Normalized Prediction Distribution Errors Research Articles

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.

External evaluation of population pharmacokinetic models for voriconazole in Chinese adult patients with hematological malignancy.

Patients with hematological malignancies are prone to invasive fungal disease due to long-term chemotherapy or radiotherapy. Voriconazole is a second-generation triazole broad-spectrum antibiotic used to prevent or treat invasive fungal infections. Many population pharmacokinetic (pop PK) models have been published for voriconazole, and various diagnostic methods are available to validate the performance of these pop PK models. However, most of the published models have not been strictly evaluated externally. The purpose of this study is to evaluate these models externally and assess their predictive capabilities. The external dataset consists of adults receiving voriconazole treatment at Fujian Medical University Union Hospital. We re-established the published models based on their final estimated values in the literature and used our external dataset for initial screening. Each model was evaluated based on the following outcomes: prediction-based diagnostics, prediction- and variability-corrected visual predictive check (pvcVPC), normalized prediction distribution errors (NPDE), and Bayesian simulation results with one to two prior observations. A total of 237 samples from 166 patients were collected as an external dataset. After screening, six candidate models suitable for the external dataset were finally obtained for comparison. Among the models, none demonstrated excellent predictive performance. Bayesian simulation shows that all models' prediction precision and accuracy were significantly improved when one or two prior concentrations were given. The published pop PK models of voriconazole have significant differences in prediction performance, and none of the models could perfectly predict the concentrations of voriconazole for our data. Therefore, extensive evaluation should precede the adoption of any model in clinical practice.

External validation of vancomycin population pharmacokinetic models in ten cohorts of infected Chinese patients

Appropriate dosage of vancomycin is critical for safety and efficacy in treating infectious diseases. Various population pharmacokinetic (PPK) models have been established. To lay a foundation for the clinical application, it is important to perform external validation of the published model. The aim of this study was to find the most suitable vancomycin PPK model for treatment of infection in China amongst all studied models developed for adults. A systematic literature search of published population vancomycin pharmacokinetic analyses was performed using the PubMed database. The identified models were evaluated in ten independent cohorts from China. Nonlinear mixed-effects modeling (NONMEM) was used for data analysis. The median prediction error (MPE), the distribution of prediction error (PE), and normalized prediction distribution errors (NPDE) were calculated and described. Predictive performance was evaluated by bias and accuracy. A total of 449 vancomycin concentrations from 397 infected participants were used for external validation. The MPE of the three models from the Chinese population was less than 10%. Half of the models had an acceptable MPE. For patients with different site infections and different renal functions, each model differed in its predictive ability to some extent. The NPDE results showed that all models had an obvious bias. None of the models had good performance in both prediction- and simulation-based diagnostics. Carrying out sufficient external validation of the PPK model before clinical application is of utmost importance. In clinical practice, the model's population closest to the application population should be used.

Population Pharmacokinetics and Dosing Regimen Optimization of Latamoxef in Chinese Children.

The present study aimed to establish population pharmacokinetic models of latamoxef, as well as its R- and S-epimers, and generate findings to guide the individualized administration of latamoxef in pediatric patients. A total of 145 in-hospital children aged 0.08–10.58 years old were included in this study. Three population pharmacokinetic models of latamoxef and its R- and S-epimers were established. The stability and predictive ability of the final models were evaluated by utilizing goodness-of-fit plots, nonparametric bootstrapping, and normalized prediction distribution errors. The final model of total latamoxef was considered as a basis for the dosing regimen. A two-compartment model with first-order elimination best described the pharmacokinetics of total latamoxef. The population typical values of total latamoxef were as follows: central compartment distribution volume (V1) of 4.84 L, peripheral compartment distribution volume (V2) of 16.18 L, clearance (CL) of 1.00 L/h, and inter-compartmental clearance (Q) of 0.97 L/h. Moreover, R-epimer has a higher apparent volume of distribution and lower clearance than S-epimer. Body surface area (BSA) was identified as the most significant covariate to V, CL, and Q. Specific recommendations are given for dosage adjustment in pediatric patients based on BSA. This study highlights that a BSA-normalized dose of latamoxef was required when treating different bacteria to reach the therapeutic target more effectively.

