Evaluating Performance of a Decision Support System to Improve Methotrexate Pharmacotherapy in Children and Young Adults With Cancer

Abstract

The management of high-dose methotrexate (MTX) therapy in patients with cancer depends on the routine monitoring of drug exposures in conjunction with leucovorin (LV), urine pH, patient hydration and other clinical indices of patient well-being. A key factor in patient oversight is the facilitation of MTX clearance in order to minimize drug-related toxicity. The aim of this investigation was to evaluate the performance of a clinical decision support system and Bayesian forecasting algorithm in the prediction of MTX concentrations and assessment of LV dosing requirements in pediatric and young adult cancer patients based on the current practice at the Children’s Hospital of Philadelphia. Fifty patients ranging in age from 8 months to 21 years (weight range,7.6 to 163.3 kg) contributing 80 total dosing events (183 MTX serum concentrations) were studied. The forecasting model was able to consistently predict future MTX concentrations with the knowledge of one prior concentration and continued to improve with additional concentration data made available through daily therapeutic drug monitoring. Precision was good at 12.9% with low bias at 2.2%. Comparison between the decision support system recommendations for LV rescue relative to the actual LV administration was also made. Sixteen patients would have initiated rescue therapy earlier, 7 patients would have received a larger dose (42 smaller) and LV would have been given less often for 37 patients. The forecasting algorithm in the MTX dashboard was reasonably accurate in predicting MTX concentrations and should improve further as the underlying model and prediction algorithm evolves. This decision support system can be useful in helping physicians decide if a patient is clearing MTX as expected or if more aggressive rescue therapy is warranted.

Developing Population Pharmacokinetic Parameters for High-Dose Methotrexate Therapy: Implication of Correlations among Developed Parameters for Individual Parameter Estimation Using the Bayesian Least-Squares Method

“…This pair of parameters had been included in most of the previously reported population parameters. 9,[11][12][13][14][15][16][17][18][19] This result indicates that the reported population parameters may be inappropriate for the Bayesian least-squares method used in the present study. Thus, we compared correlations among the possible combinations of population parameters.…”

“…The former was the combination that had the weakest correlations in the present study, and the latter was the most frequently reported combination in previous studies. [9][10][11][12][13][14][15][16][17][18][19] As shown above, the sum of the absolute value of correlation coefficients among V 1 , k 10 , k 12 , and k 21 was 1.05 for regimen B, whereas V 1 , V 2 , CL, and Q was 3.29 for regimen B. Because of the relatively strong correlations among V 1 , V 2 , CL, and Q, this combination of the population parameters was considered to be a good target for a comparison with V 1 , k 10 , k 12 , and k 21 .…”

“…In addition, because an increase in the parameters included in the pharmacokinetic models increases the possible correlations among the parameters, this issue should be considered especially for medications in which the pharmacokinetic pro-file is a 2 or higher dimensional compartment model. MTX pharmacokinetics were previously shown to be accounted for by a 2-compartment model, [9][10][11][12][13][14][15][16][17][18][19] which suggests that this issue needs to be considered. However, to the best of our knowledge, correlations and/or covariance among population parameters have not been considered especially in the clinical use of Bayesian estimation performed using the Bayesian least-squares method.…”

“…Several population parameters have already been developed for cases receiving HD-MTX, and the usefulness of these parameters has been tested. [9][10][11][12][13][14][15][16][17][18][19] Many of these studies were performed using NONMEM, which has been used as the standard method for population pharmacokinetic analyses including Bayesian estimation. However, this software is rarely used in clinical settings because of the difficulties associated with its use.…”

Modeling NSCLC Progression: Recent Advances and Opportunities Available

“…Modeling and simulation which represent the essential elements of pharmacometrics can be used in oncological drug development in selecting appropriate doses for clinical trials, optimizing trial designs, interpreting outcome data, or even predicting outcomes in settings which have not been explicitly studied (10). Pharmacometrics was also proven to be useful in individualizing clinical therapy in oncology and has been advocated for such a purpose (11)(12)(13)(14). This could be achieved based on the different covariates (such as gender, weight, mutational status of the patients, and/or others) which were found to significantly affect the outcome.…”