Normalized Prediction Distribution Errors Research Articles

Population pharmacokinetic analysis of febuxostat with high focus on absorption kinetics and food effect

Febuxostat is commonly used in clinic for the treatment of hyperuricemia. Multiple-peak phenomenon has been observed in human plasma concentration-time profiles of febuxostat, but has not been paid enough attention in previous research. This study takes a pivotal step forward by conducting a comprehensive population pharmacokinetic (PopPK) analysis of febuxostat in a healthy Chinese cohort, with a central focus on delineating its absorption profile under contrasting fasting and fed conditions, while concurrently assessing the influence of food alongside other potential covariates on febuxostat’s PK profile. The plasma concentration data used for modeling was obtained from two bioequivalence (BE) studies. Subjects were administered febuxostat 20 mg or 80 mg under fasting or fed condition. Goodness-of-fit plots, visual predict check (VPC), and normalized prediction distribution error (NPDE) were used for model evaluation. Based on the established model, PK profiles in healthy Caucasian subjects were simulated with parameter adjustment for race difference on clearance and bioavailability. Data from 128 subjects were used in the PopPK analysis. Febuxostat concentration-time curves were described by a two-compartment model with two deposit absorption compartments and lag times (Tlag). Prandial states (Food) showed significant impact on absorption rate ka1 and ka2, as well as Tlag1, and body weight was identified as a significant covariate on the apparent distribution volume. The PopPK analysis of febuxostat in healthy Chinese volunteers, under both fasted and fed conditions, successfully characterized its PK profile and underscored the significant influence of food on absorption. The potential difference of absorption between Chinese population and Caucasian population indicated from the simulations needs further investigation.

Influence of C-reactive protein on the pharmacokinetics of voriconazole in relation to the CYP2C19 genotype: a population pharmacokinetics analysis.

Voriconazole is a broad-spectrum triazole antifungal agent. A number of studies have revealed that the impact of C-reactive protein (CRP) on voriconazole pharmacokinetics was associated with the CYP2C19 phenotype. However, the combined effects of CYP2C19 genetic polymorphisms and inflammation on voriconazole pharmacokinetics have not been considered in previous population pharmacokinetic (PPK) studies, especially in the Chinese population. This study aimed to analyze the impact of inflammation on the pharmacokinetics of voriconazole in patients with different CYP2C19 genotypes and optimize the dosage of administration. Data were obtained retrospectively from adult patients aged ≥16years who received voriconazole for invasive fungal infections from October 2020 to June 2023. Plasma voriconazole levels were measured via high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). CYP2C19 genotyping was performed using the fluorescence in situ hybridization method. A PPK model was developed using the nonlinear mixed-effect model (NONMEM). The final model was validated using bootstrap, visual predictive check (VPC), and normalized prediction distribution error (NPDE). The Monte Carlo simulation was applied to evaluate and optimize the dosing regimens. A total of 232 voriconazole steady-state trough concentrations from 167 patients were included. A one-compartment model with first order and elimination adequately described the data. The typical clearance (CL) and the volume of distribution (V) of voriconazole were 3.83L/h and 134L, respectively. The bioavailability was 96.5%. Covariate analysis indicated that the CL of voriconazole was substantially influenced by age, albumin, gender, CRP, and CYP2C19 genetic variations. The V of voriconazole was significantly associated with body weight. An increase in the CRP concentration significantly decreased voriconazole CL in patients with the CYP2C19 normal metabolizer (NM) and intermediate metabolizer (IM), but it had no significant effect on patients with the CYP2C19 poor metabolizer (PM). The Monte Carlo simulation based on CRP levels indicated that patients with high CRP concentrations required a decreased dose to attain the therapeutic trough concentration and avoid adverse drug reactions in NM and IM patients. These results indicate that CRP affects the pharmacokinetics of voriconazole and is associated with the CYP2C19 phenotype. Clinicians dosing voriconazole should consider the patient's CRP level, especially in CYP2C19 NMs and IMs.