Published population pharmacokinetic models of valproic acid in adult patients: a systematic review and external validation in a Chinese sample of inpatients with bipolar disorder

ABSTRACT Background This study reviewed all published valproic acid (VPA) population pharmacokinetic (PPK) models in adult patients and assessed them using external validation methods to determine predictive performance. Methods Thirteen published PPK models (labeled with letters A to M) not restricted to children were identified in PubMed, Embase, and Web of Science databases. They were evaluated in a sample totaling 411 serum concentrations from 146 adult inpatients diagnosed with bipolar disorder in a Chinese hospital. Serum concentrations of VPA were analyzed by validated ultra-performance liquid chromatography-tandem mass spectrometry. Performance was assessed by four tests (prediction-based diagnostics, visual predictive checks, normalized prediction distribution error, and Bayesian forecasting). Results Models K and L, developed in large samples of Chinese and Thai patients, showed good performance in our Chinese dataset. Models H and J demonstrated good performance in 2 and 3 of the 4 tests, respectively. Another seven models exhibited intermediate performance. The models with the worst performance, F and M, could not be improved by Bayesian forecasting. Conclusion In our validation study, the most important factors contributing to good performance were absence of children, Asian ethnicity, one-compartment models, and inclusion of body weight and VPA dose in previously published models.

External Evaluation of Risperidone Population Pharmacokinetic Models Using Opportunistic Pediatric Data.

Risperidone is approved to treat schizophrenia in adolescents and autistic disorder and bipolar mania in children and adolescents. It is also used off-label in younger children for various psychiatric disorders. Several population pharmacokinetic models of risperidone and 9-OH-risperidone have been published. The objectives of this study were to assess whether opportunistically collected pediatric data can be used to evaluate risperidone population pharmacokinetic models externally and to identify a robust model for precision dosing in children. A total of 103 concentrations of risperidone and 112 concentrations of 9-OH-risperidone, collected from 62 pediatric patients (0.16–16.8 years of age), were used in the present study. The predictive performance of five published population pharmacokinetic models (four joint parent-metabolite models and one parent only) was assessed for accuracy and precision of the predictions using statistical criteria, goodness of fit plots, prediction-corrected visual predictive checks (pcVPCs), and normalized prediction distribution errors (NPDEs). The tested models produced similarly precise predictions (Root Mean Square Error [RMSE]) ranging from 0.021 to 0.027 nmol/ml for risperidone and 0.053–0.065 nmol/ml for 9-OH-risperidone). However, one of the models (a one-compartment mixture model with clearance estimated for three subpopulations) developed with a rich dataset presented fewer biases (Mean Percent Error [MPE, %] of 1.0% vs. 101.4, 146.9, 260.4, and 292.4%) for risperidone. In contrast, a model developed with fewer data and a more similar population to the one used for the external evaluation presented fewer biases for 9-OH-risperidone (MPE: 17% vs. 69.9, 47.8, and 82.9%). None of the models evaluated seemed to be generalizable to the population used in this analysis. All the models had a modest predictive performance, potentially suggesting that sources of inter-individual variability were not entirely captured and that opportunistic data from a highly heterogeneous population are likely not the most appropriate data to evaluate risperidone models externally.

Population Pharmacokinetics Analysis and Dosing Simulations Of Meropenem in Critically Ill Patients with Pulmonary Infection

