Validity conditions of approximations for a target-mediated drug disposition model: A novel first-order approximation and its comparison to other approximations.

Target-mediated drug disposition (TMDD) is a phenomenon characterized by a drug's high-affinity binding to a target molecule, which significantly influences its pharmacokinetic profile within an organism. The comprehensive TMDD model delineates this interaction, yet it may become overly complex and computationally demanding in the absence of specific concentration data for the target or its complexes. Consequently, simplified TMDD models employing quasi-steady state approximations (QSSAs) have been introduced; however, the precise conditions under which these models yield accurate results require further elucidation. Here, we establish the validity of three simplified TMDD models: the Michaelis-Menten model reduced with the standard QSSA (mTMDD), the QSS model reduced with the total QSSA (qTMDD), and a first-order approximation of the total QSSA (pTMDD). Specifically, we find that mTMDD is applicable only when initial drug concentrations substantially exceed total target concentrations, while qTMDD can be used for all drug concentrations. Notably, pTMDD offers a simpler and faster alternative to qTMDD, with broader applicability than mTMDD. These findings are confirmed with antibody-drug conjugate real-world data. Our findings provide a framework for selecting appropriate simplified TMDD models while ensuring accuracy, potentially enhancing drug development and facilitating safer, more personalized treatments.

Effect of methylphenidate on height in pediatric attention-deficit hyperactivity disorder patients: a systematic review and meta-analysis.

Methylphenidate (MPH), a first-line treatment for attention-deficit hyperactivity disorder (ADHD) management, has been the focus of debate for decades regarding its effect on growth. The aim of this PRISMA meta-analysis was to determine the effect of MPH on height in children/adolescents with ADHD and its predictive factors based on literature reports. Available full-text articles were systematically reviewed to identify clinical studies of pediatric ADHD patients with height Z-score (HZS) data for monotherapy MPH-treated and non-treated groups. We estimated standardized mean differences (SMDs) of HZS or its changes from baseline (ΔHZS) between groups, then identified associated factors through subgroup analyses and meta-regression. For before-after treatment studies, the paired standard errors of ΔHZS were re-estimated to demonstrate in the forest plot. Risk of bias was analyzed using the Newcastle-Ottawa Scale. Among the 29 eligible studies, 26 reported ΔHZS with self-control groups, and ΔHZS or absolute HZS were compared to other external controls in 11 studies. A significant reduction was observed between post-MHP and pre-MPH use, with high heterogeneity (SMD = - 0.40; 95% confidence interval = [ - 0.54, - 0.27]; I2 = 91%). The study region, ADHD subtype, and stimulant-naïve status of patients at baseline may modify the effect on HZS. Because of the high clinical heterogeneity in observational studies, clinicians should consider the negative effect of MPH on height in ADHD patients by determining whether patients fulfill appropriate high-risk criteria. Further well-designed longitudinal studies are required to better quantify this effect, especially with prolonged treatment.

Prothionamide Dose Optimization Using Population Pharmacokinetics for Multidrug-Resistant Tuberculosis Patients.

Prothionamide, a second-line drug for multidrug-resistant tuberculosis (MDR-TB), has been in use for a few decades. However, its pharmacokinetic (PK) profile remains unclear. This study aimed to develop a population PK model for prothionamide and then apply the model to determine the optimal dosing regimen for MDR-TB patients. Multiple plasma samples were collected from 27 MDR-TB patients who had been treated with prothionamide at 2 different study hospitals. Prothionamide was administered according to the weight-band dose regimen (500 mg/day for weight <50 kg and 750 mg/day for weight >50 kg) recommended by the World Health Organization. The population PK model was developed using nonlinear mixed-effects modeling. The probability of target attainment, based on systemic exposure and MIC, was used as a response target. Fixed-dose regimens (500 or 750 mg/day) were simulated to compare the efficacies of various dosing regimens. PK profiles adequately described the two-compartment model with first-order elimination and the transit absorption compartment model with allometric scaling on clearance. All dosing regimens had effectiveness >90% for MIC values <0.4 µg/mL in 1.0-log kill target. However, a fixed dose of 750 mg/day was the only regimen that achieved the target resistance suppression of ≥90% for MIC values of <0.2 µg/mL. In conclusion, fixed-dose prothionamide (750 mg/day), regardless of weight-band, was appropriate for adult MDR-TB patients with weights of 40 to 67 kg.

