Predictive performance of reported population pharmacokinetic models of vancomycin in Chinese adult patients

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.

Evaluation of Dose Adjustment in Patients With Renal Impairment at Muhimbili National Hospital in Tanzania

Background: The burden of renal diseases is increasing in developing countries like Tanzania. Drug accumulation exposes patients with renal impairment to drug toxicity that may lead to adverse drug reactions, poor adherence to treatment, and increased healthcare costs. There is limited information on the appropriateness of dosage regimen adjustment for patients with renal impairment, particularly in developing countries such as Tanzania. This study aimed to investigate the appropriateness of drug dosing in hospitalized patients with renal impairment in Tanzania. Methods: This was a retrospective cross-sectional study. It was conducted between November 2019 and April 2020 amongst hospitalized patients at Muhimbili National Hospital. All enrolled patients had serum creatinine levels ≥1.2 mg/dL and taking at least one drug requiring dosage regimen adjustment. Creatinine clearance was calculated from patient serum creatinine using the Cockcroft-Gault equation. Drug dosing appropriateness was determined by comparing the current practice with tertiary references. The relationship between the patient's baseline characteristics and the rate of dosage regimen adjustment was determined using the X2 test. Univariate and multivariate logistic regression analysis evaluated the predictors of dosing adjustment. Results: Most of the enrolled patients, 269 (98.9%) had comorbidities. Of the medication orders included in the final analysis, 372 (27%) needed dosage regimen adjustment. Out of the 372 medication orders, not adjusted were 168 (45.2%), inappropriately adjusted 105 (28.2%), and appropriately adjusted were only 99 (26.6%). In this study, 212 (77.9%) patients received at least one drug with an incorrect dosage regimen. Females and those with level 4 renal impairment patients were more likely to have their doses appropriately adjusted compared to their counterparts. Conclusions: In this study, about three-quarters of the patients received at least one drug with an incorrect dosage regimen. Thus, appropriate measures such as the availability of national guidelines and clinical decision support systems for drug dosing adjustment in patients' renal impairment should be in place.

External validation of population pharmacokinetic models of vancomycin in postoperative neurosurgical patients

OBJECTIVE

Vancomycin is commonly used in the prevention and treatment of intracranial infections in postoperative neurosurgical patients with narrow therapeutic window and large pharmacokinetic variations. Several population pharmacokinetic (PPK) models of vancomycin have been established for neurosurgical patients. But comprehensive external evaluation has not been performed for almost all models. The objective of this study was to evaluate the predictive ability of published vancomycin PPK models in adult postoperative neurosurgical patients using an independent dataset.

METHOD

PubMed, Embase and China National Knowledge Internet databases were searched to identify published vancomycin PPK models in adult postoperative neurosurgical patients. Prediction-based and simulation-based diagnostics were used to evaluate model predictability. Bayesian forecasting was used to assess the influence of prior concentration on model prediction performance.

RESULT

A total of 763 vancomycin plasma concentrations from 493 postoperative neurosurgical patients were included in the external dataset. Eight population pharmacokinetic models of vancomycin in postoperative neurosurgical patients were included and evaluated. The model by Zhang et al. exhibited the best predictive performance in prediction-based diagnostics and prediction-corrected visual predictive checks, followed by the model by Shen et al. The predictive performance of other models was not satisfactory. The normalized predictive distribution error test shows that none of the models is suitable to describe our data. The predictive performance of vancomycin models was obviously improved by maximum a posteriori Bayesian forecasting.

CONCLUSION

The published PPK models for adult postoperative neurosurgical patients show extensive variation in predictive performance in our patients. Although it is challenging to recommend initial doses of vancomycin from these predictive models, the combination of model-based prediction and therapeutic drug monitoring can be used for dose optimization.

Implementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patients

In individualized therapy, the Bayesian approach integrated with population pharmacokinetic models (PopPK) for predictions together with therapeutic drug monitoring (TDM) to maintain adequate objectives is useful to maximize the efficacy and minimize the probability of toxicity of vancomycin in critically ill patients. Although there are limitations to implementation, model-informed precision dosing (MIPD) is an approach to integrate these elements, which has the potential to optimize the TDM process and maximize the success of antibacterial therapy. The objective of this work was to present an app for individualized therapy and perform a validation of the implemented vancomycin PopPK models. A pragmatic approach was used for selecting the models of Llopis, Goti and Revilla for developing a Shiny app with R. Through ordinary differential equation (ODE)-based mixed effects models from the mlxR package, the app simulates the concentrations' behavior, estimates whether the model was simulated without variability and predicts whether the model was simulated with variability. Moreover, we evaluated the predictive performance with retrospective trough concentration data from patients admitted to the adult critical care unit. Although there were no significant differences in the performance of the estimates, the Llopis model showed better accuracy (mean 80.88%; SD 46.5%); however, it had greater bias (mean -34.47%, SD 63.38%) compared to the Revilla et al. (mean 10.61%, SD 66.37%) and Goti et al. (mean of 13.54%, SD 64.93%) models. With respect to the RMSE (root mean square error), the Llopis (mean of 10.69 mg/L, SD 12.23 mg/L) and Revilla models (mean of 10.65 mg/L, SD 12.81 mg/L) were comparable, and the lowest RMSE was found in the Goti model (mean 9.06 mg/L, SD 9 mg/L). Regarding the predictions, this behavior did not change, and the results varied relatively little. Although our results are satisfactory, the predictive performance in recent studies with vancomycin is heterogeneous, and although these three models have proven to be useful for clinical application, further research and adaptation of PopPK models is required, as well as implementation in the clinical practice of MIPD and TDM in real time.

PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data

Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring (TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently recommended to predict the antibiotic levels. These models, however, although using carefully designed lab observations, were often developed in limited patient populations. The increasing availability of electronic health record (EHR) data offers an opportunity to develop TDM models for real-world patient populations. Here, we present a deep learning-based pharmacokinetic prediction model for vancomycin (PK-RNN-V E) using a large EHR dataset of 5,483 patients with 55,336 vancomycin administrations. PK-RNN-V E takes the patient's real-time sparse and irregular observations and offers dynamic predictions. Our results show that RNN-PK-V E offers a root mean squared error (RMSE) of 5.39 and outperforms the traditional Bayesian model (VTDM model) with an RMSE of 6.29. We believe that PK-RNN-V E can provide a pharmacokinetic model for vancomycin and other antimicrobials that require TDM.

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.

Population Pharmacokinetics of High-Dose Methotrexate in Chinese Pediatric Patients With Acute Lymphoblastic Leukemia

High-dose methotrexate (HD-MTX) is widely used in pediatric acute lymphoblastic leukemia (ALL) treatment regimens. In this study, we aimed to develop a population pharmacokinetic (PK) model of HD-MTX in Chinese pediatric patients with ALL for designing personalized dosage regimens. In total, 4,517 MTX serum concentration data for 311 pediatric patients with ALL, aged 0.75–15.2 years and under HD-MTX treatment, were retrospectively collected at a tertiary Children’s Hospital in China. The non-linear mixed-effect model was used to establish the population PK model, using NONMEM software. The potential covariate effects of age, body weight, and biochemical measurements (renal and liver function) on MTX PK disposition were investigated. The model was then evaluated using goodness-of-fit, visual predictive check. MTX PK disposition was described using a three-compartment model reasonable well. Body weight, implemented as a fixed allometric function on all clearance and volume of distribution parameters, showed a substantial improvement in model fit. The final population model demonstrated that the MTX clearance estimate in a typical child with body weight of 19 kg was 6.9 L/h and the central distribution of volume estimate was 20.7 L. The serum creatinine significantly affected the MTX clearance, with a 0.97% decrease in clearance per 1 μmol/L of serum creatinine. Other covariates (e.g., age, sex, bilirubin, albumin, aspartate transaminase, concomitant medication) did not significantly affect PK properties of MTX. The proposed population PK model could describe the MTX concentration data in Chinese pediatric patients with ALL. This population PK model combined with a maximum a posteriori Bayesian approach could be used to estimate individual PK parameters, and optimize personalized MTX therapy in target patients, thus aiming to reduce toxicity and improve treatment outcomes.

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

Background

We 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.

Methods

The 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.

Results

A 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.

