External Evaluation of Vancomycin Population Pharmacokinetic Models at Two Clinical Centers

A Review of Vancomycin, Gentamicin, and Amikacin Population Pharmacokinetic Models in Neonates and Infants

Population pharmacokinetic (popPK) models are an essential tool when implementing therapeutic drug monitoring (TDM) and to overcome dosing challenges in neonates in clinical practice. Since vancomycin, gentamicin, and amikacin are among the most prescribed antibiotics for the neonatal population, we aimed to characterize the popPK models of these antibiotics and the covariates that may influence the pharmacokinetic parameters in neonates and infants with no previous pathologies. We searched the PubMed, Embase, Web of Science, and Scopus databases and the bibliographies of relevant articles from inception to the beginning of February 2024. The search identified 2064 articles, of which 68 met the inclusion criteria (34 for vancomycin, 21 for gentamicin, 13 for amikacin). A one-compartment popPK model was more frequently used to describe the pharmacokinetics of the three antibiotics (91.2% vancomycin, 76.9% gentamicin, 57.1% amikacin). Pharmacokinetic parameter (mean ± standard deviation) values calculated for a "typical" neonate weighing 3 kg were as follows: clearance (CL) 0.34 ± 0.80 L/h for vancomycin, 0.27 ± 0.49 L/h for gentamicin, and 0.19 ± 0.07 L/h for amikacin; volume of distribution (Vd): 1.75 ± 0.65 L for vancomycin, 1.54 ± 0.53 L for gentamicin, and 1.67 ± 0.27 L for amikacin for one-compartment models. Total body weight, postmenstrual age, and serum creatinine were common predictors (covariates) for describing the variability in CL, whereas only total body weight predominated for Vd. A single universal popPK model for each of the antibiotics reviewed cannot be implemented in the neonatal population because of the significant variability between them. Body weight, renal function, and postmenstrual age are important predictors of CL in the three antibiotics, and total body weight for Vd. TDM represents an essential tool in this population, not only to avoid toxicity but to attain the desired pharmacokinetic/pharmacodynamic index. The characteristics of the neonatal population, coupled with the lack of prospective studies and external validation of most models, indicate a need to continue investigating the pharmacokinetics of these antibiotics in neonates.

External evaluation of neonatal vancomycin population pharmacokinetic models: Moving from first-order equations to Bayesian-guided therapeutic monitoring

INTRODUCTION

Guidelines for vancomycin therapeutic monitoring recommend using a Bayesian approach with a population pharmacokinetic model to estimate the 24 h area under the concentration-time curve over first-order equations. Thus, we performed an external evaluation of population pharmacokinetic models of vancomycin in neonates and compared Bayesian results with those observed in clinical practice via pharmacokinetic equations to improve therapeutic monitoring by proposing optimized initial dosing nomograms and assessing the feasibility of reduced blood sampling strategies using the most predictive models.

METHODS

Models were identified from the literature and evaluated via an external neonatal population. A priori predictive performance was first assessed by prediction-based diagnostics, then by simulation-based diagnostics and a posteriori analyses only if deemed satisfactory; model-informed vancomycin exposure was also compared with reference first-order pharmacokinetic equations. The best-performing models were ultimately subjected to Monte Carlo simulations to develop new initial dosing nomograms offering the highest probability of achieving therapeutic target.

RESULTS

A total of 28 population pharmacokinetic models were evaluated in the external dataset, which includes 72 neonates and 380 vancomycin concentrations. Eleven models had an adequate predictive performance with bias ≤ ± 15% and imprecision ≤ 30%, while the Bayesian approach yielded over 75% agreement with reference exposure values in most cases. Nonetheless, Capparelli et al. and Mehrotra et al. models performed the best overall, showing the lowest imprecisions of 16.8% and 16.9%, respectively; both models recommended higher dosage regimens than the theoretical nomogram currently applied to favor therapeutic target attainment.

DISCUSSION

We externally evaluated numerous neonatal population pharmacokinetic models of vancomycin and used the most predictive ones to advocate new initial dosing nomograms. Clinical implementation of the Bayesian approach could reduce the time needed to reach therapeutic target and limit the number of blood samples in newborns compared with traditional pharmacokinetic equations.

Physiologically Based Pharmacokinetic Modeling of Vancomycin and its Comparison with Population Pharmacokinetic Model in Neonates

Vancomycin has a narrow therapeutic window and a high inter-individual pharmacokinetic variability, especially in neonates with fast maturational and pathophysiological changes, that needs individualized dosing. Physiologically based pharmacokinetic (PBPK) model and population pharmacokinetic (PopPK) model are both useful tools in model-informed precision dosing, while the former is under research in application of vancomycin in neonates. This study aimed to develop a PBPK model of vancomycin in adult and pediatric population, and compared it with published PopPK model (priori or Bayesian method) in predicting vancomycin concentration in 230 neonatal patients (postmenstrual age, PMA, 25-45 weeks). The developed PBPK model showed a good fit between predictions and observations. PBPK model and PopPK model are complementary in different clinical scenarios of vancomycin application. The physiological-change description of PBPK model showed a superior advantage in initial dosing optimization. As for subsequent dose optimization, PopPK Bayesian forecasting performed better than the PBPK estimation in neonates. However, initial precision dosing tools for early neonates (with PMA < 36 weeks) still need further exploitation.