Anti-neuroinflammatory Effects and Brain Pharmacokinetic Properties of Selonsertib, an Apoptosis signal-regulating Kinase 1 Inhibitor, in mice.

Selonsertib is a first-in-class apoptosis signal-regulating kinase 1 (ASK1) inhibitor in clinical trials for treating NASH and diabetic kidney disease due to its anti-inflammatory and anti-fibrotic activities. In the present study, we investigated the anti-neuroinflammatory effects and brain pharmacokinetic properties of selonsertib. It inhibited inflammatory cytokines and NO production by suppressing phosphorylated ASK1 in the LPS-stimulated microglial cell line, BV2 cells. Consistent with the in vitro results, selonsertib attenuated plasma and brain TNF-α levels in the LPS-induced murine neuroinflammation model. In vitro and in vivo pharmacokinetic studies of selonsertib were conducted in support of central nervous system (CNS) drug discovery. In both Caco-2 and MDR-MDCK cells, selonsertib exhibited a high efflux ratio, showing that it is a P-gp substrate. Selonsertib was rapidly and effectively absorbed into the systemic circulation after oral treatment, with a Tmax of 0.5 h and oral bioavailability of 74%. In comparison with high systemic exposure with Cmax of 16.2 µg/ml and AUC of 64 µg·h/mL following oral dosing of 10 mg/kg, the brain disposition of selonsertib was limited, with Cmax of 0.08 µg/g and Kp value of 0.004. This study demonstrates that selonsertib can be a therapeutic agent for neuroinflammatory diseases.

External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates.

AIM: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models. METHODS: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement. RESULTS: A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.

Population Pharmacokinetics of Sulindac and Genetic Polymorphisms of FMO3 and AOX1 in Women with Preterm Labor.

PURPOSE: This prospective study aimed to evaluate the effects of genetic polymorphisms in sulindac-related metabolizing enzyme genes including FMO3 and AOX1 on the population pharmacokinetics of sulindac in 58 pregnant women with preterm labor. METHODS: Plasma samples were collected at 1.5, 4, and 10 h after first oral administration of sulindac. Plasma concentrations of sulindac and its active metabolite (sulindac sulfide) were determined, and pharmacokinetic analysis was performed with NONMEM 7.3. RESULTS: The mean maternal and gestational ages at the time of dosing were 32.5 ± 4.4 (range, 20-41) years and 27.4 ± 4.4 (range, 16.4-33.4) weeks, respectively. In the population pharmacokinetic analysis, one depot compartment model of sulindac with absorption lag time best described the data. The metabolism of sulindac and sulindac sulfide was described using Michaelis-Menten kinetics. In stepwise modeling, gestational age impacted volume of distribution (Vc), and FMO3 rs2266782 was shown by the Michaelis constant to affect conversion of sulindac sulfide to sulindac (KM32); these were retained in the final model. CONCLUSIONS: Genetic polymorphisms of FMO3 and AOX1 could affect the pharmacokinetics of sulindac in women who undergo preterm labor. The results of this study could help clinicians develop individualized treatment plans for administering sulindac.

Application of an Inter-Species Extrapolation Method for the Prediction of Drug Interactions between Propolis and Duloxetine in Humans.

Duloxetine (DLX) is a potent drug investigated for the treatment of depression and urinary incontinence. DLX is extensively metabolized in the liver by two P450 isozymes, CYP2D6 and CYP1A2. Propolis (PPL) is one of the popular functional foods known to have effects on activities of CYPs, including CYP1A2. Due to the high probability of using DLX and PPL simultaneously, the present study was designed to investigate the potent effect of PPL on pharmacokinetics (PKs) of DLX after co-administration in humans. A PK study was first conducted in 18 rats (n = 6/group), in which the plasma concentration of DLX and its major metabolite 4-hydroxy duloxetine (4-HD) with or without administration of PPL was recorded. Population PKs and potential effects of PPL were then analyzed using NONMEM software. Lastly, these results were extrapolated from rats to humans using the allometric scaling and the liver blood flow method. PPL (15,000 mg/day) exerts a statistically significant increase in DLX exposures at steady state, with a 20.2% and 24.6% increase in DLX C m a x , s s and the same 28.0% increase in DLX A U C s s when DLX (40 or 60 mg) was administered once or twice daily, respectively. In conclusion, safety issues are required to be attended to when individuals simultaneously use DLX and PPL at high doses, and the possibility of interactions between DLX and PPL might be noted.

