External Evaluation of Population Pharmacokinetic Models of Vancomycin in Neonates: The transferability of published models to different clinical settings

Optimization of Vancomycin Initial Dosing Regimen in Neonates Using an Externally Evaluated Population Pharmacokinetic Model

BACKGROUND

Vancomycin therapeutic monitoring guidelines were revised in March 2020, and a population pharmacokinetics-guided Bayesian approach to estimate the 24-hour area under the concentration-time curve to the minimum inhibitory concentration ratio has since been recommended instead of trough concentrations. To comply with these latest guidelines, we evaluated published population pharmacokinetic models of vancomycin using an external dataset of neonatal patients and selected the most predictive model to develop a new initial dosing regimen.

METHODS

The models were identified from the literature and tested using a retrospective dataset of Canadian neonates. Their predictive performance was assessed using prediction- and simulation-based diagnostics. Monte Carlo simulations were performed to develop the initial dosing regimen with the highest probability of therapeutic target attainment.

RESULTS

A total of 144 vancomycin concentrations were derived from 63 neonates in the external population. Five of the 28 models retained for evaluation were found predictive with a bias of 15% and an imprecision of 30%. Overall, the Grimsley and Thomson model performed best, with a bias of -0.8% and an imprecision of 20.9%; therefore, it was applied in the simulations. A novel initial dosing regimen of 15 mg/kg, followed by 11 mg/kg every 8 hours should favor therapeutic target attainment.

CONCLUSIONS

A predictive population pharmacokinetic model of vancomycin was identified after an external evaluation and used to recommend a novel initial dosing regimen. The implementation of these model-based tools may guide physicians in selecting the most appropriate initial vancomycin dose, leading to improved clinical outcomes.

External Evaluation of Population Pharmacokinetics Models of Lithium in the Bipolar Population

Lithium has been used in the treatment of bipolar disorder for several decades. Treatment optimization is recommended for this drug, due to its narrow therapeutic range and a large pharmacokinetics (PK) variability. In addition to therapeutic drug monitoring, attempts have been made to predict individual lithium doses using population pharmacokinetics (popPK) models. This study aims to assess the clinical applicability of published lithium popPK models by testing their predictive performance on two different external datasets. Available PopPK models were identified and their predictive performance was determined using a clinical dataset (46 patients/samples) and the literature dataset (89 patients/samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated, and the results of both external evaluations compared. Only one model met the acceptability criteria for both datasets. Overall, there was a lack of predictability of models; median PE and median absolute PE, respectively, ranged from -6.6% to 111.2% and from 24.4% to 111.2% for the literature dataset, and from -4.5% to 137.6% and from 24.9% to 137.6% for the clinical dataset. Most models underpredicted the observed concentrations (7 out of 10 models presented a negative bias). Renal status was included as a covariate of lithium's clearance in only two models. To conclude, most of lithium's PopPK models had limited predictive performances related to the absence of covariates of interest included, such as renal status. A solution to this problem could be to improve the models with methodologies such as metamodeling. This could be useful in the perspective of model-informed precision dosing.

External validation of population pharmacokinetic models of gentamicin in paediatric population from preterm newborns to adolescents

The aim of this study was to externally validate the predictive performance of published population pharmacokinetic models of gentamicin in all paediatric age groups, from preterm newborns to adolescents. We first selected published population pharmacokinetic models of gentamicin developed in the paediatric population with a wide age range. The parameters of the literature models were then re-estimated using the PRIOR subroutine in NONMEM^®. The predictive ability of the literature and the tweaked models was evaluated. Retrospectively collected data from a routine clinical practice (512 concentrations from 308 patients) were used for validation. The models with covariates characterising developmental changes in clearance and volume of distribution had better predictive performance, which improved further after re-estimation. The tweaked model by Wang 2019 performed best, with suitable accuracy and precision across the complete paediatric population. For patients treated in the intensive care unit, a lower proportion of patients would be expected to reach the target trough concentration at standard dosing. The selected model could be used for model-informed precision dosing in clinical settings where the entire paediatric population is treated. However, for use in clinical practice, the next step should include additional analysis of the impact of intensive care treatment on gentamicin pharmacokinetics, followed by prospective validation.

Analytical and Non-Analytical Variation May Lead to Inappropriate Antimicrobial Dosing in Neonates: An In Silico Study

BACKGROUND

Therapeutic drug monitoring (TDM) of aminoglycosides and vancomycin is used to prevent oto- and nephrotoxicity in neonates. Analytical and nonanalytical factors potentially influence dosing recommendations. This study aimed to determine the impact of analytical variation (imprecision and bias) and nonanalytical factors (accuracy of drug administration time, use of non-trough concentrations, biological variation, and dosing errors) on neonatal antimicrobial dosing recommendations.

METHODS

Published population pharmacokinetic models and the Australasian Neonatal Medicines Formulary were used to simulate antimicrobial concentration-time profiles in a virtual neonate population. Laboratory quality assurance data were used to quantify analytical variation in antimicrobial measurement methods used in clinical practice. Guideline-informed dosing recommendations based on drug concentrations were applied to compare the impact of analytical variation and nonanalytical factors on antimicrobial dosing.

RESULTS

Analytical variation caused differences in subsequent guideline-informed dosing recommendations in 9.3-12.1% (amikacin), 16.2-19.0% (tobramycin), 12.2-45.8% (gentamicin), and 9.6-19.5% (vancomycin) of neonates. For vancomycin, inaccuracies in drug administration time (45.6%), use of non-trough concentrations (44.7%), within-subject biological variation (38.2%), and dosing errors (27.5%) were predicted to result in more dosing discrepancies than analytical variation (12.5%). Using current analytical performance specifications, tolerated dosing discrepancies would be up to 14.8% (aminoglycosides) and 23.7% (vancomycin).

CONCLUSIONS

Although analytical variation can influence neonatal antimicrobial dosing recommendations, nonanalytical factors are more influential. These result in substantial variation in subsequent dosing of antimicrobials, risking inadvertent under- or overexposure. Harmonization of measurement methods and improved patient management systems may reduce the impact of analytical and nonanalytical factors on neonatal antimicrobial dosing.

External Evaluation of Population Pharmacokinetic Models for Precision Dosing: Current State and Knowledge Gaps

Predicting drug exposures using population pharmacokinetic models through Bayesian forecasting software can improve individual pharmacokinetic/pharmacodynamic target attainment. However, selecting the most adapted model to be used is challenging due to the lack of guidance on how to design and interpret external evaluation studies. The confusion around the choice of statistical metrics and acceptability criteria emphasises the need for further research to fill this methodological gap as there is an urgent need for the development of standards and guidelines for external evaluation studies. Herein we discuss the scientific challenges faced by pharmacometric researchers and opportunities for future research with a focus on antibiotics.

Towards Precision Dosing of Clozapine in Schizophrenia: External Evaluation of Population Pharmacokinetic Models and Bayesian Forecasting

BACKGROUND

Therapeutic drug monitoring and treatment optimization of clozapine are recommended, owing to its narrow therapeutic range and pharmacokinetic (PK) variability. This study aims to assess the clinical applicability of published population PK models by testing their predictive performance in an external data set and to determine the effectiveness of Bayesian forecasting (BF) for clozapine treatment optimization.

METHODS

Available models of clozapine were identified, and their predictive performance was determined using an external data set (53 patients, 151 samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated. The final concentration was reestimated for all patients using covariates or previously observed concentrations.

