK M&S of clozapine in Korean patients with schizophrenia. PBPK modeling for clozapine was constructed using a population-based PBPK platform, the SimCYP® Simulator (V19; Certara, Sheffield, UK). The PBPK model was developed by optimizing the physiological parameters of the built-in population and compound libraries in the SimCYP® Simulator. The model verification was performed with the predicted/observed ratio for pharmacokinetic parameters and visual predictive checks (VPCs) plot. Simulations were performed to predict toxicities according to dosing regimens. From published data, 230 virtual trials were simulated for each dosing regimen. The predicted/observed ratio for the area under the curve and peak plasma concentration was calculated to be from 0.78 to 1.34. The observation profiles were within the 5th and 95th percentile range with no serious model misspecification through the VPC plot. A significant impact on age and gender was found for clozapine clearance. The simulation results suggested that 150 mg twice a day and 150 mg three times a day of clozapine have toxicity concerns. In conclusion, a PBPK model was developed and reasonable parameters were made from the data of Korean patients with schizophrenia. The provided model might be used to predict the pharmacokinetics of clozapine and assist dose adjustment in clinical settings.

Urinary Metabolomic Profiling after Administration of Corydalis Tuber and Pharbitis Seed Extract in Healthy Korean Volunteers.

Pharmacometabolomics is a useful tool to identify biomarkers that can assess and predict response after drug administration. The primary purpose of pharmacometabolomics is to better understand the mechanisms and pathways of a drug by searching endogenous metabolites that have significantly changed after drug administration. DA-9701, a prokinetic agent, consists of Pharbitis seed and Corydalis tube extract and it is known to improve the gastrointestinal motility. Although the overall mechanism of action of DA-9701 remains unclear, its active ingredients, corydaline and chlorogenic acid, act as a 5-HT3 and D2 receptor antagonist and 5-HT4 receptor agonist. To determine the significant metabolites after the administration of DA-9701, a qualitative analysis was carried out using ultra-high performance liquid chromatography coupled with orbitrap mass spectrometer followed by a multivariate analysis. Seven candidates were selected and a statistical analysis of fold change was performed over time. Our study concluded that all the seven selected metabolites were commonly involved in lipid metabolism and purine metabolism.

Determination of candesartan or olmesartan in hypertensive patient plasma using UPLC-MS/MS.

Candesartan and olmesartan are angiotensin II receptor blockers (ARBs) used for the treatment of hypertension and heart failure. Quantitation methods for candesartan and olmesartan were developed using ultra-high performance liquid chromatography-tandem mass spectrometry following protein precipitation. Candesartan was separated using 5 mM ammonium formate (A) and 100% acetonitrile (B) and olmesartan was separated using 2 mM ammonium formate with 0.1% formic acid (A) and 100% acetonitrile (B). Separation was performed using an isocratic method with a Thermo hypersil GOLD C18 column. Electrospray ionization was used for analyte ionization and detection of candesartan, olmesartan, and the internal standards by multiple reaction monitoring. Developed method showed excellent linearity (r > 0.99) in the concentration range of 2-500 ng/mL for candesartan and 5-2,500 ng/mL for olmesartan. Accuracies were 86.70-108.8% for candesartan and 87.87-112.6% for olmesartan. These methods were able to successfully measure plasma candesartan or olmesartan concentrations in hypertensive patients. This study can be used for pharmacokinetic studies of candesartan or olmesartan in humans.

Bioanalytical Method Using Ultra-High-Performance Liquid Chromatography Coupled with High-Resolution Mass Spectrometry (UHPL-CHRMS) for the Detection of Metformin in Human Plasma.