Population pharmacokinetics of olanzapine in pediatric patients with psychiatric disorders

ABSTRACT Objective To develop and validate a population pharmacokinetic (PPK) model of oral olanzapine in pediatric Chinese patients in order to individualize therapy in this population. Methods A total of 897 serum concentrations from 269 pediatric patients taking oral olanzapine (ages 8–17 years) were collected. Demographic parameters, biological characteristics and concomitant medications were investigated as covariates. The data were analyzed using a nonlinear mixed-effects modeling approach. Bootstrapping (1000 runs), normalized prediction distribution error (NPDE), and external validation of 62 patients were employed. Simulations were performed to explore the individualized dosing regimens in various situations. Results The one-compartment model with first-order absorption and elimination had an apparent clearance (CL/F) of 10.38 L/h, a distribution volume (V/F) of 9.41 L/kg and an absorption rate constant (Ka) fixed at 0.3 h−1. The equation was CL∕F (L∕h) = 10.38 × (body weight∕60)0.25 ×1.33 (if male) × 0.71 (if co-occurrence of infection) × 0.51 (if co-therapy with fluvoxamine) × 1.27 (if co-therapy with sertraline) × 1.43 (if co-therapy with valproate). The final model had satisfactory stability, robustness, and predictive ability. The results from a simulation suggested the oral olanzapine doses required for male and female pediatric patients weighing between 40 and 60 kg without co-medication were 10–15 mg/day and 7.5–10 mg/day, respectively, and dosage adjustments should be based on sex and body weight; and co-administrated with valproate, sertraline, or fluvoxamine. Conclusion This model may help individualize optimum dosing of oral olanzapine for pediatric patients.

Population Pharmacokinetics of Lamotrigine and Its N2-Glucuronide Metabolite in Chinese Patients With Epilepsy.

Lamotrigine is a new antiepileptic drug with substantial interindividual variability in its pharmacokinetics and therapeutic responses. This study aimed to develop population pharmacokinetic (PPK) models of lamotrigine and its N2-glucuronide metabolites for model-informed individualized therapy. A total of 353 plasma concentrations from Chinese patients with epilepsy receiving oral lamotrigine were used to develop a population PPK model using a nonlinear mixed effects modeling method. One- and two-compartment models were applied to the nonmetabolite and metabolite model, respectively. Forward addition and backward elimination were used to establish the final model. Model validation was performed using standard goodness-of-fit, bootstrap, visual predictive checks, and normalized prediction distribution errors. Finally, simulations were performed to propose lamotrigine dosages in different situations to achieve trough concentrations within the reference interval (2.5-15 mg/L). For both final population PPK models, coadministration with valproic acid (VPA) or enzyme inducer, and body weight significantly affected lamotrigine clearance. The final models for lamotrigine clearance were and for nonmetabolite and metabolite models, respectively. The precision of the PPK parameters was acceptable, and the models exhibited good predictability. Monte Carlo simulations revealed that the lamotrigine dosage administered to patients combined with an enzyme inducer must be tripled that administered with VPA to reach the target trough concentration. Variability in the pharmacokinetics of lamotrigine is large. Coadministration of VPA or an enzyme inducer and body weight are the most important factors in lamotrigine clearance in Chinese patients with epilepsy. The developed population PPK models might support further optimization of lamotrigine dosing regimens.

Population pharmacokinetic model for oral ORIN1001 in Chinese patients with advanced solid tumors.

Background: ORIN1001, a first-in-class oral IRE1-α endoribonuclease inhibitor to block the activation of XBP1, is currently in clinical development for inhibiting tumor growth and enhancing the effect of chemical or targeted therapy. Early establishment of a population pharmacokinetic (PopPK) model could characterize the pharmacokinetics (PK) of ORIN1001 and evaluate the effects of individual-specific factors on PK, which will facilitate the future development of this investigational drug. Methods: Non-linear mixed effect model was constructed by Phoenix NLME software, utilizing the information from Chinese patients with advanced solid tumors in a phase I clinical trial (Register No. NCT05154201). Statistically significant PK covariates were screened out by a stepwise process. The final model, after validating by the goodness-of-fit plots, non-parametric bootstrap, visual predictive check and test of normalized prediction distribution errors, was further applied to simulate and evaluate the impact of covariates on ORIN1001 exposure at steady state up to 900mg per day as a single agent. Results: A two-compartment model with first-order absorption (with lag-time)/elimination was selected as the best structural model. Total bilirubin (TBIL) and lean body weight (LBW) were considered as the statistically significant covariates on clearance (CL/F) of ORIN1001. They were also confirmed to exert clinically significant effects on ORIN1001 steady-state exposure after model simulation. The necessity of dose adjustments based on these two covariates remains to be validated in a larger population. Conclusion: The first PopPK model of ORIN1001 was successfully constructed, which may provide some important references for future research.