External evaluation of population pharmacokinetic models for ciclosporin in adult renal transplant recipients.

AIMS: Several population pharmacokinetic (popPK) models for ciclosporin (CsA) in adult renal transplant recipients have been constructed to optimize the therapeutic regimen of CsA. However, little is known about their predictabilities when extrapolated to different clinical centres. Therefore, this study aimed to externally evaluate the predictive ability of CsA popPK models and determine the potential influencing factors. METHODS: A literature search was conducted and the predictive performance was determined for each selected model using an independent data set of 62 patients (471 predose and 500 2-h postdose concentrations) from our hospital. Prediction-based diagnostics and simulation-based normalized prediction distribution error were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. Additionally, potential factors influencing model predictability were investigated. RESULTS: Seventeen models extracted from 17 published popPK studies were assessed. Prediction-based diagnostics showed that ethnicity potentially influenced model transferability. Simulation-based normalized prediction distribution error analyses indicated misspecification in most of the models, especially regarding variance. Bayesian forecasting demonstrated that the predictive performance of the models substantially improved with 2-3 prior observations. The predictability of nonlinear Michaelis-Menten models was superior to that of linear compartmental models when evaluating the impact of structural models, indicating the underlying nonlinear kinetics of CsA. Structural model, ethnicity, covariates and prior observations potentially affected model predictability. CONCLUSIONS: Structural model is the predominant factor influencing model predictability. Incorporation of nonlinear kinetics in CsA popPK modelling should be considered. Moreover, Bayesian forecasting substantially improved model predictability.

Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects.

BACKGROUND: Exploratory preclinical, as well as clinical trials, may involve a small number of patients, making it difficult to calculate and analyze the pharmacokinetic (PK) parameters, especially if the PK parameters show very high inter-individual variability (IIV). In this study, the performance of a classical first-order conditional estimation with interaction (FOCE-I) and expectation maximization (EM)-based Markov chain Monte Carlo Bayesian (BAYES) estimation methods were compared for estimating the population parameters and its distribution from data sets having a low number of subjects. METHODS: In this study, 100 data sets were simulated with eight sampling points for each subject and with six different levels of IIV (5%, 10%, 20%, 30%, 50%, and 80%) in their PK parameter distribution. A stochastic simulation and estimation (SSE) study was performed to simultaneously simulate data sets and estimate the parameters using four different methods: FOCE-I only, BAYES(C) (FOCE-I and BAYES composite method), BAYES(F) (BAYES with all true initial parameters and fixed ω 2 ), and BAYES only. Relative root mean squared error (rRMSE) and relative estimation error (REE) were used to analyze the differences between true and estimated values. A case study was performed with a clinical data of theophylline available in NONMEM distribution media. NONMEM software assisted by Pirana, PsN, and Xpose was used to estimate population PK parameters, and R program was used to analyze and plot the results. RESULTS: The rRMSE and REE values of all parameter (fixed effect and random effect) estimates showed that all four methods performed equally at the lower IIV levels, while the FOCE-I method performed better than other EM-based methods at higher IIV levels (greater than 30%). In general, estimates of random-effect parameters showed significant bias and imprecision, irrespective of the estimation method used and the level of IIV. Similar performance of the estimation methods was observed with theophylline dataset. CONCLUSIONS: The classical FOCE-I method appeared to estimate the PK parameters more reliably than the BAYES method when using a simple model and data containing only a few subjects. EM-based estimation methods can be considered for adapting to the specific needs of a modeling project at later steps of modeling.

