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

Using Real-World Data to Externally Evaluate Population Pharmacokinetic Models of Dexmedetomidine in Children and Infants

Dexmedetomidine is a sedative used in both adults and off-label in children with considerable reported pharmacokinetic (PK) interindividual variability affecting drug exposure across populations. Several published models describe the population PKs of dexmedetomidine in neonates, infants, children, and adolescents, though very few have been externally evaluated. A prospective PK dataset of dexmedetomidine plasma concentrations in children and young adults aged 0.01-19.9 years was collected as part of a multicenter opportunistic PK study. A PubMed search of studies reporting dexmedetomidine PK identified five population PK models developed with data from demographically similar children that were selected for external validation. A total of 168 plasma concentrations from 102 children were compared with both population (PRED) and individualized (IPRED) predicted values from each of the five published models by quantitative and visual analyses using NONMEM (v7.3) and R (v4.1.3). Mean percent prediction errors from observed values ranged from -1% to 120% for PRED, and -24% to 60% for IPRED. The model by James et al, which was developed using similar "real-world" data, nearly met the generalizability criteria from IPRED predictions. Other models developed using clinical trial data may have been limited by inclusion/exclusion criteria and a less racially diverse population than this study's opportunistic dataset. The James model may represent a useful, but limited tool for model-informed dosing of hospitalized children.

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 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 of imatinib in adults diagnosed with chronic myeloid leukaemia

AIMS

Imatinib is considered the standard first-line treatment in newly diagnosed patients with chronic-phase myeloid leukaemia (CML). Several imatinib population pharmacokinetic (popPK) models have been developed. However, their predictive performance has not been well established when extrapolated to different populations. Therefore, this study aimed to perform an external evaluation of available imatinib popPK models developed mainly in adult patients, and to evaluate the improvement in individual model-based predictions through Bayesian forecasting computed by each model at different treatment occasions.

METHODS

A literature review was conducted through PubMed and Scopus to identify popPK models. Therapeutic drug monitoring data collected in adult CML patients treated with imatinib was used for external evaluation, including prediction- and simulated-based diagnostics together with Bayesian forecasting analysis.

RESULTS

Fourteen imatinib popPK studies were included for model-performance evaluation. A total of 99 imatinib samples were collected from 48 adult CML patients undergoing imatinib treatment with a minimum of one plasma concentration measured at steady-state between January 2016 and December 2020. The model proposed by Petain et al showed the best performance concerning prediction-based diagnostics in the studied population. Bayesian forecasting demonstrated a significant improvement in predictive performance at the second visit. Inter-occasion variability contributed to reducing bias and improving individual model-based predictions.

CONCLUSIONS

Imatinib popPK studies developed in Caucasian subjects including α1-acid glycoprotein showed the best model performance in terms of overall bias and precision. Moreover, two imatinib samples from different visits appear sufficient to reach an adequate model-based individual prediction performance trough Bayesian forecasting.