Population pharmacokinetics of cyclosporine A in pediatric patients with thalassemia undergoing allogeneic hematopoietic stem cell transplantation.

To establish the population pharmacokinetics (PPK) model of cyclosporine A(CsA) in pediatric patients with thalassemia undergoing allogeneic hematopoietic stem cell transplantation (HSCT), aiming at providing a reference for clinical dose individualization of CsA. Children with thalassemia who underwent allogeneic HSCT were enrolled retrospectively. The PPK structural model and the random variable model of CsA were established on NONMEN. And goodness of fit plots (GOFs), visual predictive check (VPC), and bootstrap and normalized prediction distribution errors (NPDE) were used to evaluate the final model. A one-compartment model with first-order absorption was employed to fit the base model. A total of 74 pediatric patients and 600 observations of whole blood concentration were included. The final model included weight (WT) in clearance (CL), alongside post-operative day (POD), fluconazole (FLUC), voriconazole (VORI), posaconazole (POSA), and red blood cell count (RBC) significantly. All the model evaluations were passed. In the PPK model based on the pediatric cohort on CsA with thalassemia undergoing allogeneic HSCT, WT, POD, FLUC, VORI, POSA, and RBC were found to be the significant factors influencing CL of CsA. The reliability and robustness of the final model were excellent. It is expected that the PPK model can assist in individualizing dosing strategy clinically.

Predictive Performance of a Gentamicin Pharmacokinetic Model in Term Neonates with Perinatal Asphyxia Undergoing Controlled Therapeutic Hypothermia.

Model validation procedures are crucial when population pharmacokinetic (PK) models are used to develop dosing algorithms and to perform model-informed precision dosing. We have previously published a population PK model describing the PK of gentamicin in term neonates with perinatal asphyxia during controlled therapeutic hypothermia (TH), which showed altered gentamicin clearance during the hypothermic phase dependent on gestational age and weight. In this study, the predictive performance and generalizability of this model were assessed using an independent data set of neonates with perinatal asphyxia undergoing controlled TH. The external data set contained a subset of neonates included in the prospective observational multicenter PharmaCool Study. Predictive performance was assessed by visually inspecting observed-versus-predicted concentration plots and calculating bias and precision. In addition, simulation-based diagnostics, model refitting, and bootstrap analyses were performed. The external data set included 323 gentamicin concentrations of 39 neonates. Both the model-building and external data set included neonates from multiple centers. The original gentamicin PK model predicted the observed gentamicin concentrations with adequate accuracy and precision during all phases of controlled TH. Model appropriateness was confirmed with prediction-corrected visual predictive checks and normalized prediction distribution error analyses. Model refitting to the merged data set (n = 86 neonates with 935 samples) showed accurate estimation of PK parameters. The results of this external validation study justify the generalizability of the gentamicin dosing recommendations made in the original study for neonates with perinatal asphyxia undergoing controlled TH (5 mg/kg every 36 or 24 h with gestational age 36-41 and 42 wk, respectively) and its applicability in model-informed precision dosing.

External Evaluation of Population Pharmacokinetic Models for High-Dose Methotrexate in Adult Patients with Hematological Tumors.