PurposeThis study aimed to develop a population pharmacokinetic (PPK) model for meropenem to optimize dosing regimens for critically ill patients with pulmonary infection. Patients and methodsThis prospective PPK study of meropenem was conducted on a pooled dataset of 236 blood samples obtained from 48 patients with pulmonary infection in the intensive care unit. Meropenem plasma concentrations were measured by a validated high-performance liquid chromatography-tandem mass spectrometry method, and the data were analyzed using NONMEM. The effect of covariates on meropenem pharmacokinetics was investigated. The probability of target attainment (PTA) to achieve the target of 100% fT>MIC at the proposed dosage regimens were investigated by Monte Carlo simulations. ResultsA two-compartment model adequately described the data with estimated glomerular filtration rate (eGFR) as a covariate significantly associated with the clearance (CL) from the central compartment. The typical value of CL was 7.48 L/h, with an eGFR adjustment factor of 0.0103 mL•1.73 m2/min, and the typical values of volume of the central compartment (V1), peripheral compartmental clearance (Q), and volume of the peripheral compartment (V2) were 15.9 L, 15.8 L/h, and 14.8 L, respectively. The goodness-of-fit plots, normalized prediction distribution error, and visual predictive checks showed good fitting and predictability of the final PPK model. When eGFR was >90 mL/min/1.73 m2, and there was a short duration of infusion (<60min), it was difficult for the probability target attainment (PTA) to reach >90% for MIC ≥ 2. Continuous infusion and frequent administration were necessary to achieve the target of 100% fT>MIC for critically ill patients with pulmonary infection. ConclusionTo achieve the optimal PTA, meropenem must be administered by frequent administration or continuously by an intravenous infusion. Our findings provide important information to optimize the meropenem regime in critically ill patients with pulmonary infection depending on eGFR values.

Population pharmacokinetics of valproic acid in adult Chinese patients with bipolar disorder.

To develop and validate a population pharmacokinetic (PPK) model of valproic acid (VPA) in adult Chinese patients with bipolar disorder, and provide guidance for individualized therapy in this population. A total of 1104 serum concentrations from 272 patients were collected in this study. The data analysis was performed using a nonlinear mixed-effects modeling approach. Covariates included demographic parameters, biological characteristics, and concomitant medications. Bootstrap validation (1000 runs), normalized prediction distribution error (NPDE), and external validation of 50 patients were employed to evaluate the final model. A one-compartment model with first-order absorption and elimination was developed for VPA extended-release tablets. VPA clearance was significantly influenced by three variables: sex (12% higher in male patients), daily dose (increasing with the 0.13 exponent), and body weight (increasing with the 0.56 exponent). Typical values for the absorption rate constant (Ka), apparent clearance (CL/F), and apparent distribution volume (V/F) for a female patient weighing 70kg administered VPA 1000mg/day were 0.18h-1, 0.46 L/h, and 12.84 L, respectively. The results of model evaluation indicated a good stable and precise performance of the final model. A qualified PPK model of VPA was developed in Chinese patients with bipolar disorder. This model could be used as a suitable tool for the personalization of VPA dosing for bipolar patients.

Can Published Population Pharmacokinetic Models of Busulfan Be Used for Individualized Dosing in Chinese Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation? An External Evaluation.

Busulfan is a bifunctional alkylating agent that is widely used before hematopoietic stem cell transplantation (HSCT), in combination with other chemotherapeutic drugs. As of 2020, there is no population pharmacokinetic (popPK) model for busulfan in Chinese pediatric patients. A systemic external evaluation of 11 published popPK models was conducted in Chinese pediatric patients undergoing HSCT. Forty pediatric patients were enrolled in this study, with a total of 183 blood concentrations. The relative prediction error (PE%), median PE%, median absolute PE%, and percentage of PE% within ±20% and ±30% were calculated in prediction-based diagnostics. Simulation-based diagnostics were conducted through a prediction- and variability-corrected visual predictive check and the normalized prediction distribution error. The relative individual prediction error was calculated using Bayesian forecasting with 1 to 3 concentration points. The 1-compartment open linear popPK model, which was built by Su-jin Rhee et al (model H), incorporating the patient's body surface area, age, dosing day, and aspartate aminotransferase as significant covariates had preferable predictability than other popPK models. In prediction-based diagnostics, the median PE%, percentage of PE% within ±20%, and percentage of PE% within ±30% of model H were 8.48%, 45.35%, and 59.56%, respectively. The normalized prediction distribution error of model H showed that it followed the normal distribution. Based on Bayesian forecasting, model H showed good predictive performance. Thus, model H was the most appropriate model that can be used clinically for individualized dosage adjustments in Chinese pediatric HSCT patients.

Estimation of Mycophenolic Acid Exposure in Heart Transplant Recipients by Population Pharmacokinetic and Limited Sampling Strategies.