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.

Prospective validation of a model-informed precision dosing tool for vancomycin treatment in neonates

We recruited 48 neonates (50 vancomycin treatment episodes) in a prospective study to validate a model-informed precision dosing (MIPD) software. The initial vancomycin dose was based on a population pharmacokinetic model and adjusted every 36-48 h. Compared with a historical control group of 53 neonates (65 episodes), the achievement of a target trough concentration of 10-15 mg/L improved from 37% in the study to 62% in the MIPD group (P = 0.01), with no difference in side effects.

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.

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

OBJECTIVES

Several linezolid population pharmacokinetic (popPK) models have been established to facilitate optimal therapy; however, their extrapolated predictive performance to other clinical sites is unknown. This study aimed to externally evaluate the predictive performance of published pharmacokinetic models of linezolid in adult patients.

METHODS

For the evaluation dataset, 150 samples were collected from 70 adult patients (72.9% of which were critically ill) treated with linezolid at our center. Twenty-five published popPK models were identified from PubMed and Embase. Model predictability was evaluated using prediction-based, simulation-based, and Bayesian forecasting-based approaches to assess model predictability.

RESULTS

Prediction-based diagnostics found that the prediction error within ±30% (F30) was less than 40% in all models, indicating unsatisfactory predictability. The simulation-based prediction- and variability-corrected visual predictive check and normalized prediction distribution error test indicated large discrepancies between the observations and simulations in most of the models. Bayesian forecasting with one or two prior observations significantly improved the models' predictive performance.

CONCLUSION

The published linezolid popPK models showed insufficient predictive ability. Therefore, their sole use is not recommended, and incorporating therapeutic drug monitoring of linezolid in clinical applications is necessary.

External Validation of a Vancomycin Population Pharmacokinetic Model and Developing a New Dosage Regimen in Neonates

BACKGROUND AND OBJECTIVE

Vancomycin is the drug of choice in the treatment of MRSA infections. In a published vancomycin population pharmacokinetic study on neonates in Singapore healthcare institutions, it was found that vancomycin clearance was predicted by weight, postmenstrual age, and serum creatinine. The aim of this study was to externally validate the vancomycin population pharmacokinetic model to develop a new dosage regimen in neonates, and to compare this regimen with the existing institutional and NeoFax^® dosage regimens.

METHODS

A retrospective chart review of neonates who received vancomycin therapy and therapeutic drug monitoring was conducted. The median prediction error percentage was calculated to assess bias, while the median absolute prediction error percentage and the root mean squared error percentage were calculated to assess precision. The new dosage regimen was developed using Monte Carlo simulation.

RESULTS

A total of 20 neonates were included in the external validation dataset. Eighteen of them were premature, with a median gestational age of 27.7 (25.9-31.5) weeks and postmenstrual age of 30.5 (27.3-34.3) weeks at the point of vancomycin initiation. No apparent systematic bias was found in the predictions of the model. The external validation performed in the current study found the model to be generally unbiased. Our new vancomycin dosage regimen was able to achieve target trough concentrations and area under the curve (AUC₂₄) at a greater proportion as compared to existing institutional and NeoFax^® dosage regimens.

CONCLUSION

The pharmacokinetic model built in the previous study can be used to conduct reliable population simulations of our Asian neonatal population in Singapore. The new dosage regimen was able to achieve target trough concentrations and AUC₂₄ better than existing institutional and NeoFax^® dosage regimens.

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

OBJECTIVES

Patients with hematological malignancies are prone to invasive fungal disease due to long-term chemotherapy or radiotherapy. Voriconazole is a second-generation triazole broad-spectrum antibiotic used to prevent or treat invasive fungal infections. Many population pharmacokinetic (pop PK) models have been published for voriconazole, and various diagnostic methods are available to validate the performance of these pop PK models. However, most of the published models have not been strictly evaluated externally. The purpose of this study is to evaluate these models externally and assess their predictive capabilities.

METHODS

The external dataset consists of adults receiving voriconazole treatment at Fujian Medical University Union Hospital. We re-established the published models based on their final estimated values in the literature and used our external dataset for initial screening. Each model was evaluated based on the following outcomes: prediction-based diagnostics, prediction- and variability-corrected visual predictive check (pvcVPC), normalized prediction distribution errors (NPDE), and Bayesian simulation results with one to two prior observations.

RESULTS

A total of 237 samples from 166 patients were collected as an external dataset. After screening, six candidate models suitable for the external dataset were finally obtained for comparison. Among the models, none demonstrated excellent predictive performance. Bayesian simulation shows that all models' prediction precision and accuracy were significantly improved when one or two prior concentrations were given.

CONCLUSIONS

The published pop PK models of voriconazole have significant differences in prediction performance, and none of the models could perfectly predict the concentrations of voriconazole for our data. Therefore, extensive evaluation should precede the adoption of any model in clinical practice.