Population pharmacokinetics of cyclosporine in hematopoietic stem cell transplant patients: consideration of genetic polymorphisms.

BACKGROUND: Cyclosporine (CsA), which is used for graft-versus-host disease prophylaxis in allogeneic hematopoietic stem cell transplant (allo-HSCT), has a narrow therapeutic range and large interindividual and intraindividual pharmacokinetic variability. Nevertheless, population pharmacokinetic (PopPK) studies of CsA in allo-HSCT are scarce. OBJECTIVE: The goal of our study was to build a PopPK model of CsA in allo-HSCT in consideration of demographic, clinical, and genetic polymorphisms data. METHODS: A total of 34 adult allo-HSCT patients who received CsA were enrolled prospectively. Demographic, clinical, and CYP3A5 *1/*3, CYP2C19 *1/*2/*3, ABCB1 3435C>T, 1236C>T, 2677G>T/A, ABCC2 -24C>T, 1249G>A, VDR Bsml, Apal polymorphisms data were collected. A PopPK modeling was conducted with NONMEM program. RESULTS: A 1-compartment model with a 2-transit absorption compartment model was developed. After the stepwise covariate model building process, weight was incorporated into clearance (CL) as a power function model with the exponent value of 0.419. The final typical estimate of CL was 21.2 L/h; volume of distribution was 430 L; logit-transformed bioavailability was 1.49 (bioavailability: 81%); and transit compartment rate was 2.87/h. None of the genetic polymorphisms in CYP3A5, CYP2C19, ABCB1, ABCC2, and VDR were significant covariates in the pharmacokinetics of CsA. CONCLUSIONS: In our study, it was observed that weight had a significant effect on CL. Genetic polymorphisms did not affect CsA pharmacokinetics. Prospective studies with a larger number of participants is needed to validate the results of this study.

Development of QTc prolongation model incorporating circadian rhythm using harmonic model.

1. QT prolongation is one of the major safety tests used in the development of a new drug. The ICH guidelines for the evaluation of QT prolongation recommend the use of the in vitro hERG assay and the in vivo telemetry test. However, QT intervals change under normal conditions due to circadian rhythm and can affect the results of the tests. In this study, we developed a PK/PD model to describe the QT interval after the administration of astemizole allowing for the normal changes by circadian rhythm. 2. The typical PK parameters of absorption rate constant (ka), volume of distribution (Vc and Vm), metabolism (km), and elimination rate constant (kel and kel-m) were 0.49 h(-1), 4950 L, 20 L, 0.0127 h(-1), 0.0095 h(-1), and 0.95 h(-1), respectively. The final PK/PD model was the biophase model with the modified harmonic model. The typical PK/PD parameters, base QTc interval (QT0), amplitude (T1, T3), period of QTc interval changing (T2, T4), and EC50 were 233 ms, 3.31, 1.5, -9.24 h, 1.85 h, and 0.81 ng/ml, respectively. 3. The PK/PD model to explain the changes of the QT interval that allows normal changes in the circadian rhythm after the administration of astemizole was developed successfully. This final model can be applied to the development of a human model.

Population pharmacogenetic pharmacokinetic modeling for flip-flop phenomenon of enteric-coated mycophenolate sodium in kidney transplant recipients.

PURPOSE: Enteric-coated mycophenolate sodium (EC-MPS) is effective and safe in preventing rejection after transplantation and is mainly transported by ABCs and OATPs and metabolized by UGTs. The genetic polymorphisms affect the inter-individual variation in drug disposition and elimination. The aims of this study were to develop a population pharmacokinetic (PK) model and to evaluate the influence of genetic and clinical factors on the PK of mycophenolic acid (MPA) in Korean renal transplant recipients. METHODS: Population analysis of EC-MPS was performed using non-linear mixed effects modeling (NONMEM). After clinical and genetic factors were evaluated using a stepwise covariate method, we selected clinically relevant covariates considering covariate effects. The final model was validated by bootstrap and visual predictive check. At last, we performed the model-based simulations in order to explore an optimal dose to achieve target area under the curve (AUC) in hypothetical scenarios. RESULTS: From 166 plasma concentrations (n=34), a time-lagged two-compartment with a flip-flop model best describes the PK of MPA. The covariate analysis identified lower creatinine clearance (CLcr) and SLCO1B1 variant genotype were correlated with lower MPA clearance, on the contrary, UGT1A9 variant had decreased distribution of MPA, contributing to lower absorption. When considering to UGT1A9, SLCO1B1 genotypes, and renal function, the new recommended dose of 540 mg twice daily resulted in a higher success of achieving the target AUC0-12h in the 30-60 mg.h/L. CONCLUSIONS: CLcr, UGT1A9 and SLCO1B1 genotypes seem to be promising parameters to predict the pharmacokinetics with flip-flop phenomenon of EC-MPS in transplant recipient having stable renal function. This model on clinical practice may help prevent overexposure and achieve a proper AUC in the Korean population.