RESULTS

The 7 included models presented limited predictive performance. Only 1 model met the acceptability criteria (median PE ≤ ±20% and median absolute PE ≤30%). There was no difference between the data used for building the models (therapeutic drug monitoring or PK study) or the number of compartments in the models. A tendency for higher inaccuracy at low concentrations during treatment initiation was observed. Heterogeneities were observed in the predictive performances between the subpopulations, especially in terms of smoking status and sex. For the models included, BF significantly improved their predictive performance.

CONCLUSIONS

Our study showed that upon external evaluation, clozapine models provide limited predictive performance, especially in subpopulations such as nonsmokers. From the perspective of model-informed prediction dosing, model predictability should be improved using updating or metamodeling methods. Moreover, BF substantially improved model predictability and could be used for clozapine treatment optimization.

Role of fluid status markers as risk factors for suboptimal vancomycin concentration during continuous infusion in neonates: an observational study

Vancomycin is widely used in neonatal sepsis but proportion of newborn reaching recommended concentration is variable. Fluid status impact on vancomycin level remains understudied. We aimed to study fluid factors impacting vancomycin concentration at 24 h of treatment. We performed a prospective and retrospective observational monocentric study of NICU patients requiring a vancomycin treatment. We used a continuous infusion protocol, with age-appropriate loading and maintenance doses. Vancomycin target serum concentration after 24 h (C_24h) was above 20 mg/L. Demographic, infections, and organ failure variables were analyzed as potential predictors of C_24h. Over the study period, 70 infective episodes in 52 patients were included. At treatment initiation, the median post-natal age was 12.5 days (IQR 7-23), post menstrual age 30 weeks (IQR 28-35), and median weight 1140 g (IQR 835-1722). Germs isolated were mainly gram-positive with 73.5% being coagulase-negative Staphylococci. Median C_24h was 18.7 mg/L (IQR 15.4-22.4). Overall, 41 (58.6%) treatments had a C_24h < 20 mg/L. After multivariate analysis, higher creatinine level (OR 1.03 (95% CI 1.002-1.06)) was associated with C_24h ≥ 20 mg/L; weight gain the day before infection (OR 0.21 (95% CI 0.05-0.79)) and positive biomarkers of inflammation (OR 0.22 (0.05-0.94)) were associated with C_24h < 20 mg/L.

CONCLUSION

Vancomycin C_24h was underdosed in 60% of patients and factors linked to changes in vancomycin pharmacokinetic such as volume of distribution and clearance, linked to creatinine level, inflammation, or weight gain, were identified.

WHAT IS KNOWN

• Adjustment of vancomycin regimen remains difficult due to inter- and intra-individual variability of vancomycin pharmacokinetics. • Impact of fluid status on vancomycin concentration in critically ill neonates is incompletely studied.

WHAT IS NEW

• Proportion of patients with adequate vancomycin concentration using a target adapted to nosocomial gram-positive bacteria MIC is low. • We confirmed the role of creatinine level and report two new factors associated with low vancomycin concentration: presence of systemic inflammation and weight gain.

Implementation of Vancomycin Therapeutic Monitoring Guidelines: Focus on Bayesian Estimation Tools in Neonatal and Pediatric Patients

BACKGROUND

The 2020 consensus guidelines for vancomycin therapeutic monitoring recommend using Bayesian estimation targeting the ratio of the area under the curve over 24 hours to minimum inhibitory concentration as an optimal approach to individualize therapy in pediatric patients. To support institutional guideline implementation in children, the objective of this study was to comprehensively assess and compare published population-based pharmacokinetic (PK) vancomycin models and available Bayesian estimation tools, specific to neonatal and pediatric patients.

METHODS

PubMed and Embase databases were searched from January 1994 to December 2020 for studies in which a vancomycin population PK model was developed to determine clearance and volume of distribution in neonatal and pediatric populations. Available Bayesian software programs were identified and assessed from published articles, software program websites, and direct communication with the software company. In the present review, 14 neonatal and 20 pediatric models were included. Six programs (Adult and Pediatric Kinetics, BestDose, DoseMeRx, InsightRx, MwPharm++, and PrecisePK) were evaluated.

RESULTS

Among neonatal models, Frymoyer et al and Capparelli et al used the largest PK samples to generate their models, which were externally validated. Among the pediatric models, Le et al used the largest sample size, with multiple external validations. Of the Bayesian programs, DoseMeRx, InsightRx, and PrecisePK used clinically validated neonatal and pediatric models.

CONCLUSIONS

To optimize vancomycin use in neonatal and pediatric patients, clinicians should focus on selecting a model that best fits their patient population and use Bayesian estimation tools for therapeutic area under the -curve-targeted dosing and monitoring.

Development, validation, and application of an UPLC-MS/MS method for vancomycin, norvancomycin, methotrexate, paclitaxel, and imatinib analysis in human plasma

Background

Vancomycin, norvancomycin, methotrexate, paclitaxel, and imatinib are five commonly used drugs which are all recommended to therapeutic drug monitoring in clinical settings. However, the blood concentration monitoring of these drugs and the interpretations of the test results are limited to some extent due to the differences of testing instruments and testing methods.

Methods

We established an ultra-performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS) method for simultaneous quantification of vancomycin, norvancomycin, methotrexate, paclitaxel, and imatinib in human plasma. The method was validated according to the guideline for bioanalytical method validation and applied in clinical therapy.

Results

The calibration ranges of vancomycin, norvancomycin, methotrexate, paclitaxel, and imatinib were 0.5–100 μg/mL, 0.5–100 μg/mL, 5–1000 ng/mL, 10–2000 ng/mL, and 5–500 ng/mL, respectively. Inaccuracy and imprecision of every drug were less than 15%. The internal standard normalized recovery rates of vancomycin and norvancomycin were about 45%, while which of methotrexate, paclitaxel, and imatinib were almost 100%. No obvious carryover effect was observed. Samples were stable for at least 24 h in the automatic sampler, 72 h at 4°C, and 1 week in −80°C. There were no differences of concentrations between plasma and serum for the five drugs. Moreover, there were positive correlations between methotrexate and vancomycin concentrations and creatinine, as well as positive correlation between imatinib concentration and age of the patient.

Conclusions

The UPLC-MS/MS method was competent for the simultaneous monitoring of vancomycin, norvancomycin, methotrexate, paclitaxel, and imatinib because of its short analysis time, high specificity, and accuracy.

Dosing of vancomycin and target attainment in neonates: a systematic review

INTRODUCTION

Neonatal infections caused by Gram-positive bacteria are commonly treated with vancomycin. However, there is a lack of agreement on the optimal vancomycin dosing regimen and corresponding vancomycin exposure to correlate with efficacy and toxicity.

OBJECTIVES

This review aimed to evaluate dosing of vancomycin in neonates, therapeutic target attainment and clinical toxicity and efficacy outcomes.

METHODS

Two electronic databases - Embase and PubMed (Medline) - were systematically searched between 1995-2020. Studies that reported dosing regimens, drug concentrations, toxicity, and efficacy of vancomycin in neonates were eligible for inclusion. Descriptive analysis and a narrative synthesis were performed.

RESULTS

The systematic review protocol was registered with the PROSPERO International Prospective Register of Systematic reviews in 2020 (registration number: CRD42020219568). Twenty-four studies were included for final analysis. Overall, the data from the included studies showed a great degree of heterogeneity. Therapeutic drug monitoring practices were different between institutions. Although most studies used trough concentration with a target range of 10-20 mg/L, target attainment was different across the studies. The probability of target attainment was < 80% in all tested dosing algorithms. Few studies reported on vancomycin efficacy and toxicity.

