Population pharmacokinetic and pharmacogenetic analysis of 6-mercaptopurine in paediatric patients with acute lymphoblastic leukaemia

Abstract

Aims: To investigate the population pharmacokinetics of 6-mercaptopurine (6-MP) active metabolites in paediatric patients with acute lymphoblastic leukaemia (ALL) and examine the effects of various genetic polymorphisms on the disposition of these metabolites.

Methods: Data were collected prospectively from 19 paediatric patients with ALL (n = 75 samples, 150 concentrations) who received 6-MP maintenance chemotherapy (titrated to a target dose of 75 mg m(-2) day(-1)). All patients were genotyped for polymorphisms in three enzymes involved in 6-MP metabolism. Population pharmacokinetic analysis was performed with the nonlinear mixed effects modelling program (nonmem) to determine the population mean parameter estimate of clearance for the active metabolites.

Results: The developed model revealed considerable interindividual variability (IIV) in the clearance of 6-MP active metabolites [6-thioguanine nucleotides (6-TGNs) and 6-methylmercaptopurine nucleotides (6-mMPNs)]. Body surface area explained a significant part of 6-TGNs clearance IIV when incorporated in the model (IIV reduced from 69.9 to 29.3%). The most influential covariate examined, however, was thiopurine methyltransferase (TPMT) genotype, which resulted in the greatest reduction in the model's objective function (P < 0.005) when incorporated as a covariate affecting the fractional metabolic transformation of 6-MP into 6-TGNs. The other genetic covariates tested were not statistically significant and therefore were not included in the final model.

Conclusions: The developed pharmacokinetic model (if successful at external validation) would offer a more rational dosing approach for 6-MP than the traditional empirical method since it combines the current practice of using body surface area in 6-MP dosing with a pharmacogenetically guided dosing based on TPMT genotype.

Figure 1

Individual 6-thioguanine nucleotide (6-TGN) (A) and 6-methylmercaptopurine nucleotide (6-mMPN) (B) concentration plots. Patients having any mutation are highlighted and their corresponding types are displayed. The therapeutic lower and upper limits suggested in literature are indicated by the dashed lines. TPMT mutant (▵); ITPA mutant (□); TPMT and ITPA mutant (○)

Figure 2

Plots of the conditional estimates of η_i,CL6-TGNsvs. body surface area. The solid line indicates the Lowess smooth line

Figure 3

Scatter plots of observed vs. population predicted (A) and individual predicted (B) red blood cell (RBC) concentrations of 6-thioguanine nucleotides (6-TGNs) in the index group of the FINAL model. The solid line represents the line of identity

Figure 4

Scatter plots of observed vs. population predicted (A) and individual predicted (B) red blood cell (RBC) concentrations of 6-methylmercaptopurine nucleotides (6-mMPNs) in the index group of the FINAL model. The solid line represents the line of identity

Figure 6

Longitudinal assessment of the predictive performance of the FINAL model in two representative patients from the validation dataset: (A) 12-year-old boy and (B) 6-year-old girl. (▪) Observed and (•) model-predicted 6-thioguanine nucleotide (6-TGN) concentrations. (▴) Observed and (▾) predicted 6-methylmercaptopurine nucleotide (6-mMPN) concentrations

Figure 5

Scatter plots of weighted residuals vs. predicted concentrations of 6-thioguanine nucleotides (6-TGNs) (A) and 6-methylmercaptopurine nucleotides (6-mMPNs) (B) in red blood cells (RBC)

Figure 7

Visual predictive check of the final model fitted to the full dataset (n = 19 patients). A plot of the time course of the observed concentrations of 6-thioguanine nucleotides (6-TGNs) (A) and 6-methylmercaptopurine nucleotides (6-mMPNs) (B) along with the median and 90% prediction intervals for the simulated values. Median prediction (------); 90% prediction interval (—); Observed concentrations (•)