Currently, numerous population pharmacokinetic (popPK) models for methotrexate (MTX) have been published for estimating PK parameters and variability. However, it is unclear whether the accuracy of these models is sufficient for clinical application. The aim of this study is to evaluate published models and assess their predictive performance according to the standards of scientific research. A total of 237 samples from 74 adult patients who underwent high-dose MTX (HDMTX) treatment at Shanghai Changzheng Hospital were collected. The software package NONMEM was used to perform an external evaluation for each model, including prediction-based diagnosis, simulation-based diagnosis, and Bayesian forecasting. The simulation-based diagnosis includes normalized prediction distribution error (NPDE) and visual predictive check (VPC). Following screening, 7 candidate models suitable for external validation were identified for comparison. However, none of these models exhibited excellent predictive performance. Bayesian simulation results indicated that the prediction precision and accuracy of all models significantly improved when incorporating prior concentration information. The published popPK models for MTX exhibit significant differences in their predictive performance, and none of the models were able to accurately predict MTX concentrations in our data set. Therefore, before adopting any model in clinical practice, extensive evaluation should be conducted.

Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study.

Background: Several studies have investigated the population pharmacokinetics (popPK) of valproic acid (VPA) in children with epilepsy. However, the predictive performance of these models in the extrapolation to other clinical environments has not been studied. Hence, this study evaluated the predictive abilities of pediatric popPK models of VPA and identified the potential effects of protein binding modeling strategies. Methods: A dataset of 255 trough concentrations in 202 children with epilepsy was analyzed to assess the predictive performance of qualified models, following literature review. The evaluation of external predictive ability was conducted by prediction- and simulation-based diagnostics as well as Bayesian forecasting. Furthermore, five popPK models with different protein binding modeling strategies were developed to investigate the discrepancy among the one-binding site model, Langmuir equation, dose-dependent maximum effect model, linear non-saturable binding equation and the simple exponent model on model predictive ability. Results: Ten popPK models were identified in the literature. Co-medication, body weight, daily dose, and age were the four most commonly involved covariates influencing VPA clearance. The model proposed by Serrano et al. showed the best performance with a median prediction error (MDPE) of 1.40%, median absolute prediction error (MAPE) of 17.38%, and percentages of PE within 20% (F20, 55.69%) and 30% (F30, 76.47%). However, all models performed inadequately in terms of the simulation-based normalized prediction distribution error, indicating unsatisfactory normality. Bayesian forecasting enhanced predictive performance, as prior observations were available. More prior observations are needed for model predictability to reach a stable state. The linear non-saturable binding equation had a higher predictive value than other protein binding models. Conclusion: The predictive abilities of most popPK models of VPA in children with epilepsy were unsatisfactory. The linear non-saturable binding equation is more suitable for modeling non-linearity. Moreover, Bayesian forecasting with prior observations improved model fitness.

External evaluation of the predictive performance of published population pharmacokinetic models of linezolid in adult patients

Several linezolid population pharmacokinetic (popPK) models have been established to facilitate optimal therapy; however, their extrapolated predictive performance to other clinical sites is unknown. This study aimed to externally evaluate the predictive performance of published pharmacokinetic models of linezolid in adult patients. For the evaluation dataset, 150 samples were collected from 70 adult patients (72.9% of which were critically ill) treated with linezolid at our center. Twenty-five published popPK models were identified from PubMed and Embase. Model predictability was evaluated using prediction-based, simulation-based, and Bayesian forecasting-based approaches to assess model predictability. Prediction-based diagnostics found that the prediction error within ±30% (F30) was less than 40% in all models, indicating unsatisfactory predictability. The simulation-based prediction- and variability-corrected visual predictive check and normalized prediction distribution error test indicated large discrepancies between the observations and simulations in most of the models. Bayesian forecasting with one or two prior observations significantly improved the models' predictive performance. The published linezolid popPK models showed insufficient predictive ability. Therefore, their sole use is not recommended, and incorporating therapeutic drug monitoring of linezolid in clinical applications is necessary.

Population pharmacokinetics of topiramate in Chinese children with epilepsy.