Conclusions

The 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.

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

WHAT IS KNOWN AND OBJECTIVES

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.

METHODS

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.

RESULTS

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.

WHAT IS NEW AND CONCLUSION

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

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.

Comparison of the Predictive Performance Between Cystatin C and Serum Creatinine by Vancomycin via a Population Pharmacokinetic Models: A Prospective Study in a Chinese Population

BACKGROUND

Most of the current published population pharmacokinetic (PopPK) models are based on serum creatinine, but we often encounter an underestimation of its concentration in our clinical work. Therefore, we established a cystatin C-based model of vancomycin.

OBJECTIVES

The purpose of this study was to externally verify the PopPK model of vancomycin based on the glomerular filtration rate (GFR) estimated by serum cystatin C in our previous study and to compare the prediction performance of cystatin C (Cys C) and serum creatinine (SCR)-based models.

METHODS

The external data set consists of adults receiving vancomycin treatment at The First Affiliated Hospital of Guangxi Medical University. We summarized and restored published models based on serum creatinine values from the literature and used our external data set for initial screening. Visual and external verifications were used to further select candidate models for comparison. The mean prediction error (ME), mean absolute error (MAE) and root mean squared error (RMSE) were the primary outcomes for the overall comparison. Group comparisons of patients with different glomerular filtration rates (GFRs), ages and body mass index (BMI) levels were obtained by the Bayesian method.

RESULTS

A total of 156 patients with 233 samples were collected as an external data set. Sixteen published models were summarized and restored. After screening, four candidate models suitable for the external data set were finally obtained for comparison. The cystatin C-based model has a smaller ME value in the overall comparison. In the group comparison, serum creatinine-based models were underestimated in the prediction for patient groups with age ≥ 60 years, abnormal BMI values and GFR < 90 ml/min/1.73 m², for which the cystatin C-based model could solve this problem.

CONCLUSION

After comparison, we suggest that cystatin C is a superior renal function marker to serum creatinine for vancomycin PopPK models.

Can Population Pharmacokinetics of Antibiotics be Extrapolated? Implications of External Evaluations

BACKGROUND AND OBJECTIVE

External evaluation is an important issue in the population pharmacokinetic analysis of antibiotics. The purpose of this review was to summarize the current approaches and status of external evaluations and discuss the implications of external evaluation results for the future individualization of dosing regimens.

METHODS

We systematically searched the PubMed and EMBASE databases for external evaluation studies of population analysis and extracted the relevant information from these articles. A total of 32 studies were included in this review.

RESULTS

Vancomycin was investigated in 17 (53.1%) articles and was the most studied drug. Other studied drugs included gentamicin, tobramycin, amikacin, amoxicillin, ceftaroline, meropenem, fluconazole, voriconazole, and rifampicin. Nine (28.1%) studies were prospective, and the sample size varied widely between studies. Thirteen (40.6%) studies evaluated the population pharmacokinetic models by systematically searching for previous studies. Seven (21.9%) studies were multicenter studies, and 27 (84.4%) adopted the sparse sampling strategy. Almost all external evaluation studies of antibiotics (93.8%) used metrics for prediction-based diagnostics, while relatively fewer studies were based on simulations (46.9%) and Bayesian forecasting (25.0%).

CONCLUSION

The results of external evaluations in previous studies revealed the poor extrapolation performance of existing models of prediction- and simulation-based diagnostics, whereas the posterior Bayesian method could improve predictive performance. There is an urgent need for the development of standards and guidelines for external evaluation studies.

Population Pharmacokinetic Modeling of Vancomycin in Thai Patients With Heterogeneous and Unstable Renal Function

BACKGROUND

Vancomycin is widely used to treat gram-positive bacterial infections. However, given significant interpatient variability in its pharmacokinetics, maintaining plasma concentrations is difficult within its characteristically narrow therapeutic window. This is especially challenging in patients with unstable renal function. Thus, the aim of this study was to develop a population pharmacokinetic model for vancomycin that is suitable for Thai patients with variable renal functions, including those with unstable renal function.