Purpose: The aim of this study is i) to establish a strategy to estimate the area under the curve of the dosing interval (AUC0–12h) of mycophenolic acid (MPA) in the heart transplant recipients and ii) to find the covariates that significantly affect the pharmacokinetics of MPA exposure. Methods: This single-center, prospective, open-label, observational study was conducted in 91 adult heart transplant recipients orally taking mycophenolate mofetil dispersible tablets. Samples collected intensively and sparsely were analyzed by the enzyme-multiplied immunoassay technique, and all the data were used in PPK modeling. Potential covariates were tested stepwise. The goodness-of-fit plots, the normalized prediction distribution error, and prediction-corrected visual predictive check were used for model evaluation. Optimal sampling times by ED-optimal strategy and multilinear regression (MLR) were analyzed based on the simulated data by the final PPK model. Moreover, using intensive data from 14 patients, the accuracy of AUC0–12h estimation was evaluated by Passing–Bablok regression analysis and Bland–Alman plots for both the PPK model and MLR equation. Results: A two-compartment model with first-order absorption and elimination with a lag time was chosen as the structure model. Co-medication of proton pump inhibitors (PPIs), estimated glomerular filtration rate (eGFR), and albumin (ALB) were found to significantly affect bioavailability (F), clearance of central compartment (CL/F), and the distribution volume of the central compartment (V2/F), respectively. Co-medication of PPIs decreased F by 27.6%. When eGFR decreased by 30 ml/min/1.73 m2, CL/F decreased by 23.7%. However, the impact of ALB on V2/F was limited to MPA exposure. The final model showed an adequate fitness of the data. The optimal sampling design was pre-dose and 1 and 4 h post-dose for pharmacokinetic estimation. The best-fit linear equation was finally established as follows: AUC0–12h = 3.539 × C0 + 0.288 × C0.5 + 1.349 × C1 + 6.773 × C4.5. Conclusion: A PPK model was established with three covariates in heart transplant patients. Co-medication of PPIs and eGFR had a remarkable impact on AUC0–12h of MPA. A linear equation was also concluded with four time points as an alternative way to estimate AUC0–12h for MPA.

Modeling and Simulation for Individualized Therapy of Amisulpride in Chinese Patients with Schizophrenia: Focus on Interindividual Variability, Therapeutic Reference Range and the Laboratory Alert Level.

PurposeTo explain the high inter-individual variability (IIV) and the frequency of exceeding the therapeutic reference range and the laboratory alert level of amisulpride, a population pharmacokinetic (PPK) model in Chinese patients with schizophrenia was built based on therapeutic drug monitoring (TDM) data to guide individualized therapy.Patients and MethodsPlasma concentration data (330 measurements from 121 patients) were analyzed using a nonlinear mixed-effects modeling (NONMEM) approach with first-order conditional estimation with interaction (FOCE I). The concentrations of amisulpride were detected by HPLC-MS/MS. Age, weight, sex, combination medication history and renal function status were evaluated as main covariates. The model was internally validated using goodness-of-fit, bootstrap and normalized prediction distribution error (NPDE). Recommended dosage regimens for patients with key covariates were estimated on the basis of Monte Carlo simulations and the established model.ResultsA one-compartment model with first-order absorption and elimination was found to adequately characterize amisulpride concentration in Chinese patients with schizophrenia. The population estimates of the apparent volume of distribution (V/F) and apparent clearance (CL/F) were 12.7 L and 1.12 L/h, respectively. Age significantly affected the clearance of amisulpride and the final model was as follows: CL/F=1.04×(AGE/32)−0.624 (L/h). To avoid exceeding the laboratory alert level (640 ng/mL), the model-based simulation results showed that the recommended dose of amisulpride was no more than 600 mg/d for patients aged 60 years, 800 mg/d for those aged 40 years and 1200 mg/d for those aged 20 years, respectively.ConclusionDosage optimization of amisulpride can be carried out according to age to reduce the risk of adverse reactions. The model can be used as a suitable tool for designing individualized therapy for Chinese patients with schizophrenia.

External Evaluation of Population Pharmacokinetic Models and Bayes-Based Dosing of Infliximab.