Topiramate, a broad-spectrum antiepileptic drug, exhibits substantial inter-individual variability in both its pharmacokinetics and therapeutic response. The aim of this study was to investigate the influence of patient characteristics and genetic variants on topiramate clearance using population pharmacokinetic (PPK) models in a cohort of Chinese pediatric patients with epilepsy. The PPK model was constructed using a nonlinear mixed-effects modeling approach, utilizing a dataset comprising 236 plasma concentrations of topiramate obtained from 181 pediatric patients with epilepsy. A one-compartment model combined with a proportional residual model was employed to characterize the pharmacokinetics of topiramate. Covariate analysis was performed using forward addition and backward elimination to assess the influence of covariates on the model parameters. The model was thoroughly evaluated through goodness-of-fit analysis, bootstrap, visual predictive checks, and normalized prediction distribution errors. Monte Carlo simulations were utilized to devise topiramate dosing strategies. In the final PPK models of topiramate, body weight, co-administration with oxcarbazepine, and a combined genotype of GKIR1-UGT (GRIK1 rs2832407, UGT2B7 rs7439366, and UGT1A1 rs4148324) were identified as significant covariates affecting the clearance (CL). The clearance was estimated using the formulas CL (L/h) = 0.44 × (BW⁄11.7)0.82 × eOXC for the model without genetic variants and CL (L/h) = 0.49 × (BW⁄11.7)0.81 × eOXC × eGRIK1-UGT for the model incorporating genetic variants. The volume of distribution (Vd) was estimated using the formulas Vd (L) = 6.6 × (BW⁄11.7). The precision of all estimated parameters was acceptable. Furthermore, the model demonstrated good predictability, exhibiting stability and effectiveness in describing the pharmacokinetics of topiramate. The clearance of topiramate in pediatric patients with epilepsy may be subject to the influence of factors such as body weight, co-administration with oxcarbazepine, and genetic polymorphism. In this study, PPK models were developed to better understand and account for these factors, thereby improving the precision and individualization of topiramate therapy in children with epilepsy.

Population pharmacokinetics of oxcarbazepine active metabolite in Chinese children with epilepsy.

Oxcarbazepine (OXC) is an antiepileptic drug whose efficacy is largely attributed to its monohydroxy derivative metabolite (MHD). Nevertheless, there exists significant inter-individual variability in both the pharmacokinetics and therapeutic response of this drug. The objective of this study is to explore the impact of patients' characteristics and genetic variants on MHD clearance in a population pharmacokinetic (PPK) model of Chinese pediatric patients with epilepsy. The PPK model was developed using a nonlinear mixed effects modeling method based on 231 MHD plasma concentrations obtained from 185 children with epilepsy. The one-compartment model and combined residual model were established to describe the pharmacokinetics of MHD. Forward addition and backward elimination were employed to evaluate the impact of covariates on the model parameters. The model was evaluated using goodness-of-fit, bootstrap, visual predictive checks, and normalized prediction distribution errors. In the two final PPK models, age, estimated glomerular filtration rate (eGFR), and a combined genotype of six variants (rs1045642, rs2032582, rs7668282, rs2396185, rs2304016, rs1128503) were found to significantly reduce inter-individual variability for MHD clearance. The inter-individual clearance equals to 1.38 × (Age/4.74)0.29 × (eGFR/128.66)0.25 × eθABCB-UGT-SCN-INSR for genetic variants included model and 1.30 × (Age/4.74)0.30 × (eGFR/128.66)0.23 for model without genetic variants. The precision of all parameters was deemed acceptable, and the model exhibited good predictability while remaining stable and effective. Conclusion: Age, eGFR, and genotype may play a significant role in MHD clearance in children with epilepsy. The developed PPK models hold potential utility in facilitating oxcarbazepine dose adjustment in pediatric patients. What is Known: • The adjustment of the oxcarbazepine regimen remains difficult due to the considerable inter- and intra-individual variability of oxcarbazepine pharmacokinetics. • Body weight and co-administration with enzyme-inducing antiepileptic drugs emerge as the most influential factors contributing to the pharmacokinetics of MHD. What is New: • A positive correlation was observed between eGFR and the clearance of MHD in pediatric patients with epilepsy. • We explored the influence of genetic polymorphisms on MHD clearance and identified a combined genotype (ABCB-UGT-SCN-INSR) that exhibited a significant association with MHD concentration.

A pharmacokinetic analysis of amisulpride in adult Chinese patients with schizophrenia: Impact of creatinine clearance.