METHODS

Data from 213 patients, including 564 blood samples, were retrospectively collected; approximately 70% patients exhibited unstable renal function during vancomycin treatment. The model building group was randomly assigned 108 patients and the remaining 33 patients comprised the validation group. A population pharmacokinetic model was developed that incorporated drug clearance (CL) as a function of time-varying creatine clearance (CrCL). The predictive ability of the resulting population model was evaluated using the validation data set, including its ability to forecast serum concentrations within a Bayesian feedback algorithm.

RESULTS

A 2-compartment model with drug CL values that changed with time-varying CrCL adequately described vancomycin pharmacokinetics in the evaluated heterogeneous patient population with unstable renal function. Vancomycin CL was related to time-varying CrCL as follows: CL (t) = 0.11 + 0.021 × CrCL (t) (CrCL <120 mL/min. Using the population model, Bayesian estimation with at least one measured serum concentration resulted in a forecasting error of small bias (-2.4%) and adequate precision (31.5%).

CONCLUSIONS

In hospitals with a high incidence of unstable renal function, incorporating time-varying CrCL with Bayesian estimation and at least one measured drug concentration, along with frequent CrCL monitoring, improves the predictive performance of therapeutic drug monitoring of vancomycin.

Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients

Purpose

Bayesian forecasting is crucial for model-based dose optimization based on therapeutic drug monitoring (TDM) data of vancomycin in intensive care (ICU) patients. We aimed to evaluate the performance of Bayesian forecasting using maximum a posteriori (MAP) estimation for model-based TDM.

Methods

We used a vancomycin TDM data set (n = 408 patients). We compared standard MAP-based Bayesian forecasting with two alternative approaches: (i) adaptive MAP which handles data over multiple iterations, and (ii) weighted MAP which weights the likelihood contribution of data. We evaluated the percentage error (PE) for seven scenarios including historical TDM data from the preceding day up to seven days.

Results

The mean of median PEs of all scenarios for the standard MAP, adaptive MAP and weighted MAP method were − 7.7%, −4.5% and − 6.7%. The adaptive MAP also showed the narrowest inter-quartile range of PE. In addition, regardless of MAP method, including historical TDM data further in the past will increase prediction errors.

Conclusions

The proposed adaptive MAP method outperforms standard MAP in predictive performance and may be considered for improvement of model-based dose optimization. The inclusion of historical data beyond either one day (standard MAP and weighted MAP) or two days (adaptive MAP) reduces predictive performance.

Electronic supplementary material

The online version of this article (10.1007/s11095-020-02908-7) contains supplementary material, which is available to authorized users.

Assessing the accuracy of two Bayesian forecasting programs in estimating vancomycin drug exposure

Background

Current guidelines for intravenous vancomycin identify drug exposure (as indicated by the AUC) as the best pharmacokinetic (PK) indicator of therapeutic outcome.

Objectives

To assess the accuracy of two Bayesian forecasting programs in estimating vancomycin AUC0–∞ in adults with limited blood concentration sampling.

Methods

The application of seven vancomycin population PK models in two Bayesian forecasting programs was examined in non-obese adults (n = 22) with stable renal function. Patients were intensively sampled following a single (1000 mg or 15 mg/kg) dose. For each patient, AUC was calculated by fitting all vancomycin concentrations to a two-compartment model (defined as AUCTRUE). AUCTRUE was then compared with the Bayesian-estimated AUC0–∞ values using a single vancomycin concentration sampled at various times post-infusion.

Results

Optimal sampling times varied across different models. AUCTRUE was generally overestimated at earlier sampling times and underestimated at sampling times after 4 h post-infusion. The models by Goti et al. (Ther Drug Monit 2018;

40

212–21) and Thomson et al. (J Antimicrob Chemother 2009;

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1050–7) had precise and unbiased sampling times (defined as mean imprecision &lt;25% and &lt;38 mg·h/L, with 95% CI for mean bias containing zero) between 1.5 and 6 h and between 0.75 and 2 h post-infusion, respectively. Precise but biased sampling times for Thomson et al. were between 4 and 6 h post-infusion.

Conclusions

When using a single vancomycin concentration for Bayesian estimation of vancomycin drug exposure (AUC), the predictive performance was generally most accurate with sample collection between 1.5 and 6 h after infusion, though optimal sampling times varied across different population PK models.