CONCLUSION

This is a comprehensive overview of dosing strategies of vancomycin in neonates. There was inadequate evidence to propose an optimal therapeutic regimen in the newborn population, based on the data obtained, due to the heterogeneity in the design and objectives of the included studies. Consistent and homogeneous comparative randomised clinical trials are needed to identify a dosing regimen with a probability of target attainment of > 90% without toxicity.

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

PURPOSE

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.

METHODS

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.

RESULTS

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 (K_a), apparent clearance (CL/F), and apparent distribution volume (V/F) for a female patient weighing 70 kg administered VPA 1000 mg/day were 0.18 h^-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.

CONCLUSIONS

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.

Degradation of vancomycin in external quality assessment samples is a factor to underestimate its concentration

Aim: To compare the difference between the measured and target values in vancomycin external quality assessment (EQA) samples and to investigate the factors for underestimating its concentration. Materials & methods: A retrospective analysis of 195 international vancomycin EQA results was performed. Deviations of the concentrations determined by TDx fluorescence polarization immunoassay (FPIA), Axsym FPIA and Architect chemiluminescence microparticle immunoassay (CMIA) method were -2.43, -16.28 and -10.53%, respectively. Chromatographic peaks of the crystalling degradation products appeared in samples with large deviations. Vancomycin were degraded after long-term transporting and high temperature. Conclusion: Vancomycin concentrations measured by Axsym FPIA and Architect CMIA methods were likely to be underestimated. Long-term transporting resulted in low EQA results, suggesting that establishing a local EQA system for vancomycin is essential.

A continued learning approach for model-informed precision dosing: updating models in clinical practice

Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to include also altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, since only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step towards building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.

Does Prolonged Infusion Time Really Improve the Efficacy of Meropenem Therapy? A Prospective Study in Critically Ill Patients

Introduction

Meropenem is a carbapenem antibiotic, which has demonstrated excellent antimicrobial activity against gram-negative clinical isolates. It is also commonly used in critically ill patients. This study aimed to determine the pharmacokinetics/pharmacodynamics of meropenem in critically ill patients and whether prolonged injection duration is really beneficial to meropenem therapy.

Methods

We included 209 samples in 64 patients in this prospective study. PPK analysis and Monte Carlo dosing simulations were developed using Phoenix.

Results

A two-compartment model described the data adequately. Clearance (CL), volume (V), clearance of peripheral compartment (CL₂), and volume of peripheral compartment (V₂) were 6.15 l/h, 2.83 l/h, 17.40 l, and 17.48 l, respectively. Creatinine clearance and uric acid were significant covariates. Patients with creatinine clearance ≤ 60 ml/min and uric acid > 400 μmol/l could achieve the target > 90% under the minimum inhibitory concentration (MIC) of 8 mg/l, even with the administration dose of 500 mg/8 h with a 2-h infusion. Prolonging the infusion time significantly improved the therapeutic effect when MIC < 4. However, for the pharmacodynamic (PD) effects of 100% fT > MIC and 100% fT > 4 MIC, no significant statistical difference was observed in critically ill patients.

Conclusions

Critically ill patients with lower creatinine clearance and higher uric acid levels tended to need a lower dosage of meropenem. Prolonged infusion time was not always beneficial for those who needed a higher therapeutic target (100% fT > MIC, 100% fT > 4 MIC) or with MIC > 4 mg/l. Increasing dose or alternative therapeutic strategies may be required for critically ill patients with drug-resistant or severe infections. The study is of great significance to guide the rational use of meropenem in critically ill patients.

Trial Registration

The trial was registered in the China Clinical Trial (ChiCTR1900020672). Registered on 12 January 2019.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40121-021-00551-2.

Meropenem is commonly used empirically or targeted in critically ill patients for bacterial infection. Many studies have reported that prolonged infusion time can improve the efficacy of meropenem therapy. However, we are skeptical about that. Meanwhile, prolonged injections can sometimes cause mobility problems for patients. A quantitative method is used to evaluate meropenem use. It is called the population pharmacokinetic model or pharmacodynamic study. Using this method, we found two significant influencing factors of meropenem metabolism: creatinine clearance and uric acid level. It is likely that patients with a lower level of creatinine clearance and a high uric acid level tend to require lower dosages of meropenem. As for the effect of infusion time, Monte Carlo simulation was used, which can do 3000 simulations on an individual. The result was complex. We found infusion time was beneficial only when bacteria were sensitive to meropenem. The evidence suggests that prolonged injection duration sometimes does not significantly improve the outcome of antimicrobial therapy.

Predicting the Outcome of Voriconazole Individualized Medication Using Integrated Pharmacokinetic/Pharmacodynamic Model

Therapeutic drug monitoring is considered to be an effective tool for the individualized use of voriconazole. However, drug concentration measurement alone doesn’t take into account the susceptibility of the infecting microorganisms to the drug. Linking pharmacodynamic data with the pharmacokinetic profile of individuals is expected to be an effective method to predict the probability of a certain therapeutic outcome. The objective of this study was to individualize voriconazole regimens by integrating individual pharmacokinetic parameters and pathogen susceptibility data through Monte Carlo simulations The individual pharmacokinetic parameters of 35 hospitalized patients who received voriconazole were calculated based on a validated population pharmacokinetic model. The area under the concentration-time curve for free drug/minimal inhibitory concentration (fAUC_ss/MIC) > 25 was selected as the pharmacokinetic/pharmacodynamic (PK/PD) parameter predicting the efficacy of voriconazole. The cumulative fraction of response (CFR) of the target value was assessed. To verify this conclusion, a logistic regression analysis was used to explore the relationship between actual clinical efficiency and the CFR value. For the 35 patients, the area under the free drug concentration-time curve (fAUC_ss) was calculated to be 34.90 ± 21.67 mgh/L. According to the dualistic logistic regression analysis, the minimal inhibitory concentration (MIC) value of different kinds of fungi had a great influence on the effectiveness of clinical treatment. It also showed that the actual clinical efficacy and the CFR value of fAUC_ss/MIC had a high degree of consistency. The results suggest that it is feasible to individualize voriconazole dosing and predict clinical outcomes through the integration of data on pharmacokinetics and antifungal susceptibility.

Pharmacokinetics of Antibiotics in Pediatric Intensive Care: Fostering Variability to Attain Precision Medicine

Children show important developmental and maturational changes, which may contribute greatly to pharmacokinetic (PK) variability observed in pediatric patients. These PK alterations are further enhanced by disease-related, non-maturational factors. Specific to the intensive care setting, such factors include critical illness, inflammatory status, augmented renal clearance (ARC), as well as therapeutic interventions (e.g., extracorporeal organ support systems or whole-body hypothermia [WBH]). This narrative review illustrates the relevance of both maturational and non-maturational changes in absorption, distribution, metabolism, and excretion (ADME) applied to antibiotics. It hereby provides a focused assessment of the available literature on the impact of critical illness—in general, and in specific subpopulations (ARC, extracorporeal organ support systems, WBH)—on PK and potential underexposure in children and neonates. Overall, literature discussing antibiotic PK alterations in pediatric intensive care is scarce. Most studies describe antibiotics commonly monitored in clinical practice such as vancomycin and aminoglycosides. Because of the large PK variability, therapeutic drug monitoring, further extended to other antibiotics, and integration of model-informed precision dosing in clinical practice are suggested to optimise antibiotic dose and exposure in each newborn, infant, or child during intensive care.