To develop a stable population pharmacokinetic (PPK) model of amisulpride and to investigate the effects of covariates on the pharmacokinetic parameters in adult Chinese patients with schizophrenia. This retrospective study was carried out using 168 serum samples from 88 patients collected during routine clinical monitoring. Covariates recorded included demographic parameters (gender, age, weight), clinical parameters (serum creatinine, creatinine clearance), and intake of co-medications. The amisulpride PPK model was established using a nonlinear mixed effects modeling (NONMEM) approach. Goodness-of-fit (GOF) plots, bootstrap validation (1,000 runs), and normalized prediction distribution error (NPDE) were used in the evaluation of the final model. A one-compartment model with first-order absorption and elimination was developed. The population estimates for apparent clearance (CL/F) and apparent volume of distribution (V/F) were 32.6 L/h and 391 L, respectively. Estimated creatinine clearance (eCLcr) was a significant covariate for CL/F. The established model was: CL/F = 32.6 × (eCLcr/114.3)0.485 (L/h). The stability of the model was confirmed using GOF plots, bootstrap, and NPDE. Creatinine clearance is a major covariate which is positively correlated with CL/F. Therefore, additional dose adjustments of amisulpride may be required on the basis of eCLcr. An ethnic difference may exist in the pharmacokinetics of amisulpride, but further research is needed in order to confirm this possibility. The PPK model of amisulpride for adult Chinese schizophrenic patients established here using NONMEM, is potentially an important tool for individualizing drug dosage and therapeutic drug monitoring.

External evaluation and systematic review of population pharmacokinetic models for high-dose methotrexate in cancer patients

Several population pharmacokinetic (PPK) models have been established to optimize the therapeutic regimen and reduce the toxicity of high-dose methotrexate (HDMTX) in patients with cancer. However, their predictive performance when extrapolated to different clinical centers was unknown. In this study, we aimed to externally evaluate the predictive ability of HDMTX PPK models and determine the potential influencing factors. We searched the literature and determined the predictive performance of the selected models using methotrexate concentrations in 721 samples from 60 patients in the First Affiliated Hospital of the Navy Medical University. Prediction-based diagnostics and simulation-based normalized prediction distribution errors (NPDE) were used to evaluate the predictive performance of the models. The influence of prior information was also assessed using Bayesian forecasting, and the potential factors affecting model predictability were investigated. Thirty models extracted from published PPK studies were assessed. Prediction-based diagnostics showed that the number of compartments potentially influenced model transferability, and simulation-based NPDE indicated model misspecification. Bayesian forecasting significantly improved the predictive performance of the models. Various factors, including bioassays, covariates, and population diagnosis, influence model extrapolation. The published models were unsatisfactory for all prediction-based diagnostics, except for the 24 h methotrexate concentration monitoring and simulation-based diagnostics, making them inappropriate for direct extrapolation. Moreover, Bayesian forecasting combined therapeutic drug monitoring could improve the predictive performance of the models.

Population Pharmacokinetic Modeling of Zaltoprofen in Healthy Adults: Exploring the Dosage Regimen.

Zaltoprofen is a drug used for various pain and inflammatory diseases. Scientific and quantitative dosage regimen studies regarding its clinical application are scarce. This study aimed to discover effective covariates related to interindividual pharmacokinetic variability through population pharmacokinetic modeling for zaltoprofen and to explore dosage regimens. The bioequivalence results of healthy Korean males, biochemical analysis, and CYP2C9 genotyping information were utilized in modeling. The established model has been sufficiently verified through a bootstrap, goodness-of-fit, visual predictive check, and normalized prediction distribution error. External data sets derived from the literature were used for further model validation. The final model could be used to verify the dosage regimen through multiple exposure simulations according to the numerical change of the selected covariates. Zaltoprofen pharmacokinetics could be explained by a two-compartment with a first-order absorption model. Creatinine clearance (CrCL) and albumin were identified as effective covariates related to interindividual zaltoprofen pharmacokinetic variability, and they had positive and negative correlations with clearance (CL/F), respectively. The differences in pharmacokinetics between individuals according to CYP2C9 genetic polymorphisms (*1/*1 and *1/*3) were not significant or valid covariates. The model simulation confirmed that zaltoprofen pharmacokinetics could significantly differ as the CrCL and albumin levels changed within the normal range. Steady-state plasma exposure to zaltoprofen was significantly reduced in the group with CrCL and albumin levels of 130 mL/min and 3.5 g/dL, respectively, suggesting that dose adjustment may be necessary. This study is useful to guide precision medicine of zaltoprofen and provides scientific quantitative judgment data for its clinical applications.