Optimizing Vancomycin Dosing in Chronic Kidney Disease by Deriving and Implementing a Web-Based Tool Using a Population Pharmacokinetics Analysis

Background: Chronic kidney disease (CKD) patients requiring intravenous vancomycin bear considerable risks of adverse outcomes both from the infection and vancomycin therapy itself, necessitating especially precise dosing to avoid sub- and supratherapeutic vancomycin exposure.

Methods: In this retrospective study, we performed a population pharmacokinetic analysis to construct a vancomycin dose prediction model for CKD patients who do not require renal replacement therapy. The model was externally validated on an independent cohort of patients to assess its prediction accuracy. The pharmacokinetic parameter estimates and the equations were productized into a Web application (VancApp) subsequently implemented in routine care. The association between VancApp-based dosing and time-to-target concentration attainment, 30-day mortality, and nephrotoxicity were assessed postimplementation.

Results: The model constructed from an initial cohort (n = 80) revealed a population clearance and volume of distribution of 1.30 L/h and 1.23 L/kg, respectively. External model validation (n = 112) demonstrated a mean absolute prediction error of 1.25 mg/L. Following 4 months of clinical implementation of VancApp as an optional alternative to usual care [VancApp (n = 22) vs. usual care (n = 21)], patients who had received VancApp-based dosing took a shorter time to reach target concentrations (median: 66 vs. 102 h, p = 0.187) and had fewer 30-day mortalities (14% vs. 24%, p = 0.457) compared to usual care. While statistical significance was not achieved, the clinical significance of these findings appear promising.

Conclusion: Clinical implementation of a population pharmacokinetic model for vancomycin in CKD can potentially improve dosing precision in CKD and could serve as a practical means to improve vital clinical outcomes.

External Evaluation of Population Pharmacokinetic Models of Vancomycin in Large Cohorts of Intensive Care Unit Patients

Dosing of vancomycin is often guided by therapeutic drug monitoring and population pharmacokinetic models in the intensive care unit (ICU). The validity of these models is crucial, as ICU patients have marked pharmacokinetic variability. Therefore, we set out to evaluate the predictive performance of published population pharmacokinetic models of vancomycin in ICU patients. The PubMed database was used to search for population pharmacokinetic models of vancomycin in adult ICU patients. The identified models were evaluated in two independent data sets which were collected from two large hospitals in the Netherlands (Amsterdam UMC, Location VUmc, and OLVG Oost). We also tested a one-compartment model with fixed values for clearance and volume of distribution, in which a clinical standard dosage regimen (SDR) was mimicked to assess its predictive performance. Prediction error was calculated to assess the predictive performance of the models. Six models plus the SDR model were evaluated. The model of Roberts et al. (J. A. Roberts, F. S. Taccone, A. A. Udy, J.-L. Vincent, F. Jacobs, and J. Lipman, Antimicrob Agents Chemother 55:2704-2709, 2011, https://doi.org/10.1128/AAC.01708-10) performed satisfactorily, with mean and median values of prediction error of 5.1% and -7.5%, respectively, for Amsterdam UMC, Location VUmc, patients, and -12.6% and -17.2% respectively, for OLVG Oost patients. The other models, including the SDR model, yielded high mean values (-49.7% to 87.7%) and median values (-56.1% to 66.1%) for both populations. In conclusion, only the model of Roberts et al. was able to validly predict the concentrations of vancomycin for our data, whereas other models and standard dosing were largely inadequate. Extensive evaluation should precede the adoption of any model in clinical practice for ICU patients.

Evaluating tacrolimus pharmacokinetic models in adult renal transplant recipients with different CYP3A5 genotypes

PURPOSE

Numerous studies have been conducted on the population pharmacokinetics of tacrolimus in adult renal transplant recipients. It has been reported that the cytochrome P450 (CYP) 3A5 genotype is an important cause of variability in tacrolimus pharmacokinetics. However, the predictive performance of population pharmacokinetic (PK) models of tacrolimus should be evaluated prior to their implementation in clinical practice. The aim of the study reported here was to test the predictive performance of these published PK models of tacrolimus.