Development of a population pharmacokinetic model to describe olmesartan medoxomil/ hydrochlorothiazide (20/12.5 mg) FDC tablet in male healthy South Korean subjects.

AIM: The objective of the present study was to develop population pharmacokinetic models for olmesartan medoxomil and hydrochlorothiazide and to investigate the influence of demographic factors on these population pharmacokinetics. METHODS: Plasma concentrations of olmesartan medoxomil and hydrochlorothiazide were measured in 41 healthy volunteers enrolled in our bioequivalence study by LC-MS/MS following oral administration of an olmesartan medoxomil/hydrochlorothiazide (20/12.5 mg) fixed-dose combination tablet. This data and covariates were subjected to nonlinear mixed-effect modeling analysis using the NONMEM software. Evaluation featured a visual predicted check and bootstrapping. RESULTS: The distributions of olmesartan medoxomil and hydrochlorothiazide were best fitted using a two-compartment model with no lag time and first-order elimination. When analyzing hydrochlorothiazide kinetics, we found that TCHO and CL/F were correlated, while. HB and Ka influenced olmesartan medoxomil modeling. All evaluations indicated that the pharmacokinetic profiles of olmesartan medoxomil and hydrochlorothiazide were adequately described using our PPK model. CONCLUSIONS: This study indicates that demographic factors influence the inter-individual variability in the disposition of the combination drug, and it might be more useful to apply it to the PK of olmesartan medoxomil/hydrochlorothiazide (20/12.5 mg) FDC tablets administered to patients with hypertension. *These two authors contributed equally to this work.

A simple pharmacokinetic model of alendronate developed using plasma concentration and urine excretion data from healthy men.

The study of pharmacokinetics of alendronate has been hampered by difficulties in accurately and reproducibly determining their concentrations in serum and urine. Thus, pharmacokinetic characteristics of alendronate have been described in many reports based on urinary excretion data; and plasma pharmacokinetics and the simultaneous pharmacokinetic models of alendronate in plasma and urine are not available. The aims of this study were to measure alendronate concentration in plasma and excretion in urine concurrently and to develop compartmental pharmacokinetic model using urine data. In open-label, single-dose pharmacokinetic study, 10 healthy male volunteers received oral dose of alendronate (70 mg tablet). Blood and urine alendronate concentrations were determined using validated high-performance liquid chromatography method. Non-compartmental analysis was performed using WinNonlin program (Pharsight Inc., Apex, NC). A one-compartment pharmacokinetic model was applied to describe pharmacokinetics of alendronate. A peak plasma alendronate concentration of 33.10 ± 14.32 ng/mL was attained after 1.00 ± 0.16 h. The cumulative amount of alendronate excreted in urine and peak excretion rate were 731.28 ± 654.57 µg and 314.68 ± 395.43 µg/h, respectively. The model, which included first-order absorption rate for oral dosing, showed good fit to alendronate data obtained from plasma and urine. The absorption rate constant was 2.68 ± 0.95 h(-1). The elimination rate constants Kurine and Knon-ur were 0.005 ± 0.004 h(-1) and 0.42 ± 0.08 h(-1), respectively. The pharmacokinetics of alendronate in plasma and urine of healthy men can be predicted using one-compartment model, and thus the behavior of drug in plasma can be estimated from urinary excretion data.