Fractal Kinetic Implementation in Population Pharmacokinetic Modeling.

Compartment modeling is a widely accepted technique in the field of pharmacokinetic analysis. However, conventional compartment modeling is performed under a homogeneity assumption that is not a naturally occurring condition. Since the assumption lacks physiological considerations, the respective modeling approach has been questioned, as novel drugs are increasingly characterized by physiological or physical features. Alternative approaches have focused on fractal kinetics, but evaluations of their application are lacking. Thus, in this study, a simulation was performed to identify desirable fractal-kinetics applications in conventional modeling. Visible changes in the profiles were then investigated. Five cases of finalized population models were collected for implementation. For model diagnosis, the objective function value (OFV), Akaike's information criterion (AIC), and corrected Akaike's information criterion (AICc) were used as performance metrics, and the goodness of fit (GOF), visual predictive check (VPC), and normalized prediction distribution error (NPDE) were used as visual diagnostics. In most cases, model performance was enhanced by the fractal rate, as shown in a simulation study. The necessary parameters of the fractal rate in the model varied and were successfully estimated between 0 and 1. GOF, VPC, and NPDE diagnostics show that models with the fractal rate described the data well and were robust. In the simulation study, the fractal absorption process was, therefore, chosen for testing. In the estimation study, the rate application yielded improved performance and good prediction-observation agreement in early sampling points, and did not cause a large shift in the original estimation results. Thus, the fractal rate yielded explainable parameters by setting only the heterogeneity exponent, which reflects true physiological behavior well. This approach can be expected to provide useful insights in pharmacological decision making.

Joint population pharmacokinetic modeling of venlafaxine and O-desmethyl venlafaxine in healthy volunteers and patients to evaluate the impact of morbidity and concomitant medication.

Introduction: Venlafaxine (VEN) is a widely used dual selective serotonin/noradrenaline reuptake inhibitor indicated for depression and anxiety. It undergoes first-pass metabolism to its active metabolite, O-desmethyl venlafaxine (ODV). The aim of the present study was to develop a joint population pharmacokinetic (PPK) model to characterize their pharmacokinetic characters simultaneously. Methods: Plasma concentrations with demographic and clinical data were derived from a bioequivalence study in 24 healthy subjects and a naturalistic TDM setting containing 127 psychiatric patients. A parent-metabolite PPK modeling was performed with NONMEM software using a non-linear mixed effect modeling approach. Goodness of fit plots and normalized prediction distribution error method were used for model validation. Results and conclusion: Concentrations of VEN and ODV were well described with a one-compartment model incorporating first-pass metabolism. The first-pass metabolism was modeled as a first-order conversion. The morbid state and concomitant amisulpride were identified as two significant covariates affecting the clearance of VEN and ODV, which may account for some of the variations in exposure. This model may contribute to the precision medication in clinical practice and may inspire other drugs with pre-system metabolism.

Population pharmacokinetics and dosing optimization of olanzapine in Chinese paediatric patients: Based on the impact of sex and concomitant valproate on clearance.

Olanzapine is an atypical antipsychotic drug used for mental disorders. There are limited studies providing sufficient pharmacokinetic data, thus the variability of concentrations of olanzapine used in Chinese paediatric patients aged 10 to 17 years remains to be evaluated. Therapeutic drug monitoring data were collected from 151 paediatric patients aged 10 to 17 years who received olanzapine. The model was developed with a NONMEM software program. The final model validation and evaluation were assessed by bootstrap, diagnostic scatter plots, and normalized prediction distribution error (NPDE). Regimens of different dosages were simulated to reach the target concentration levels of 20 ng/ml, by using the final model with typical parameters. The one-compartment model was considered the best fit for the data. Typical estimates of the absorption rate constant (Ka), apparent clearance (CL/F), and apparent distribution volume (V/F) in the final model were 0.142 h-1 , 15.4L/h, and 322 L, respectively. Sex and concomitant valproate (VPA) were included as significant predictors of olanzapine clearance, which was described by the following equation: CL/F=15.4 × (1 + 0.546 × SEX) × (1 + 0.264 × VPA). Results of Monte-Carlo simulation suggested that male paediatric patients with concomitant VPA were advised to take no less than 15 mg per day of olanzapine orally, and in female paediatric patients with concomitant VPA, a dosing regimen of 10mg may be sufficient to achieve the therapeutic range of olanzapine. Our results identified concomitant valproate and sex as significant covariates in olanzapine population pharmacokinetics. Our model may be a useful tool for recommending dosage adjustments for physicians. The pharmacokinetics of olanzapine in patients aged 10 to 17 years was generally similar to that of adults and the elderly.

