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

Abstract

Bayesian estimation enables the individual pharmacokinetic parameters of the medication administrated to be estimated using only a few blood concentrations. Due to wide inter-individual variability in the pharmacokinetics of methotrexate (MTX), the concentration of MTX needs to be frequently determined during high-dose MTX therapy in order to prevent toxic adverse events. To apply the benefits of Bayesian estimation to cases treated with this therapy, we attempted to develop an estimation method using the Bayesian leastsquares method, which is commonly used for therapeutic monitoring in a clinical setting. Because this method hypothesizes independency among population pharmacokinetic parameters, we focused on correlations among population pharmacokinetic parameters used to estimate individual parameters. A two-compartment model adequately described the observed concentration of MTX. The individual pharmacokinetic parameters of MTX were estimated in 57 cases using the maximum likelihood method. Among the available parameters accounting for a 2-compartment model, V 1 , k 10 , k 12 , and k 21 were found to be the combination showing the weakest correlations, which indicated that this combination was best suited to the Bayesian least-squares method. Using this combination of population pharmacokinetic parameters, Bayesian estimation provided an accurate estimation of individual parameters. In addition, we demonstrated that the degree of correlation among population pharmacokinetic parameters used in the estimation affected the precision of the estimates. This result highlights the necessity of assessing correlations among the population pharmacokinetic parameters used in the Bayesian least-squares method.

The Population Pharmacokinetics of High-Dose Methotrexate in Infants with Acute Lymphoblastic Leukemia Highlight the Need for Bedside Individualized Dose Adjustment: A Report from the Children’s Oncology Group

“…A two-compartment model best characterized the PK of MTX in the 71 infants in our study, which is consistent with previous reports of high-dose MTX in both children and adults [46,48,[50][51][52][53][54][55][56][57]. Our model applied theory based allometry to scale CL and volume of distribution parameters to a standard weight of 70 kg [17,58].…”

Population Pharmacokinetics of High-Dose Methotrexate in Chinese Pediatric Patients With Acute Lymphoblastic Leukemia

“…The previous population PK models suggested that a list of covariates [e.g., age (Aumente et al, 2006;Thompson et al, 2007;Colom et al, 2009), body weight (Aumente et al, 2006;Colom et al, 2009), renal function (Dupuis et al, 2008;Fukuhara et al, 2008;Mao et al, 2014), and polymorphisms of transporters (Kim et al, 2012;Liu et al, 2017)] were related to MTX PK exposure. Unlike the previously published population PK models using the two-compartment disposition model (Wall et al, 2000;Aumente et al, 2006;Faltaos et al, 2006;Piard et al, 2007;Min et al, 2009;Buitenkamp et al, 2010;Jönsson et al, 2011;Watanabe et al, 2014;Beechinor et al, 2019), the current study based on 311 pediatric patients enabled to fit the MTX concentration-time data by using a three-compartment disposition model. The final model demonstrated that the MTX clearance estimate in a typical child with a body weight of 19 kg was 6.9 L/h.…”

Population Pharmacokinetic Study and Individual Dose Adjustments of High‐Dose Methotrexate in Chinese Pediatric Patients With Acute Lymphoblastic Leukemia or Osteosarcoma

“…28 Base Model Development Current literature and investigations of the pharmacokinetic profiles suggest that a 2-compartment model sufficiently describes the pharmacokinetics of highdose methotrexate, so it was chosen as the base model. [32][33][34][35][36][37][38][39][40][41][42][43][44] Interindividual variabilities in pharmacokinetic parameters, namely clearance, central volume, intercompartmental clearance, peripheral volume, and interoccasion variability in clearance were assumed to follow log-normal distributions. Residual variability was described using the exponential error model.…”