External Model Performance Evaluation of Twelve Infliximab Population Pharmacokinetic Models in Patients with Inflammatory Bowel Disease

Infliximab is approved for treatment of various chronic inflammatory diseases including inflammatory bowel disease (IBD). However, high variability in infliximab trough levels has been associated with diverse response rates. Model-informed precision dosing (MIPD) with population pharmacokinetic models could help to individualize infliximab dosing regimens and improve therapy. The aim of this study was to evaluate the predictive performance of published infliximab population pharmacokinetic models for IBD patients with an external data set. The data set consisted of 105 IBD patients with 336 infliximab concentrations. Literature review identified 12 published models eligible for external evaluation. Model performance was evaluated with goodness-of-fit plots, prediction- and variability-corrected visual predictive checks (pvcVPCs) and quantitative measures. For anti-drug antibody (ADA)-negative patients, model accuracy decreased for predictions > 6 months, while bias did not increase. In general, predictions for patients developing ADA were less accurate for all models investigated. Two models with the highest classification accuracy identified necessary dose escalations (for trough concentrations < 5 µg/mL) in 88% of cases. In summary, population pharmacokinetic modeling can be used to individualize infliximab dosing and thereby help to prevent infliximab trough concentrations dropping below the target trough concentration. However, predictions of infliximab concentrations for patients developing ADA remain challenging.

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.

Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction

BACKGROUND

Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data.

OBJECTIVE

The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.

METHODS

Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.

RESULTS

The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.

CONCLUSION

A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.

Population Pharmacokinetic Models of Vancomycin in Paediatric Patients: A Systematic Review

BACKGROUND

Vancomycin is commonly used to treat gram-positive bacterial infections in the paediatric population, but dosing can be challenging. Population pharmacokinetic (popPK) modelling can improve individualization of dosing regimens. The primary objective of this study was to describe popPK models of vancomycin and factors that influence pharmacokinetic (PK) variability in paediatric patients.

METHODS

Systematic searches were conducted in the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, International Pharmaceutical Abstracts and the grey literature without language or publication status restrictions from inception to 17 August 2020. Observational studies that described the development of popPK models of vancomycin in paediatric patients (< 18 years of age) were included. Risk of bias was assessed using the National Heart, Lung and Blood Institute Study Quality Assessment Tool for Case Series Studies.

RESULTS

Sixty-four observational studies (1 randomized controlled trial, 13 prospective studies and 50 retrospective studies of 9019 patients with at least 25,769 serum vancomycin concentrations) were included. The mean age was 2.5 years (range 1 day-18 years), serum creatinine was 47.1 ± 33.6 µmol/L, and estimated creatinine clearance was 97.4 ± 76 mL/min/1.73m². Most studies found that vancomycin PK was best described by a one-compartment model (71.9%). There was a wide range of clearance and volume of distribution (V_d) values (range 0.014-0.27 L/kg/h and 0.43-1.46 L/kg, respectively) with interindividual variability as high as 49.7% for clearance and 136% for V_d, proportional residual variability up to 37.5% and additive residual variability up to 17.5 mg/L. The most significant covariates for clearance were weight, age, and serum creatinine or creatinine clearance, and weight for V_d. Variable dosing recommendations were suggested.

CONCLUSION

Numerous popPK models of vancomycin were derived, however external validation of suggested dosing regimens and analyses in subgroup paediatric populations such as dialysis patients are still needed before a popPK model with best predictive performance can be applied for dosing recommendations. Significant intraindividual and interindividual PK variability was present, which demonstrated the need for ongoing therapeutic drug monitoring and derivation of PK models for vancomycin for certain subgroup populations, such as dialysis patients.

Population pharmacokinetics-pharmacodynamics of ceftazidime in neonates and young infants: Dosing optimization for neonatal sepsis

Ceftazidime is a third-generation cephalosporin with high activity against many pathogens. But the ambiguity and diversity of the dosing regimens in neonates and young infants impair access to effective treatment. Thus, we conducted a population pharmacokinetic study of ceftazidime in this vulnerable population and recommended a model-based dosage regimen to optimize sepsis therapy. Totally 146 neonates and young infants (gestational age (GA): 36-43.4 weeks, postnatal age (PNA): 1-81 days, current weight (CW): 900-4500 g) were enrolled based on inclusion and exclusion criteria. Ceftazidime bloods samples (203) were obtained using the opportunistic sampling strategy and determined by the high-performance liquid chromatography. The population pharmacokinetic-pharmacodynamic analysis was conducted by nonlinear mixed effects model (NONMEM). A one-compartment model with first-order elimination best described the pharmacokinetic data. Covariate analysis showed the significance of GA, PNA, and CW on developmental pharmacokinetics. Monte Carlo simulation was performed based on above covariates and minimum inhibitory concentration (MIC). In the newborns with PNA ≤ 3 days (MIC=8 mg/L), the dose regimen was 25 mg/kg twice daily (BID). For the newborns with PNA > 3 days (MIC=16 mg/L), the optimal dose was 30 mg/kg three times daily (TID) for those with GA ≤ 37 weeks and 40 mg/kg TID for those with GA > 37 weeks. Overall, on the basis of the developmental population pharmacokinetic-pharmacodynamic analysis covering the whole range of neonates and young infants, the evidence-based ceftazidime dosage regimens were proposed to optimize neonatal early-onset and late-onset sepsis therapy.

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 Vancomycin Population Pharmacokinetic Models at Two Clinical Centers

Background: Numerous vancomycin population pharmacokinetic models in neonates have been published; however, their predictive performances remain unknown. This study aims to evaluate their external predictability and explore the factors that might affect model performance. Methods: Published population pharmacokinetic models in neonates were identified from the literature and evaluated using datasets from two clinical centers, including 171 neonates with a total of 319 measurements of vancomycin levels. Predictive performance was assessed by prediction- and simulation-based diagnostics and Bayesian forecasting. Furthermore, the effect of model structure and a number of identified covariates was also investigated. Results: Eighteen published pharmacokinetic models of vancomycin were identified after a systematic literature search. Using prediction-based diagnostics, no model had a median prediction error of ≤ ± 15%, a median absolute prediction error of ≤30%, and a percentage of prediction error that fell within ±30% of >50%. A simulation-based visual predictive check of most models showed there were large deviations between observations and simulations. After Bayesian forecasting with one or two prior observations, the predicted performance improved significantly. Weight, age, and serum creatinine were identified as the most important covariates. Moreover, employing a maturation model based on weight and age as well as nonlinear model to incorporate serum creatinine level significantly improved predictive performance. Conclusion: The predictability of the pharmacokinetic models for vancomycin is closely related to the approach used for modeling covariates. Bayesian forecasting can significantly improve the predictive performance of models.

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.

Model-Informed Precision Dosing of Antibiotics in Pediatric Patients: A Narrative Review

Optimal pharmacotherapy in pediatric patients with suspected infections requires understanding and integration of relevant data on the antibiotic, bacterial pathogen, and patient characteristics. Because of age-related physiological maturation and non-maturational covariates (e.g., disease state, inflammation, organ failure, co-morbidity, co-medication and extracorporeal systems), antibiotic pharmacokinetics is highly variable in pediatric patients and difficult to predict without using population pharmacokinetics models. The intra- and inter-individual variability can result in under- or overexposure in a significant proportion of patients. Therapeutic drug monitoring typically covers assessment of pharmacokinetics and pharmacodynamics, and concurrent dose adaptation after initial standard dosing and drug concentration analysis. Model-informed precision dosing (MIPD) captures drug, disease, and patient characteristics in modeling approaches and can be used to perform Bayesian forecasting and dose optimization. Incorporating MIPD in the electronic patient record system brings pharmacometrics to the bedside of the patient, with the aim of a consisted and optimal drug exposure. In this narrative review, we evaluated studies assessing optimization of antibiotic pharmacotherapy using MIPD in pediatric populations. Four eligible studies involving amikacin and vancomycin were identified from 418 records. Key articles, independent of year of publication, were also selected to highlight important attributes of MIPD. Although very little research has been conducted until this moment, the available data on vancomycin indicate that MIPD is superior compared to conventional dosing strategies with respect to target attainment. The utility of MIPD in pediatrics needs to be further confirmed in frequently used antibiotic classes, particularly aminoglycosides and beta-lactams.