Population pharmacokinetics model for escitalopram in Chinese psychiatric patients: effect of CYP2C19 and age.

Objective: To establish a population pharmacokinetic model in Chinese psychiatric patients to characterize escitalopram pharmacokinetic profile to identify factors influencing drug exposure, and through simulation to compare the results with the established therapeutic reference range. Methods: Demographic information, dosing regimen, CYP2C19 genotype, concomitant medications, and liver and kidney function indicators were retrospectively collected for inpatients taking escitalopram with therapeutic drug monitoring from 2018 to 2021. Nonlinear mixed-effects modeling was used to model the pharmacokinetic characteristics of escitalopram. Goodness-of-fit plots, bootstrapping, and normalized prediction distribution errors were used to evaluate the model. Simulation for different dosing regimens was based on the final estimations. Results: The study comprised 106 patients and 337 measurements of serum sample. A structural model with one compartment with first-order absorption and elimination described the data adequately. The population-estimated apparent volume of distribution and apparent clearance were 815 and 16.3 L/h, respectively. Age and CYP2C19 phenotype had a significant effect on the apparent clearance (CL/F). CL/F of escitalopram decreased with increased age, and CL/F of poor metabolizer patients was significantly lower than in extensive and immediate metabolizer patients. The final model-based simulation showed that the daily dose of adolescents with poor metabolizer might be as high as 15 mg or 20 mg and referring to the therapeutic range for adults may result in overdose and a high risk of adverse effects in older patients. Conclusion: A population pharmacokinetics model of escitalopram was successfully created for the Chinese population. Depending on the age of the patients, CYP2C19 genotype and serum drug concentrations throughout treatment are required for adequate individualization of dosing regimens. When developing a regimen for older patients, especially those who are poor metabolizers, vigilance is required.

External Evaluation of Population Pharmacokinetic Models of Busulfan in Chinese Adult Hematopoietic Stem Cell Transplantation Recipients.

Objective: Busulfan (BU) is a bi-functional DNA-alkylating agent used in patients undergoing hematopoietic stem cell transplantation (HSCT). Over the last decades, several population pharmacokinetic (pop PK) models of BU have been established, but external evaluation has not been performed for almost all models. The purpose of the study was to evaluate the predictive performance of published pop PK models of intravenous BU in adults using an independent dataset from Chinese HSCT patients, and to identify the best model to guide personalized dosing. Methods: The external evaluation methods included prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting. In prediction-based diagnostics, the relative prediction error (PE%) was calculated by comparing the population predicted concentration (PRED) with the observations. Simulation-based diagnostics included the prediction- and variability-corrected visual predictive check (pvcVPC) and the normalized prediction distribution error (NPDE). Bayesian forecasting was executed by giving prior one to four observations. The factors influencing the model predictability, including the impact of structural models, were assessed. Results: A total of 440 concentrations (110 patients) were obtained for analysis. Based on prediction-based diagnostics and Bayesian forecasting, preferable predictive performance was observed in the model developed by Huang et al. The median PE% was -1.44% which was closest to 0, and the maximum F20 of 57.27% and F30 of 72.73% were achieved. Bayesian forecasting demonstrated that prior concentrations remarkably improved the prediction precision and accuracy of all models, even with only one prior concentration. Conclusion: This is the first study to comprehensively evaluate published pop PK models of BU. The model built by Huang et al. had satisfactory predictive performance, which can be used to guide individualized dosage adjustment of BU in Chinese patients.