Predicting lung exposure of intramuscular niclosamide as an antiviral agent: Power-law based pharmacokinetic modeling.

Niclosamide, a potent anthelmintic agent, has emerged as a candidate against COVID-19 in recent studies. Its formulation has been investigated extensively to address challenges related to systemic exposure. In this study, niclosamide was formulated as a long-acting intramuscular injection to achieve systemic exposure in the lungs for combating the virus. To establish the dose-exposure relationship, a hamster model was selected, given its utility in previous COVID-19 infection studies. Pharmacokinetic (PK) analysis was performed using NONMEM and PsN. Hamsters were administered doses of 55, 96, 128, and 240 mg/kg with each group comprising five animals. Two types of PK models were developed, linear models incorporating partition coefficients and power-law distributed models, to characterize the relationship between drug concentrations in the plasma and lungs of the hamsters. Numerical and visual diagnostics, including basic goodness-of-fit and visual predictive checks, were employed to assess the models. The power-law-based PK model not only demonstrated superior numerical performance compared with the linear model but also exhibited better agreement in visual diagnostic evaluations. This phenomenon was attributed to the nonlinear relationship between drug concentrations in the plasma and lungs, reflecting kinetic heterogeneity. Dose optimization, based on predicting lung exposure, was conducted iteratively across different drug doses, with the minimum effective dose estimated to be ~1115 mg/kg. The development of a power-law-based PK model proved successful and effectively captured the nonlinearities observed in this study. This method is expected to be applicable for investigating the drug disposition of specific formulations in the lungs.

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.

Population pharmacokinetics analysis of cyclophosphamide with genetic effects in patients undergoing hematopoietic stem cell transplantation.

PURPOSE: To build a population pharmacokinetic (PK) model of cyclophosphamide (CY) and its metabolite, 4-hydroxycyclophosphamide (HCY), in patients undergoing allogeneic haematopoietic stem cell transplantation (HSCT) and to identify covariates, including genetic polymorphisms, which affect CY and HCY PK parameters. METHOD: The study cohort comprised 21 patients undergoing HSCT who received CY intravenously between 2009 and 2011. Clinical characteristics and CY and HCY concentration data were collected for all patients, and ABCB1, ABCC2, GSTA1, GSTM1, GSTP1, GSTT1, CYP2B6, CYP2C19, and CYP3A5 genotyping was performed. A hypothetical enzyme compartment was conducted using the NONMEM program. RESULTS: A population PK analysis showed that the ABCC2 1249 genotype and aspartate aminotransferase levels significantly affected non-induced clearance (CL UI) and induced clearance (CL I) of CY, respectively. The final estimate of the mean CL UI and CL I of CY was 15.5 and 0.683 L/h, respectively, and the mean volume of distribution (V 1) of CY was 88.0 L. The inter-individual variability for CL UI, CL I, and V 1 of CY was 52.8, 200, and 18.0 %, respectively. Additionally, the CL UI of CY was significantly decreased to approximately 51 % in patients with the 1249 GA heterozygous genotype compared to those with the 1249 GG wild-type genotype (p < 0.05). There were only three heterozygous GA variants of ABCC2 1249 in the study patients. CONCLUSIONS: The population PK model developed in this study implies an influence of genetic factors on the clearance of CY. Clearance was moderately reduced in patients with the ABCC2 1249GA heterozygous genotype.

Prediction of the tacrolimus population pharmacokinetic parameters according to CYP3A5 genotype and clinical factors using NONMEM in adult kidney transplant recipients.