Despite the well-demonstrated efficacy of infliximab in inflammatory diseases, treatment failure remains frequent. Dose adjustment using Bayesian methods has shown in silico its interest in achieving target plasma concentrations. However, most of the published models have not been fully validated in accordance with the recommendations. This study aimed to submit these models to an external evaluation and verify their predictive capabilities. Eight models were selected for external evaluation, carried out on an independent database (409 concentrations from 157 patients). Each model was evaluated based on the following parameters: goodness-of-fit (comparison of predictions to observations), residual error model (population weighted residuals (PWRES), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE)), and predictive performances (prediction-corrected visual predictive checks (pcVPC) and Bayesian simulations). The performances observed during this external evaluation varied greatly from one model to another. The eight evaluated models showed a significant bias in population predictions (from −7.19 to 7.38 mg/L). Individual predictions showed acceptable bias and precision for six of the eight models (mean error of −0.74 to −0.29 mg/L and mean percent error of −16.6 to −0.4%). Analysis of NPDE and pcVPC confirmed these results and revealed a problem with the inclusion of several covariates (weight, concomitant immunomodulatory treatment, presence of anti-drug antibodies). This external evaluation showed satisfactory results for some models, notably models A and B, and highlighted several prospects for improving the pharmacokinetic models of infliximab for clinical-biological application.

External evaluation of published population pharmacokinetic models of polymyxin B.

Several population pharmacokinetics (popPK) models for polymyxin B have been constructed to optimize therapeutic regimens. However, their predictive performance remains unclear when extrapolated to different clinical centers. Therefore, this study aimed to evaluate the predictive ability of polymyxin B popPK models. A literature search was conducted, and the predictive performance was determined for each selected model using an independent dataset of 20 patients (92 concentrations) from the Third Xiangya Hospital. Prediction- and simulation-based diagnostics were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. Eight published studies were evaluated. In prediction-based diagnostics, the prediction error within ± 30% was over 50% in two models. In simulation-based diagnostics, the prediction- and variability-corrected visual predictive check (pvcVPC) showed satisfactory predictivity in three models, while the normalized prediction distribution error (NPDE) tests indicated model misspecification in all models. Bayesian forecasting demonstrated a substantially improvement in the model predictability even with one prior observation. Not all published models were satisfactory in prediction- and simulation-based diagnostics; however, Bayesian forecasting improved the predictability considerably with priors, which can be applied to guide polymyxin B dosing recommendations and adjustments for clinicians.

Establishment of a population pharmacokinetics model of vancomycin in 94 infants with septicemia and its application in individualized therapy

BackgroundWe aim to develop a population pharmacokinetics (PopPK) model of vancomycin for the treatment of septicemia in infants younger than one year. Factors influence of the PK was investigated to optimize vancomycin dosing regimen.MethodsThe nonlinear mixed effects modelling software (NONMEM) was used to develop the PopPK model of vancomycin. The stability and predictive ability of the final model were assessed by using normalized prediction distribution errors (NPDE) and bootstrap methods. The final model was subjected to Monte Carlo simulation in order to determine the optimal dose.ResultsA total of 205 trough and peak concentrations in 94 infants (0–1 year of age) with septicemia were analyzed. The interindividual variability of the PK parameter was described by the exponential model. Residual error was better described by the proportional model than the mixed proportional and addition models. Serum creatinine concentration and body weight are the major factors that affect the PK parameters of vancomycin. The clearance was shown to be higher when ceftriaxone was co-treated. More than two model evaluation methods showed better stability than the base model, with superior predictive performance, which can develop individualized dosing regimens for clinical reference. Through prediction of final model, the trough concentration was more likely < 5 mg/L when a routine dose of 10 mg/kg is administered every 6 h to 3–9-month-old infants. Therefore, the dose should be increased in the treatment of infant septicemia.ConclusionsThe stable and effective PopPK model of vancomycin in Chinese infants with septicemia was established. This model has satisfactory predictive ability for clinically individualized dosing regimens in this vulnerable population.