Dosing Recommendations for Lamotrigine in Children: Evaluation Based on Previous and New Population Pharmacokinetic Models

Lamotrigine is a broad-spectrum antiepileptic drug with high interindividual variability in serum concentrations in children. The aims of this study were to evaluate the predictive performance of pediatric population pharmacokinetic (PPK) models published on lamotrigine, to build a new model with our monitoring data and to evaluate the current recommended doses. A validation cohort included patients treated with lamotrigine who had a serum level assayed during therapeutic drug monitoring (TDM). PPK models published in the literature were first applied to the validation cohort. We assessed their predictive performance using mean prediction errors, root mean squared errors, and visual predictive checks. A new model was then built using the data. Dose simulations were performed to evaluate the doses recommended. We included 270 lamotrigine concentrations ranging from 0.5 to 17.9 mg/L from 175 patients. The median (range) age and weight were 11.8 years (0.8-18 years) and 32.7 kg (8-110 kg). We tested 6 PPK models; most had acceptable bias and precision but underestimated the variability of the cohort. We built a 1-compartment model with first-order absorption and elimination, allometric scaling, and effects of inhibitor and inducer comedications. In our cohort, 22.6% of trough concentrations were below 2.5 mg/L. In conclusion, we proposed a PPK model that can be used for TDM of lamotrigine in children. In our population, a high percentage of children had low trough concentrations of lamotrigine. As the intervals of recommended doses are large, we suggest aiming at the higher range of doses to reach the target concentration.

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 meta-analysis of individual data to design the first randomized efficacy trial of vancomycin in neonates and young infants

OBJECTIVES

In the absence of consensus, the present meta-analysis was performed to determine an optimal dosing regimen of vancomycin for neonates.

METHODS

A 'meta-model' with 4894 concentrations from 1631 neonates was built using NONMEM, and Monte Carlo simulations were performed to design an optimal intermittent infusion, aiming to reach a target AUC0-24 of 400 mg·h/L at steady-state in at least 80% of neonates.

RESULTS

A two-compartment model best fitted the data. Current weight, postmenstrual age (PMA) and serum creatinine were the significant covariates for CL. After model validation, simulations showed that a loading dose (25 mg/kg) and a maintenance dose (15 mg/kg q12h if <35 weeks PMA and 15 mg/kg q8h if ≥35 weeks PMA) achieved the AUC0-24 target earlier than a standard 'Blue Book' dosage regimen in >89% of the treated patients.

CONCLUSIONS

The results of a population meta-analysis of vancomycin data have been used to develop a new dosing regimen for neonatal use and to assist in the design of the model-based, multinational European trial, NeoVanc.

Personalized Tuberculosis Treatment Through Model-Informed Dosing of Rifampicin

BACKGROUND AND OBJECTIVE

This study proposes a model-informed approach for therapeutic drug monitoring (TDM) of rifampicin to improve tuberculosis (TB) treatment.

METHODS

Two datasets from pulmonary TB patients were used: a pharmacokinetic study (34 patients, 373 samples), and TDM data (96 patients, 391 samples) collected at Radboud University Medical Center, The Netherlands. Nine suitable population pharmacokinetic models of rifampicin were identified in the literature and evaluated on the datasets. A model developed by Svensson et al. was found to be the most suitable based on graphical goodness of fit, residual diagnostics, and predictive performance. Prediction of individual area under the concentration-time curve from time zero to 24 h (AUC₂₄) and maximum concentration (C_max) employing various sampling strategies was compared with a previously established linear regression TDM strategy, using sampling at 2, 4, and 6 h, in terms of bias and precision (mean error [ME] and root mean square error [RMSE]).

RESULTS

A sampling strategy using 2- and 4-h blood collection was selected to be the most suitable. The bias and precision of the two strategies were comparable, except that the linear regression strategy was more biased in prediction of the AUC₂₄ than the model-informed approach (ME of 9.9% and 1.5%, respectively). A comparison of resulting dose advice, using predictions on a simulated dataset, showed no significant difference in sensitivity or specificity between the two methods. The model was successfully implemented in the InsightRX precision dosing platform.

CONCLUSION

Blood sampling at 2 and 4 h, combined with model-based prediction, can be used instead of the currently used linear regression strategy, shortening the sampling by 2 h and one sampling point without performance loss while simultaneously offering flexibility in sampling times.

Population Pharmacokinetics of Vancomycin in the Pediatric Ventricular Assist Device Population

OBJECTIVES

Determine the pharmacokinetic disposition of vancomycin in the pediatric ventricular assist device population.

DESIGN

A retrospective, population pharmacokinetic study.

SETTING

Large, quaternary care children's hospital.

PATIENTS

Less than 19 years old initiated on vancomycin while undergoing ventricular assist device therapy from 2011 to 2018 in our institution.

INTERVENTIONS

Patient data were summarized by using descriptive statistical methods, and population pharmacokinetic analysis was performed by using NONMEM (Icon, PLC, Dublin, Ireland). Simulation was performed to identify a vancomycin dosing strategy that resulted in a trough concentration less than 15 mg/L and an area under the curve0-24:minimum inhibitory concentration ratio of greater than 400.

MEASUREMENTS AND MAIN RESULTS

A total of 69 patients (male 50.7%, median age 7.1 years [interquartile range, 2.4-11.9]) met study criteria (HeartWare [Framingham, MA] = 37, Berlin Heart [Berlin, Germany] = 22, Impella [Abiomed, Danvers, MA] = 4, RotaFlow [Maquet, Hirrlingen, Germany] right ventricular assist device = 3, HeartMate II [Abbott Laboratories, Abbott Park, IL] = 2, Berlin Heart biventricular assist device = 1). Patients received a median of 21 doses (interquartile range, 13-44 doses) of IV vancomycin (14.8 ± 1.8 mg/kg/dose) along with vancomycin as an intrathoracic irrigation (n = 48; 69.6%). The mean serum concentration was 12.2 ± 5.2 mg/L at 11.2 ± 6.9 hours after a dose. A one-compartment pharmacokinetic model best fit the data with allometric scaling on clearance and volume of distribution. Clearance was characterized by total body weight and serum creatinine, and volume of distribution was characterized by total body weight. Simulation identified doses greater than 15 mg/kg/dose with extended intervals were necessary to achieve endpoints.

CONCLUSIONS

Vancomycin dosing in pediatric ventricular assist device patients should be altered in comparison to nonventricular assist device patients and should be accompanied with frequent serum concentration monitoring.

Precision and accuracy of commercial assays for vancomycin therapeutic drug monitoring: evaluation based on external quality assessment scheme

BACKGROUND

Vancomycin remains a mainstay of the treatment of Gram-positive bacterial infections. It is crucial to accurately determine vancomycin serum concentration for adequate dose adjustment.

OBJECTIVES

To evaluate the precision and accuracy of commercial assay techniques for vancomycin concentration and to assess the comparability of vancomycin detection methods in Chinese laboratories.