PURPOSE: Tacrolimus is a commonly used immunosuppressant in solid organ transplantation recipients, but it is characterized by a narrow therapeutic range and large inter-individual variability. The purpose of this study was to establish a population pharmacokinetic (PK) model of tacrolimus and evaluate the influence of clinical covariates, including the genetic polymorphisms of the cytochrome P450 3A5 gene (CYP3A5) and gene encoding P-glycoprotein (ABCB1), on the PK parameters in adult Korean kidney transplant recipients. METHODS: Clinical data were collected retrospectively for 400 days after the initiation of a tacrolimus-based immunosuppression therapy. Data from 2,788 trough blood samples obtained from 80 subjects were used to perform a population PK analysis with a nonlinear mixed-effect model (NONMEM). RESULTS: The estimated population mean values of clearance (CL/F) and volume of distribution (V/F) were 22.9 L/h and 716 L, respectively, and the k(a) was fixed to 4.5 h⁻¹. The CYP3A5 genotype, hematocrit level, and post-operative days were identified as the main covariates that influence CL/F, and body weight was found to have a significant effect on V/F. Other covariates, including ABCB1 genotype, corticosteroid dosage, sex, and other clinical data, did not contribute to the pharmacokinetics of tacrolimus. CONCLUSIONS: This tacrolimus population PK model will be a valuable tool in developing rational guidelines and provides a basis for individualized therapy after kidney transplantation in clinical settings of Korea.

Application of the Population Pharmacokinetics Model-Based Approach to the Prediction of Drug-Drug Interaction between Rivaroxaban and Carbamazepine in Humans.

Rivaroxaban (RIV) is one of the direct oral anticoagulants used to prevent and treat venous and arterial thromboembolic events. Considering the therapeutic indications, RIV is likely to be concomitantly administered with various other drugs. Among these is carbamazepine (CBZ), one of the recommended first-line options to control seizures and epilepsy. RIV is a strong substrate of cytochrome P450 (CYP) enzymes and Pgp/BCRP efflux transporters. Meanwhile, CBZ is well known as a strong inducer of these enzymes and transporters. Therefore, drug-drug interaction (DDI) between CBZ and RIV is expected. This study aimed to predict the DDI profile of CBZ and RIV in humans by using a population pharmacokinetics (PK) model-based approach. We previously investigated the population PK parameters of RIV administered alone or with CBZ in rats. In this study, those parameters were extrapolated from rats to humans by using simple allometry and liver blood flow scaling, and then applied to back-simulate the PK profiles of RIV in humans (20 mg RIV per day) used alone or with CBZ (900 mg CBZ per day). Results showed that CBZ significantly reduced RIV exposure. The AUCinf and Cmax of RIV decreased by 52.3% and 41.0%, respectively, following the first RIV dose, and by 68.5% and 49.8% at the steady state. Therefore, the co-administration of CBZ and RIV warrants caution. Further studies investigating the extent of DDIs between these drugs should be conducted in humans to fully understand their safety and effects.

Development of a Physiologically Based Pharmacokinetic Model for Tegoprazan: Application for the Prediction of Drug-Drug Interactions with CYP3A4 Perpetrators.

Tegoprazan is a novel potassium-competitive acid blocker (P-CAB) developed by CJ Healthcare (Korea) for the treatment of gastroesophageal reflux disease and helicobacter pylori infections. Tegoprazan is mainly metabolized by cytochrome P450 (CYP) 3A4. Considering the therapeutic indications, tegoprazan is likely to be administered in combination with various drugs. Therefore, the investigation of drug-drug interactions (DDI) between tegoprazan and CYP3A4 perpetrators is imperative. In the present study, we first aimed to develop a physiologically based pharmacokinetic (PK) model for tegoprazan and its major metabolite, M1, using PK-Sim®. This model was applied to predict the DDI between tegoprazan and CYP3A4 perpetrators. Clarithromycin, a potent inhibitor of CYP3A4, and rifampicin, a strong inducer of CYP3A4, were selected as case studies. Our results show that clarithromycin significantly increased the exposure of tegoprazan. The area under the concentration-time curve (AUC) and Cmax of tegoprazan in the steady state increased up to 4.54- and 2.05-fold, respectively, when tegoprazan (50 mg, twice daily) was coadministered with clarithromycin (500 mg, three times daily). Rifampicin significantly reduced the exposure of tegoprazan. The AUC and Cmax of tegoprazan were reduced by 5.71- and 3.51-fold when tegoprazan was coadministered with rifampicin (600 mg, once daily). Due to the high DDI potential, the comedication of tegoprazan with CYP3A4 perpetrators should be controlled. The dosage adjustment for each individual is suggested.