Estrogen profile- and pharmacogenetics-based lamotrigine dosing regimen optimization: Recommendations for pregnant women with epilepsy

During pregnancy, various physiological changes occur that can alter the pharmacokinetics of antiepileptic drugs, such as lamotrigine (LTG). Anticipating the change in LTG dose required to achieve a pre-pregnancy target concentration is challenging. This study aimed to develop a refined population pharmacokinetic (PopPK) model of LTG in pregnant women with epilepsy (WWE) to identify factors explaining the variability in pharmacokinetics and to establish a model-informed individualized dosing regimen. On that basis, a coarsened model containing only clinical variables was also developed to examine its predictive performance compared to the refined model. In total, 322 concentration-time points from 51 pregnant WWE treated with LTG were employed to establish a refined PopPK model that included endogenous estrogen profiles, variants of candidate genes encoding LTG-metabolizing enzymes and -transporter proteins, and other clinical variables and a coarsened model that included only clinical variables, respectively. Data from an additional 11 patients were used for external validation of these two models. A nonlinear mixed-effect modeling approach was used for PopPK analysis of LTG. The standard goodness-of-fit method, bootstrap, normalized prediction distribution errors and external evaluation were adopted to estimate the stability and predictive performance of the candidate models. Akaike information criterion (AIC) was used to compare the goodness of fit between these two models. A lower AIC indicates a better fit of the data and the preferred model. Recommended dosing regimens for pregnant WWE were selected using Monte Carlo simulation based on the established optimal model. In the refined PopPK model, the population mean of apparent LTG clearance (CL/F) in pregnant WWE was estimated to be 2.82 L/h, with an inter-individual variability of 23.6%. PopPK analysis indicated that changes in estrogen profile during pregnancy were the predominant reason for the significant variations in LTG-CL/F. Up to the 3rd trimester, the concentration accumulation effect of E2 increased LTG-CL/F by 5.109 L/h from baseline levels. Contrary to effect of E2, E3 as the main circulating estrogen in pregnancy with a peak value of 34.41 ng/mL is 1000-fold higher than that in non-pregnancy reduced LTG-CL/F by 1.413 L/h. In addition, the UGT2B7 rs4356975 C > T and ABCB1 rs1128503 A > G variants may contribute to a better understanding of the inter-individual variability in LTG-CL/F. LTG-CL/F was 1.66-fold higher in UGT2B7 rs4356975 CT or TT genotype carriers than in CC genotype carriers. In contrast, ABCB1 rs1128503 GG genotype carriers had only 71.9% of the LTG-CL/F of AA or AG genotype carriers. In the coarsened PopPK model, the gestational age was a promising predictor of changes in LTG-CL/F. When comparing these two models, the refined PopPK model was favored over the coarsened PopPK model (AIC = −30.899 vs. −20.017). Monte Carlo simulation based on optimal PopPK model revealed that the LTG dosage administered to carriers of the UGT2B7 rs4356975 CT or TT genotype required a 33–50% increase to reach the pre-pregnancy target concentration, and carriers of the ABCB1 rs1128503 GG genotype required a 33–66% lower dose of LTG than carriers of the ABCB1 rs1128503 AA or AG genotype. Changes in estrogen profile during pregnancy was a better predictor of variations in LTG-CL/F than gestational age. The developed model based on estrogen profile and pharmacogenetics can serve as a foundation for further optimization of dosing regimens of LTG in pregnant WWE.

Systematic external evaluation of reported population pharmacokinetic models of vancomycin in Chinese children and adolescents.