METHODS

Human serum samples spiked with known concentrations of vancomycin were provided to laboratories participating in the external quality assessment scheme (EQAS). Assay methods included chemiluminescence, enzyme immunoassay (EIA) and so on. The dispersion of the measurements was analysed and the robust coefficient of variation (rCV), relative percentage difference (RPD) and satisfactory rate for method groups were calculated. Moreover, performance of the Chinese laboratories was assessed.

RESULTS

A total of 657 results from 75 laboratories were collected, including 84 samples from 10 Chinese laboratories. The median rCV, median RPD and satisfactory rates classified by methods ranged from 1.85% to 15.87%, -14.75% to 13.34% and 94.59% to 100.00%, respectively. Significant differences were seen in precision, between kinetic interaction of microparticles in solution (KIMS) and other methods, and in accuracy, between enzyme-multiplied immunoassay technique (EMIT), fluorescence polarization immunoassay (FPIA) and other techniques. Vancomycin detection in China mainly depended on the chemiluminescence and EMIT methods, which tended to result in lower measurements.

CONCLUSIONS

Although almost all assays in this study achieved an acceptable performance for vancomycin serum concentration monitoring, obvious inconsistencies between methods were still observed. Chinese laboratories were more likely to underestimate vancomycin concentrations. Thus, recognizing inconsistencies between methods and regular participation in vancomycin EQAS are essential.

Development and external evaluation of a population pharmacokinetic model for continuous and intermittent administration of vancomycin in neonates and infants using prospectively collected data

BACKGROUND

Vancomycin is commonly used for nosocomial bacterial pathogens causing late-onset septicaemia in preterm infants. We prospectively collected pharmacokinetic data aiming to describe pharmacokinetics and determine covariates contributing to the variability in neonatal vancomycin pharmacokinetics. Further, we aimed to use the model to compare the ratio of AUC24 at steady-state to the MIC (AUC24,ss/MIC) of several intermittent and continuous dosing regimens.

METHODS

Newborns receiving vancomycin for suspected or confirmed late-onset sepsis were included. Peak and trough concentrations for intermittent vancomycin dosing and steady-state concentrations for continuous vancomycin dosing were measured. NONMEM 7.3 was used for population pharmacokinetic analysis. Monte Carlo simulations were performed to compare dosing schemes.

RESULTS

Data from 54 infants were used for model development and from 34 infants for the model evaluation {corrected gestational age [median (range)] = 29 (23.7-41.9) weeks and 28 (23.4-41.7) weeks, respectively}. The final model was a one-compartment model. Weight and postmenstrual age were included a priori, and then no additional covariate significantly improved the model fit. Final model parameter estimates [mean (SEM)]: CL = 5.7 (0.3) L/h/70 kg and V = 39.3 (3.7) L/70 kg. Visual predictive check of the evaluation dataset confirmed the model can predict external data. Simulations using MIC of 1 mg/L showed that for neonates with gestational age ≤25 weeks and postnatal age ≤2 weeks AUC24,ss/MIC was lower with the intermittent regimen (median 482 versus 663).

CONCLUSIONS

A population pharmacokinetic model for continuous and intermittent vancomycin administration in infants was developed. Continuous administration might be favourable for treating infections caused by resistant microorganisms in very young and immature infants.

Prospective validation of a model-informed precision dosing tool for vancomycin in intensive care patients

AIMS

Vancomycin is an important antibiotic for critically ill patients with Gram-positive bacterial infections. Critically ill patients typically have severely altered pathophysiology, which leads to inefficacy or toxicity. Model-informed precision dosing may aid in optimizing the dose, but prospectively validated tools are not available for this drug in these patients. We aimed to prospectively validate a population pharmacokinetic model for purpose model-informed precision dosing of vancomycin in critically ill patients.

METHODS

We first performed a systematic evaluation of various models on retrospectively collected pharmacokinetic data in critically ill patients and then selected the best performing model. This model was implemented in the Insight Rx clinical decision support tool and prospectively validated in a multicentre study in critically ill patients. The predictive performance was obtained as mean prediction error and relative root mean squared error.

RESULTS

We identified 5 suitable population pharmacokinetic models. The most suitable model was carried forward to a prospective validation. We found in a prospective multicentre study that the selected model could accurately and precisely predict the vancomycin pharmacokinetics based on a previous measurement, with a mean prediction error and relative root mean squared error of respectively 8.84% (95% confidence interval 5.72-11.96%) and 19.8% (95% confidence interval 17.47-22.13%).

CONCLUSION

Using a systematic approach, with a retrospective evaluation and prospective verification we showed the suitability of a model to predict vancomycin pharmacokinetics for purposes of model-informed precision dosing in clinical practice. The presented methodology may serve a generic approach for evaluation of pharmacometric models for the use of model-informed precision dosing in the clinic.

External evaluation of population pharmacokinetic models for continuous administration of meropenem in critically ill adult patients

PURPOSE

Beta-lactams (BL), the most commonly prescribed class of antibiotics, are recommended as the first-line therapy for multiple indications in infectious disease guidelines. Meropenem (MERO) is frequently used in intensive care units (ICU) to treat bacterial infections with or without sepsis. The pharmacokinetics of MERO display a large variability in patients admitted to ICUs due to altered pathophysiology. The aim of this study was to perform an external evaluation of published population pharmacokinetic models of MERO in order to test their predictive performance in a cohort of ICU adult patients.

METHODS

A literature search in PubMed/Medline database was made following the PRISMA statement. External evaluation was performed using NONMEM software, and the bias and inaccuracy values were calculated.

RESULTS

An external validation dataset from the Timone Hospital in Marseille, France, included 84 concentration samples from 27 patients. Four models of MERO were identified according to the inclusion criteria of the study. None of the models presented acceptable values of bias and inaccuracy.

CONCLUSION

While performing external evaluations on some populations may confirm a model's suitability to diverse groups of patients, there is still some variability that cannot be explained nor solved by the procedure. This brings to light the difficulty to develop only one model for ICU patients and the need to develop one specific model to each population of critically ill patients.

Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy

Currently employed methods for qualifying population physiologically-based pharmacokinetic (Pop-PBPK) model predictions of continuous outcomes (e.g., concentration-time data) fail to account for within-subject correlations and the presence of residual error. In this study, we propose a new method for evaluating Pop-PBPK model predictions that account for such features. The approach focuses on deriving Pop-PBPK-specific normalized prediction distribution errors (NPDE), a metric that is commonly used for population pharmacokinetic model validation. We describe specific methodological steps for computing NPDE for Pop-PBPK models and define three measures for evaluating model performance: mean of NPDE, goodness-of-fit plots, and the magnitude of residual error. Utility of the proposed evaluation approach was demonstrated using two simulation-based study designs (positive and negative control studies) as well as pharmacokinetic data from a real-world clinical trial. For the positive-control simulation study, where observations and model simulations were generated under the same Pop-PBPK model, the NPDE-based approach denoted a congruency between model predictions and observed data (mean of NPDE =  - 0.01). In contrast, for the negative-control simulation study, where model simulations and observed data were generated under different Pop-PBPK models, the NPDE-based method asserted that model simulations and observed data were incongruent (mean of NPDE =  - 0.29). When employed to evaluate a previously developed clindamycin PBPK model against prospectively collected plasma concentration data from 29 children, the NPDE-based method qualified the model predictions as successful (mean of NPDE = 0). However, when pediatric subpopulations (e.g., infants) were evaluated, the approach revealed potential biases that should be explored.

