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

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • The cytotoxic effects of 6-mercaptopurine (6-MP) were found to be due to drug-derived intracellular metabolites (mainly 6-thioguanine nucleotides and to some extent 6-methylmercaptopurine nucleotides) rather than the drug itself. • Current empirical dosing methods for oral 6-MP result in highly variable drug and metabolite concentrations and hence variability in treatment outcome. WHAT THIS STUDY ADDS • The first population pharmacokinetic model has been developed for 6-MP active metabolites in paediatric patients with acute lymphoblastic leukaemia and the potential demographic and genetically controlled factors that could lead to interpatient pharmacokinetic variability among this population have been assessed. • The model shows a large reduction in interindividual variability of pharmacokinetic parameters when body surface area and thiopurine methyltransferase polymorphism are incorporated into the model as covariates. • The developed model offers a more rational dosing approach for 6-MP than the traditional empirical method (based on body surface area) through combining it with pharmacogenetically guided dosing based on thiopurine methyltransferase genotype. 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.

The development of an objective methodology to measure medication adherence to oral thiopurines in paediatric patients with acute lymphoblastic leukaemia - an exploratory study

Aims - To develop a method that prospectively assesses adherence rates in paediatric patients with acute lymphoblastic leukaemia (ALL) who are receiving the oral thiopurine treatment 6-mercaptopurine (6-MP). Methods - A total of 19 paediatric patients with ALL who were receiving 6-MP therapy were enrolled in this study. A new objective tool (hierarchical cluster analysis of drug metabolite concentrations) was explored as a novel approach to assess non-adherence to oral thiopurines, in combination with other objective measures (the pattern of variability in 6-thioguanine nucleotide erythrocyte concentrations and 6-thiouric acid plasma levels) and the subjective measure of self-reported adherence questionnaire. Results - Parents of five ALL patients (26.3%) reported at least one aspect of non-adherence, with the majority (80%) citing “carelessness at times about taking medication” as the primary reason for non-adherence followed by “forgetting to take the medication” (60%). Of these patients, three (15.8%) were considered non-adherent to medication according to the self-reported adherence questionnaire (scored ≥ 2). Four ALL patients (21.1%) had metabolite profiles indicative of non-adherence (persistently low levels of metabolites and/or metabolite levels clustered variably with time). Out of these four patients, two (50%) admitted non-adherence to therapy. Overall, when both methods were combined, five patients (26.3%) were considered non-adherent to medication, with higher age representing a risk factor for non-adherence (P < 0.05). Conclusions - The present study explored various ways to assess adherence rates to thiopurine medication in ALL patients and highlighted the importance of combining both objective and subjective measures as a better way to assess adherence to oral thiopurines.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Pharmacogenomic studies of the anticancer and immunosuppressive thiopurines mercaptopurine and azathioprine

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • 6-Mercaptopurine (6-MP) and azathioprine (AZA) are both inactive prodrugs that require intracellular activation into the active 6-thioguanine nucleotides (6-TGNs). • This metabolic process undergoes three different competitive pathways that are catalysed by three different enzymes; xanthine oxidase (XO), thiopurine methyltransferase (TPMT) and inosine triphosphatase (ITPA), all of which exhibit genetic polymorphisms. • Although the impact of genetic variation in the TPMT gene on treatment outcome and toxicity has been demonstrated, the role of other polymorphisms remains less well known. WHAT THIS STUDY ADDS • New information on the allelic variation of these three enzymes (XO, TPMT and ITPA) and their influence on 6-MP/AZA metabolism and toxicity. • Confirmation of the association of TPMT polymorphism with haematological toxicity. • Identified potential genetic characteristics that may contribute to higher risk of adverse events (such as ITPA IVS2+21A→C mutation). AIMS - To examine the allelic variation of three enzymes involved in 6-mercaptopurine/azathioprine (6-MP/AZA) metabolism and evaluate the influence of these polymorphisms on toxicity, haematological parameters and metabolite levels in patients with acute lymphoblastic leukaemia (ALL) or inflammatory bowel disease (IBD). METHODS - Clinical data and blood samples were collected from 19 ALL paediatric patients and 35 IBD patients who were receiving 6-MP/AZA therapy. All patients were screened for seven genetic polymorphisms in three enzymes involved in mercaptopurine metabolism [xanthine oxidase, inosine triphosphatase (C94→A and IVS2+21A→C) and thiopurine methyltransferase]. Erythrocyte and plasma metabolite concentrations were also determined. The associations between the various genotypes and myelotoxicity, haematological parameters and metabolite concentrations were determined. RESULTS - Thiopurine methyltransferase variant alleles were associated with a preferential metabolism away from 6-methylmercaptopurine nucleotides (P = 0.008 in ALL patients, P = 0.038 in IBD patients) favouring 6-thioguanine nucleotides (6-TGNs) (P = 0.021 in ALL patients). Interestingly, carriers of inosine triphosphatase IVS2+21A→C variants among ALL and IBD patients had significantly higher concentrations of the active cytotoxic metabolites, 6-TGNs (P = 0.008 in ALL patients, P = 0.047 in IBD patients). The study confirmed the association of thiopurine methyltransferase heterozygosity with leucopenia and neutropenia in ALL patients and reported a significant association between inosine triphosphatase IVS2+21A→C variants with thrombocytopenia (P = 0.012). CONCLUSIONS - Pharmacogenetic polymorphisms in the 6-MP pathway may help identify patients at risk for associated toxicities and may serve as a guide for dose individualization.