Various population pharmacokinetic (PopPK) models for vancomycin in children and adolescents have been constructed to optimize the therapeutic regimen of vancomycin. However, little is known about their predictive performance when extrapolated to different clinical centres. Therefore, the aim of this study was to externally validate the predictability of vancomycin PopPK model when extrapolated to different clinical centres and verify its applicability in an independent data set. The published models were screened from the literature and evaluated using an external data set of a total of 451 blood concentrations of vancomycin measured in 220 Chinese paediatric patients. Prediction- and simulation-based diagnostics and Bayesian forecasting were performed to evaluate the predictive performance of the models. Ten published PopPK models were assessed. Prediction-based diagnostics showed that none of the investigated models met all the standards (median prediction error (MDPE) ≤ ±20%, median absolute prediction error (MAPE) ≤30%, PE% within ±20% (F20 ) ≥35% and PE% within ±30% (F30 ) ≥50%), indicating unsatisfactory predictability. In simulation-based diagnostics, both the visual predictive checks (VPC) and the normalized prediction distribution error (NPDE) indicated misspecification in all models. Bayesian forecasting results showed that the accuracy and precision of individual predictions could be significantly improved with one or two prior observations, but frequent monitoring might not be necessary in the clinic, since Bayesian forecasting identified that greater number of samples did not significantly improve the predictability. Model 3 established by Moffett et al showed better predictability than other models. The 10 published models performed unsatisfactorily in prediction- and simulation-based diagnostics; none of the published models was suitable for designing the initial dosing regimens of vancomycin. Pharmacokinetic characteristics and covariates, such as weight, renal function, age and underlying disease should be taken into account when extrapolating the vancomycin model. Bayesian forecasting combined with therapeutic drug monitoring based on model 3 can be used to adjust vancomycin dosing regimens.

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

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. 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. 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 and Dose Optimization of Ganciclovir in Critically Ill Children.

Objective: The present study aims to establish a population pharmacokinetic model of ganciclovir and optimize the dosing regimen in critically ill children suffering from cytomegalovirus related disease. Methods: A total of 104 children were included in the study. The population pharmacokinetic model was developed using the Phoenix NLME program. The final model was validated by diagnostic plots, nonparametric bootstrap, visual predictive check, and normalized prediction distribution errors. To further evaluate and optimize the dosing regimens, Monte Carlo simulations were performed. Moreover, the possible association between systemic exposure and hematological toxicity were also monitored in the assessment of adverse events. Results: The ganciclovir pharmacokinetics could be adequately described by a one-compartment model with first-order elimination along with body weight and estimated glomerular filtration rate as significant covariates. As showed in this study, the typical population parameter estimates of apparent volume of distribution and apparent clearance were 11.35 L and 5.23 L/h, respectively. Simulations indicated that the current regimen at a dosage of 10 mg/kg/d would result in subtherapeutic exposure, and elevated doses might be required to reach the target ganciclovir level. No significant association between neutropenia, the most frequent toxicity reported in our study (19.23%), and ganciclovir exposure was observed. Conclusion: A population pharmacokinetic model of intravenous ganciclovir for critically ill children with cytomegalovirus infection was successfully developed. Results showed that underdosing of ganciclovir was relatively common in critically ill pediatric patients, and model-based approaches should be applied in the optimizing of empiric dosing regimens.

Population Pharmacokinetics of Hydroxychloroquine in COVID-19 Patients: Implications for Dose Optimization.

Background and ObjectiveIn the absence of characterization on pharmacokinetics and reference concentrations for hydroxychloroquine in COVID-19 patients, the dose and treatment duration for hydrochloroquine are currently empirical, mainly based on in vitro data, and may vary across national guidelines and clinical study protocols. The aim of this paper is to describe the pharmacokinetics of hydroxychloroquine in COVID-19 patients, considered to be a key step toward its dosing optimization.MethodsWe have developed a population pharmacokinetic model for hydroxychloroquine in COVID-19 patients using prospectively collected pharmacokinetic data from patients either enrolled in a clinical trial or treated with hydroxychloroquine as part of standard of care in two tertiary Belgian hospitals.ResultsThe final population pharmacokinetic model was a one-compartment model with first-order absorption and elimination. The estimated parameter values were 9.3/h, 860.8 L, and 15.7 L/h for the absorption rate constant, the central compartment volume, and the clearance, respectively. The bioavailability factor was fixed to 0.74 based on previously published models. Model validations by bootstraps, prediction corrected visual predictive checks, and normalized prediction distribution errors gave satisfactory results. Simulations were performed to compare the exposure obtained with alternative dosing regimens.ConclusionThe developed models provide useful insight for the dosing optimization of hydroxychloroquine in COVID-19 patients. The present results should be used in conjunction with exposure-efficacy and exposure-safety data to inform optimal dosing of hydroxychloroquine in COVID-19.Electronic supplementary materialThe online version of this article (10.1007/s13318-020-00648-y) contains supplementary material, which is available to authorized users.