Amikacin nomogram for treatment of adult cystic fibrosis exacerbations based on an external evaluation of a population pharmacokinetic model

BACKGROUND

In patients with cystic fibrosis (CF), amikacin is the alternative for the treatment of acute pulmonary exacerbations associated with pathogens resistant to tobramycin. Population pharmacokinetic (PK) models of amikacin in adult patients with CF have been previously published. However, current dosing recommendations remain disputed (Illamola et al. Clin Pharmacokinet. 2018;57(10):1217-1228). We perform here the first external evaluation of a published amikacin adult CF population PK model and propose a dosing nomogram for initial dosing.

METHODS

We retrospectively collected demographic, biological, and clinical data from the medical records of adult patients who had received intravenous amikacin. To assess the predictive performance of this model we applied visual comparison of predictions to observations, calculation of bias and inaccuracy, and simulation-based diagnostics. Monte Carlo simulations from the evaluated model were used to compare maximum concentration/minimum inhibitory concentration achieved with different dosing regimens.

RESULTS

A total of 91 concentrations from 19 adult patients with CF were collected for external evaluation. The model predicted amikacin concentrations with reasonable bias (7.2% [95% confidence interval, CI: -0.7% to 15.0%]) and inaccuracy (18.2% [95% CI: 12.0%-24.4%]). Our simulations with this model suggest that administered amikacin doses must be adjusted to creatinine clearance and also adjusted to body weight (doses from 20 to 45 mg/kg/d). According to these simulations, we developed the Montreal amikacin nomogram to optimize amikacin dosing regimens in patients with CF.

CONCLUSION

In conclusion, we developed the first nomogram to optimize initial amikacin dosing regimens in patients with CF based on this external evaluation of a recently published amikacin population PK model.

Developmental Population Pharmacokinetics and Dosing Optimization of Cefepime in Neonates and Young Infants

Objective

Cefepime is used to treat severe infections in neonates. Pharmacokinetic data have only been evaluated among preterm neonates and population pharmacokinetic model lacked external validation. Hence, our aim is to obtain the population pharmacokinetic parameters of cefepime with large sampling and optimize the cefepime dosage regimen for neonatal infection based on developmental pharmacokinetics–pharmacodynamics.

Methods

Blood samples from neonates and young infants treated with cefepime were collected using the opportunistic sampling design. The concentration of cefepime was determined using high performance liquid chromatography with ultraviolet detection. The population pharmacokinetic model was established using NONMEM software.

Results

One hundred blood samples from eighty-five neonates were analyzed. The population pharmacokinetics of cefepime were described by a one-compartment model with first-order elimination. Covariate analysis indicated that serum creatinine concentration, postmenstrual age and current weight had significant impact on the pharmacokinetic parameters of cefepime. Monte Carlo simulation results showed that the current dosage regimen (30 mg/kg, q12 h) had a high risk of insufficient dose. For 70% of neonates to obtain a higher free drug concentration than the minimum inhibitory concentration during 70% of the dosing interval, 50 mg/kg q12 h was needed with a susceptibility breakpoint of 4 mg/l. For a minimum inhibitory concentration of 8 mg/l, 40 mg/kg q8 h was recommended for all neonates.

Conclusion

A population pharmacokinetic model of cefepime in neonates and young infants was established. According to simulation results based on the developmental pharmacokinetics–pharmacodynamics, different dosage regimens should be given depending on pathogens and the postmenstrual age.

High sensitivity versus low level of vancomycin needs to be concern for another alternative anti- Staphylococcus aureus as the first- line antibiotic

Background and aim: Vancomycin has been the first-line therapy for MRSA infection disease for many years. According to standard guidelines, the therapeutic vancomycin trough concentration should be above 10 mg/L and optimally between 15-20 mg/L. The aim of this study was to evaluate vancomycin trough level concentration in patients infected with MRSA.Methods: This cross- sectional study included a sample of 170 patients admitted to the ICU of Loghman hospital. We used a standard questionnaire, then applied appropriate statistical tests. All collected data had been analyzed and interpreted by IBM SPSS Statistics 19.0.Results: Among this study population, 71.8% was male. Just 20.8% of the patients can reach the therapeutic level trough even after changing the dose. It should be noted that a significant percentage of toxicity was observed after increasing the dose. Conclusions: Even though high sensitivity against vancomycin disc has been seen in antibiogram tests, sufficient efficiency has not been distinguished, in the sense that, just a few patients by low trough level concentration, reached to therapeutic level after the dose change. Based on some sources, because of the side effects and limited safe range of vancomycin, we should consider a new approach to the alternative antibiotics. (www.actabiomedica.it)

Optimizing Antibiotic Drug Therapy in Pediatrics: Current State and Future Needs

The selection of the right antibiotic and right dose necessitates clinicians understand the contribution of pharmacokinetic variability stemming from age-related physiologic maturation and the pharmacodynamics to optimize drug exposure for clinical response. The complexity of selecting the right dose arises from the multiplicity of pediatric age groups, from premature neonates to adolescents. Body size and age (which relate to organ function) must be incorporated to optimize antibiotic dosing in this vulnerable population. In the effort to optimize and individualize drug dosing regimens, clinical pharmacometrics that incorporate population-based pharmacokinetic modeling, Bayesian estimation, and Monte Carlo simulations are utilized as a quantitative approach to understanding and predicting the pharmacology and clinical and microbiologic efficacy of antibiotics. In addition, opportunistic study designs and alternative blood sampling strategies can serve as practical approaches to ensure successful conduct of pediatric studies. This review article examines relevant literature on optimization of antibiotic pharmacotherapy in pediatric populations published within the last decade. Specific pediatric antibiotic data, including beta-lactam antibiotics, aminoglycosides, and vancomycin, are critically evaluated.

Pharmacokinetic modelling and Bayesian estimation-assisted decision tools to optimize vancomycin dosage in neonates: only one piece of the puzzle

Introduction: Vancomycin is commonly administered to neonates, while observational data on therapeutic drug monitoring (TDM, trough levels) suggest that vancomycin exposure and dosage remain substandard. Area covered: Data on vancomycin pharmacokinetics (PK) and its covariates are abundant. Consequently, modeling is an obvious tool to improve targeted exposure, with a shift from TDM trough levels to area under the curve (AUC_24h) targets, as in adults. Continuous administration appeared as a practice to facilitate AUC_24h target attainment, while Bayesian model-supported targeting emerged as a novel tool. However, the AUC_24h/MIC (minimal inhibitory concentration) target itself should consider neonate-specific aspects (bloodstream infections, coagulase-negative staphylococci, protein binding, underexplored causes of variability, like assays, preparation and administration inaccuracies, or missing covariates). Expert opinion: To improve targeted exposure in neonates, initial vancomycin prescription should be based on 'a priori model-based individual dosing' using validated dosing regimens, followed by further tailoring by dosing optimization applying Bayesian estimation-assisted TDM. Future research should focus on the feasibility to integrate these tools (individualized dosing, Bayesian models) in clinical practice, and to perform PK/PD studies in the relevant animal models and human neonatal setting (coagulase-negative staphylococci, bloodstream infections).

