Evaluation and Application of Population Pharmacokinetic Models for Identifying Delayed Methotrexate Elimination in Patients With Primary Central Nervous System Lymphoma

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.

Factors influencing methotrexate pharmacokinetics highlight the need for individualized dose adjustment: a systematic review

PURPOSE

To develop a population pharmacokinetic (PPK) model for methotrexate (MTX) dosage for all ages, assess the association between concentration and clearance, and determine covariates affecting MTX disposition.

METHODS

We compared MTX PK profiles among neonates, children, and adults by performing a systematic literature search for published population MTX models and conducted a Monte Carlo-based meta-analysis. Subsequently, we evaluated study quality and covariates significantly affecting dosage regimens and compared LDMTX and HDMTX PK profiles.

RESULTS

Of the total 40 studies included, 34 were HDMTX, and six were LDMTX studies. For HDMTX, three studies involving neonates reported estimated apparent clearances (median, range) of 0.53 (0.27-0.77) L/kg/h; for 14 studies involving children, 0.23 (0.07-0.23) L/kg/h; and for 13 involving adults, 0.11 (0.03-0.22) L/kg/h. Neonates had a higher volume of distribution than children and adults. For LDMTX studies, apparent clearance was 0.085 (0.05-1.68) L/kg/h, and volume of distribution was 0.25 (0.018-0.47) L/kg, lower than those of HDMTX studies, with large between-subject variability. Bodyweight significantly influenced apparent clearance and volume of distribution, whereas renal function mainly influenced clearance. Mutations in certain genes reduced MTX clearance by 8-35.3%, whereas those in others increased it by 15-48%. Body surface area (BSA) significantly influenced apparent clearance with a median reduction of 51% when BSA increased in pediatric patients.

CONCLUSIONS

Methotrexate dosage regimens were primarily based on body surface area and renal function. Further studies are needed to evaluate MTX pharmacokinetics and pharmacodynamics in both children (especially infants) and adults.

External evaluation and systematic review of population pharmacokinetic models for high-dose methotrexate in cancer patients

Several population pharmacokinetic (PPK) models have been established to optimize the therapeutic regimen and reduce the toxicity of high-dose methotrexate (HDMTX) in patients with cancer. However, their predictive performance when extrapolated to different clinical centers was unknown. In this study, we aimed to externally evaluate the predictive ability of HDMTX PPK models and determine the potential influencing factors. We searched the literature and determined the predictive performance of the selected models using methotrexate concentrations in 721 samples from 60 patients in the First Affiliated Hospital of the Navy Medical University. Prediction-based diagnostics and simulation-based normalized prediction distribution errors (NPDE) were used to evaluate the predictive performance of the models. The influence of prior information was also assessed using Bayesian forecasting, and the potential factors affecting model predictability were investigated. Thirty models extracted from published PPK studies were assessed. Prediction-based diagnostics showed that the number of compartments potentially influenced model transferability, and simulation-based NPDE indicated model misspecification. Bayesian forecasting significantly improved the predictive performance of the models. Various factors, including bioassays, covariates, and population diagnosis, influence model extrapolation. The published models were unsatisfactory for all prediction-based diagnostics, except for the 24 h methotrexate concentration monitoring and simulation-based diagnostics, making them inappropriate for direct extrapolation. Moreover, Bayesian forecasting combined therapeutic drug monitoring could improve the predictive performance of the models.