Metformin is the first-line medicine for the treatment of type 2 diabetes. Drug interactions between metformin and other drugs, food, or beverages cannot only cause changes in the pharmacokinetic profiles but also affect the efficacy of metformin. The purpose of this study was to develop a rapid and reliable bioanalytical method for the detection of plasma metformin concentration in humans. To remove interfering substances in plasma, acidified acetonitrile (acetonitrile containing 0.1% formic acid) was added to samples. Ultra-high-performance liquid chromatography (UHPLC) coupled with high resolution mass spectrometry (HRMS) was used to analyze metformin and its internal standard (metformin-d6). Analyte separation was performed on a BEH HILIC analytical column (100 × 2.1 mm, 1.7 µm) using a gradient elution of 0.1% formic acid (A) and acetonitrile with 0.1% formic acid (B). The total chromatographic run time was 2 min. The developed method was validated for its linearity, accuracy and precision, selectivity (signal of interfering substance; analyte, lower limit of quantification (LLOQ) ≤ 20%; IS, IS ≤ 5%), sensitivity (LLOQ, 5 ng/mL; S/N ratio ≥ 10), stability (low quality control (LQC, 15 ng/mL), 2.95-14.19%; high quality control (HQC, 1600 ng/mL), -9.49-15.10%), dilution integrity (diluted QC (4000 ng/mL); 10-folds diluted QC (400 ng/mL); 5-folds diluted QC (800 ng/mL); accuracy, 81.30-91.98%; precision, ≤4.47%), carry-over (signal of double blank; analyte, LLOQ ≤20%; IS, IS ≤5%), and matrix effect (LQC, 10.109%; HQC, 12.271%) under various conditions. The constructed calibration curves were shown linear in the concentration range of 5-2000 ng/mL, with within- and between-run precision values of <8.19% and accuracy in the range of 91.13-105.25%. The plasma metformin concentration of 16 healthy subjects was successfully measured by applying the validated bioanalytical method.

Evaluation of the Effect of CYP2D6 Genotypes on Tramadol and O-Desmethyltramadol Pharmacokinetic Profiles in a Korean Population Using Physiologically-Based Pharmacokinetic Modeling.

Tramadol is a µ-opioid receptor agonist and a monoamine reuptake inhibitor. O-desmethyltramadol (M1), the major active metabolite of tramadol, is produced by CYP2D6. A physiologically-based pharmacokinetic model was developed to predict changes in time-concentration profiles for tramadol and M1 according to dosage and CYP2D6 genotypes in the Korean population. Parallel artificial membrane permeation assay was performed to determine tramadol permeability, and the metabolic clearance of M1 was determined using human liver microsomes. Clinical study data were used to develop the model. Other physicochemical and pharmacokinetic parameters were obtained from the literature. Simulations for plasma concentrations of tramadol and M1 (after 100 mg tramadol was administered five times at 12-h intervals) were based on a total of 1000 virtual healthy Koreans using SimCYP® simulator. Geometric mean ratios (90% confidence intervals) (predicted/observed) for maximum plasma concentration at steady-state (Cmax,ss) and area under the curve at steady-state (AUClast,ss) were 0.79 (0.69-0.91) and 1.04 (0.85-1.28) for tramadol, and 0.63 (0.51-0.79) and 0.67 (0.54-0.84) for M1, respectively. The predicted time-concentration profiles of tramadol fitted well to observed profiles and those of M1 showed under-prediction. The developed model could be applied to predict concentration-dependent toxicities according to CYP2D6 genotypes and also, CYP2D6-related drug interactions.

Population pharmacokinetic analysis of tramadol and O-desmethyltramadol with genetic polymorphism of CYP2D6.

Aim: Tramadol is widely used to treat acute, chronic, and neuropathic pain. Its primary active metabolite, O-desmethyltramadol (M1), is mainly responsible for its µ-opioid receptor-related analgesic effect. Tramadol is metabolized to M1 mainly by the cytochrome P450 (CYP) 2D6 enzyme, and to other metabolites by CYP3A4 and CYP2B6. The aim of this study was to develop a population pharmacokinetic (PK) model of tramadol and its metabolite using healthy Korean subjects. Methods: Data on plasma concentrations of tramadol and M1 were obtained from 23 healthy Korean male subjects after a twice-daily oral dose of 100 mg of tramadol, every 12 hrs, for a total of 5 times. Blood samples were collected at 0 (pre-dose), 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 10, 12, 24, 48 and 72 hrs after last administration. Plasma tramadol concentrations were then analyzed using LC/MS. Population PK analysis of tramadol and its metabolite was performed using a nonlinear mixed-effects modeling (NONMEM). Results: A one-compartment model with combined first-order and zero-order absorption was well fitted to the concentration-time curve of tramadol. M1 was well described by the one-compartment model as an extension of the parent drug (tramadol) model. Genetic polymorphisms of CYP2D6 correlated with the clearance of tramadol, and clearance from the central compartment to the metabolite compartment. Conclusion: The parent-metabolite model successfully characterized the PK of tramadol and its metabolite M1 in healthy Korean male subjects. These results could be applied to evaluate plasma tramadol concentrations after various dosing regimens.