Model extrapolation to a real-world dataset: evaluation of tacrolimus population pharmacokinetics and drug interaction in pediatric liver transplantation patients

1. Numerous tacrolimus population pharmacokinetic (PPK) models in pediatric liver transplantation patients have been established to define an optimal dose schedule. However, the applicability of extrapolating these PPK models to our clinical center remains unknown. The goals of the present study was to evaluate model external predictiveness and establish a new model applicable to traditional therapeutic drug monitoring data.2. Published PPK models were collected from the literature and assessed using our real-world dataset including 41 pediatric liver transplantation patients via the individual prediction error method. The establishment of a new model was characterized using non-linear mixed-effects modeling.3. Nine published pediatric liver transplantation PPK models were identified, three of which could be applied to our real-world dataset. However, these models were dissatisfactory in terms of individual prediction error and hence, inadequate for extrapolation. Finally, a new model applicable to our real-world dataset was established as follows: CL/F = 22.9 × (WT/70)^0.75 × (1 - WZ × 0.264) × (1 - FCZ × 0.338) × (1 + ASPI × 0.281) × (POD/41)^0.0486 L/h; V/F = 906 × (WT/70) L. Where WT, WZ, FCZ, ASPI and POD were weight, Wuzhi capsule, fluconazole, aspirin and post-transplantation day, respectively. In conclusion, published models were inadequate for application to our real-world dataset. The present study produced a new model applicable to our real-world study data.

Rational Use of Antibiotics in Neonates: Still in Search of Tailored Tools

Rational medicine use in neonates implies the prescription and administration of age-appropriate drug formulations, selecting the most efficacious and safe dose, all based on accurate information on the drug and its indications in neonates. This review illustrates that important uncertainties still exist concerning the different aspects (when, what, how) of rational antibiotic use in neonates. Decisions when to prescribe antibiotics are still not based on robust decision tools. Choices (what) on empiric antibiotic regimens should depend on the anticipated pathogens, and the available information on the efficacy and safety of these drugs. Major progress has been made on how (beta-lactam antibiotics, aminoglycosides, vancomycin, route and duration) to dose. Progress to improve rational antibiotic use necessitates further understanding of neonatal pharmacology (short- and long-term safety, pharmacokinetics, duration and route) and the use of tailored tools and smarter practices (biomarkers, screening for colonization, and advanced therapeutic drug monitoring techniques). Implementation strategies should not only facilitate access to knowledge and guidelines, but should also consider the most effective strategies (‘skills’) and psychosocial aspects involved in the prescription process: we should be aware that both the decision not to prescribe as well as the decision to prescribe antibiotics is associated with risks and benefits.

Systematic external evaluation of published population pharmacokinetic models of mycophenolate mofetil in adult kidney transplant recipients co-administered with tacrolimus

AIMS

Various mycophenolate mofetil (MMF) population pharmacokinetic (popPK) models have been developed to describe its PK characteristics and facilitate its optimal dosing in adult kidney transplant recipients co-administered with tacrolimus. However, the external predictive performance has been unclear. Thus, this study aimed to comprehensively evaluate the external predictability of published MMF popPK models in such populations and investigate the potential influencing factors.

METHODS

The external predictability of qualified popPK models was evaluated using an independent dataset. The evaluation included prediction- and simulation-based diagnostics, and Bayesian forecasting. In addition, factors influencing model predictability, especially the impact of structural models, were investigated.

RESULTS

Fifty full PK profiles from 45 patients were included in the evaluation dataset and 11 published popPK models were identified and evaluated. In prediction-based diagnostics, the prediction error within ±30% was less than 50% in most published models. The prediction- and variability-corrected visual predictive check and posterior predictive check showed large discrepancies between the observations and simulations in most models. Moreover, the normalized prediction distribution errors of all models did not follow a normal distribution. Bayesian forecasting demonstrated an improvement in the model predictability. Furthermore, the predictive performance of two-compartment (2CMT) models incorporating the enterohepatic circulation (EHC) process was not superior to that of conventional 2CMT models.

CONCLUSIONS

The published models showed large variability and unsatisfactory predictive performance, which indicated that therapeutic drug monitoring was necessary for MMF clinical application. Further studies incorporating potential covariates need to be conducted to investigate the key factors influencing model predictability of MMF.

Population Pharmacokinetics and Dosing Optimization of Amoxicillin in Neonates and Young Infants

Amoxicillin is widely used to treat bacterial infections in neonates. However, considerable intercenter variability in dosage regimens of antibiotics exists in clinical practice. The pharmacokinetics of amoxicillin has been described in only a few preterm neonates. Thus, we aimed to evaluate the population pharmacokinetics of amoxicillin through a large sample size covering the entire age range of neonates and young infants and to establish evidence-based dosage regimens based on developmental pharmacokinetics-pharmacodynamics. This is a prospective, multicenter, pharmacokinetic study using an opportunistic sampling design. Amoxicillin plasma concentrations were determined using high-performance liquid chromatography. Population pharmacokinetic analysis was performed using NONMEM. A total of 224 pharmacokinetic samples from 187 newborns (postmenstrual age range, 28.4 to 46.3 weeks) were available for analysis. A two-compartment model with first-order elimination was used to describe population pharmacokinetics. Covariate analysis showed that current weight, postnatal age, and gestational age were significant covariates. The final model was further validated for predictive performance in an independent cohort of patients. Monte Carlo simulation demonstrated that for early-onset sepsis, the currently used dosage regimen (25 mg/kg twice daily [BID]) resulted in 99.0% of premature neonates and 87.3% of term neonates achieving the pharmacodynamic target (percent time above MIC), using a MIC breakpoint of 1 mg/liter. For late-onset sepsis, 86.1% of premature neonates treated with 25 mg/kg three times a day (TID) and 79.0% of term neonates receiving 25 mg/kg four times a day (QID) reached the pharmacodynamic target, using a MIC breakpoint of 2 mg/liter. The population pharmacokinetics of amoxicillin was assessed in neonates and young infants. A dosage regimen was established based on developmental pharmacokinetics-pharmacodynamics.

Continuous-Infusion Vancomycin in Neonates: Assessment of a Dosing Regimen and Therapeutic Proposal

Introduction: Vancomycin remains the reference antibiotic in neonates for care-related infections caused by ß-lactam-resistant Gram-positive bacteria. Achieving the optimal serum vancomycin level is challenging because of high inter-individual variability and the drug's narrow therapeutic window. Continuous infusion might offer pharmacokinetic and practical advantages, but we lack consensus on the dosing regimen. The aim was to determine the proportion of neonates achieving an optimal therapeutic vancomycin level at the first vancomycin concentration assay and which dosing regimen is the most suitable for neonates. Methods: All neonates receiving continuous-infusion vancomycin (loading dose 15 mg/kg and maintenance dose 30 mg/kg/d) in a neonatal intensive care unit were retrospectively analyzed. The proportion of neonates reaching the target serum vancomycin level was calculated. After reviewing the literature to identify all published articles proposing a dosing regimen for continuous-infusion vancomycin for neonates, regimens were theoretically applied to our population by using maintenance doses according to covariate(s) proposed in the original publication. Results: Between January 2013 and December 2014, 75 neonates received 91 vancomycin courses by continuous infusion. Median gestational age, birth weight, and postnatal age were 27 weeks (interquartile range 26-30.5), 815 g (685-1,240), and 15 days (9-33). At the first assay, only 28/91 (30.8%) courses resulted in vancomycin levels between 20 and 30 mg/L (target level), 23/91 (25.3%) >30 mg/L and 40/91 (43.9%) <20 mg/L. We applied six published dosing regimens to our patients. One of these dosing regimens based on corrected gestational age (CGA) and serum creatinine level (SCR) would have allowed us to prescribe lower doses to neonates with high vancomycin levels and higher doses to neonates with low levels. Conclusions: A simplified dosing regimen of continuous-infusion vancomycin did not achieve therapeutic ranges in neonates; a patient-tailored dosing regimen taking into account CGA and SCR level or an individualized pharmacokinetic model can help to anticipate the inter-individual variability in neonates and would have been more suitable.