METHODS

A literature search of the PubMed and Web of Science databases ultimately led to the inclusion of eight one-compartment models in our analysis. We collected a total of 1715 trough concentrations from 174 patients. Predictive performance was assessed based on visual and numerical comparison bias and imprecision and by the use of simulation-based diagnostics and Bayesian forecasting.

RESULTS

Of the eight one-compartment models assessed, seven showed better predictive performance in CYP3A5 extensive metabolizers in terms of bias and imprecision. Results of the simulation-based diagnostics also supported the findings. The model based on a Chinese population in 2013 (model 3) showed the best and most stable predictive performance in all the tests and was more informative in CYP3A5 extensive metabolizers. As expected, Bayesian forecasting improved model predictability. Diversity among models and between different CYP3A5 genotypes of the same model was also narrowed by Bayesian forecasting.

CONCLUSIONS

Based on our results, we recommend using model 3 in CYP3A5 extensive metabolizers in clinical practice. All models had a poor predictive performance in CYP3A5 poor metabolizers, and they should be used with caution in this patient population. However, Bayesian forecasting improved the predictability and reduced differences, and thus the models could be applied in this latter patient population for the design of maintenance dose.

Population Pharmacokinetics of Vancomycin in Chinese ICU Neonates: Initial Dosage Recommendations

The main goal of our study was to characterize the population pharmacokinetics of vancomycin in critically ill Chinese neonates to develop a pharmacokinetic model and investigate factors that have significant influences on the pharmacokinetics of vancomycin in this population. The study population consisted of 80 neonates in the neonatal intensive care unit (ICU) from which 165 trough and peak concentrations of vancomycin were obtained. Nonlinear mixed effect modeling was used to develop a population pharmacokinetic model for vancomycin. The stability and predictive ability of the final model were evaluated based on diagnostic plots, normalized prediction distribution errors and the bootstrap method. Serum creatinine (Scr) and body weight were significant covariates on the clearance of vancomycin. The average clearance was 0.309 L/h for a neonate with Scr of 23.3 μmol/L and body weight of 2.9 kg. No obvious ethnic differences in the clearance of vancomycin were found relative to the earlier studies of Caucasian neonates. Moreover, the established model indicated that in patients with a greater renal clearance status, especially Scr < 15 μmol/L, current guideline recommendations would likely not achieve therapeutic area under the concentration-time curve over 24 h/minimum inhibitory concentration (AUC24h/MIC) ≥ 400. The exceptions to this are British National Formulary (2016-2017), Blue Book (2016) and Neofax (2017). Recommended dose regimens for neonates with different Scr levels and postmenstrual ages were estimated based on Monte Carlo simulations and the established model. These findings will be valuable for developing individualized dosage regimens in the neonatal ICU setting.

Population Pharmacokinetics and Dosing Considerations for Gentamicin in Newborns with Suspected or Proven Sepsis Caused by Gram-Negative Bacteria

The aim of this study was to describe the population pharmacokinetics (PK) of gentamicin in neonates with suspected or proven Gram-negative sepsis and determine the optimal dosage regimen in relation to the bacterial MICs found in this population. Data were prospectively collected between October 2012 and January 2013 in the Neonatal Intensive Care Unit (NICU) at the Academic Medical Center (AMC), Amsterdam, The Netherlands. A single nonlinear mixed-effects regression analysis (NONMEM) was performed to describe the population PK of gentamicin. Dosage regimens based upon gestational age (GA) were generated using Monte Carlo simulations with the final model. Target values were based on the MIC distribution in our patient population. In total, 136 gentamicin concentrations from 65 (pre)term neonates were included. The PK was best described by an allometric 2-compartment model with postmenstrual age (PMA) as a covariate on clearance (Cl). The MIC distribution (median, 0.75 [range, 0.5 to 1.5] mg/liter) justified a gentamicin target peak concentration of 8 to 12 mg/liter. This study describes the PK of gentamicin in (pre)term neonates. Dosage regimens of 5 mg/kg of body weight every 48 h, 5 mg/kg every 36 h, and 5 mg/kg every 24 h for patients with GAs of <37 weeks, 37 to 40 weeks, and ≥40 weeks, respectively, are recommended.