Genetic polymorphisms in ABCG2 and CYP1A2 are associated with imatinib dose reduction in patients treated for gastrointestinal stromal tumors.

Imatinib has a mild toxicity profile, although severe adverse events may develop. In this pharmacogenetic pathway analysis the need for dose reduction and cessation of therapy was tested for an association with single nucleotide polymorphisms (SNPs) in genes related to imatinib pharmacology. Retrospective data from 315 patients with a gastrointestinal stromal tumor who received imatinib 400 mg o.d. was associated with 36 SNPs. SNPs that showed a trend in univariate testing were tested in a multivariate model with clinical factors and correction for multiple testing was performed. Dose reduction was associated with carriership of the A-allele in rs2231137 in ABCG2 (OR 7.35, p = 0.0002) and two C-alleles in rs762551 in CYP1A2 (OR 7.12, p = 0.001). Results remained significant after correction for multiple testing. Therapy cessation did not show an association with any of the tested SNPs. These results may help identifying patients at increased risk for toxicity who could benefit from intensified follow-up.

Circadian variation in tamoxifen pharmacokinetics in mice and breast cancer patients.

The anti-estrogen tamoxifen is characterized by a large variability in response, partly due to pharmacokinetic differences. We examined circadian variation in tamoxifen pharmacokinetics in mice and breast cancer patients. Pharmacokinetic analysis was performed in mice, dosed at six different times (24-h period). Tissue samples were used for mRNA expression analysis of drug-metabolizing enzymes. In patients, a cross-over study was performed. During three 24-h periods, after tamoxifen dosing at 8 a.m., 1 p.m., and 8 p.m., for at least 4 weeks, blood samples were collected for pharmacokinetic measurements. Differences in tamoxifen pharmacokinetics between administration times were assessed. The mRNA expression of drug-metabolizing enzymes showed circadian variation in mouse tissues. Tamoxifen exposure seemed to be highest after administration at midnight. In humans, marginal differences were observed in pharmacokinetic parameters between morning and evening administration. Tamoxifen C(max )and area under the curve (AUC)0-8 h were 20 % higher (P < 0.001), and tamoxifen t(max) was shorter (2.1 vs. 8.1 h; P = 0.001), indicating variation in absorption. Systemic exposure (AUC0-24 h) to endoxifen was 15 % higher (P < 0.001) following morning administration. The results suggest that dosing time is of marginal influence on tamoxifen pharmacokinetics. Our study was not designed to detect potential changes in clinical outcome or toxicity, based on a difference in the time of administration. Circadian rhythm may be one of the many determinants of the interpatient and intrapatient pharmacokinetic variability of tamoxifen.

Integrated semi-physiological pharmacokinetic model for both sunitinib and its active metabolite SU12662.

AIMS: Previously published pharmacokinetic (PK) models for sunitinib and its active metabolite SU12662 were based on a limited dataset or lacked important elements such as correlations between sunitinib and its metabolite. The current study aimed to develop an improved PK model that circumvented these limitations and to prove the utility of the PK model in treatment optimization in clinical practice. METHODS: One thousand two hundred and five plasma samples from 70 cancer patients were collected from three PK studies with sunitinib and SU12662. A semi-physiological PK model for sunitinib and SU12662 was developed incorporating pre-systemic metabolism using non-linear mixed effects modelling (nonmem). Allometric scaling based on body weight was applied. The final model was used for simulation of the PK of different treatment regimens. RESULTS: Sunitinib and SU12662 PK were best described by a one and two compartment model, respectively. Introduction of pre-systemic formation of SU12662 strongly improved model fit, compared with solely systemic metabolism. The clearance of sunitinib and SU12662 was estimated at 35.7 (relative standard error (RSE) 5.7%) l h(-1) and 17.1 (RSE 7.4%) l h(-1), respectively for 70 kg patients. Correlation coefficients were estimated between inter-individual variability of both clearances, both volumes of distribution and between clearance and volume of distribution of SU12662 as 0.53, 0.48 and 0.45, respectively. Simulation of the PK model predicted correctly the ratio of patients who did not reach proposed PK targets for efficacy. CONCLUSIONS: A semi-physiological PK model for sunitinib and SU12662 in cancer patients was presented including pre-systemic metabolism. The model was superior to previous PK models in many aspects.

Prospective Analysis in GIST Patients on the Role of Alpha-1 Acid Glycoprotein in Imatinib Exposure.

BACKGROUND: For imatinib, a relationship between systemic exposure and clinical outcome has been suggested. Importantly, imatinib concentrations are not stable and decrease over time, for which several mechanisms have been suggested. In this study, we investigated if a decrease in alpha-1 acid glycoprotein (AGP) is the main cause of the lowering in imatinib exposure over time. METHODS: We prospectively measured imatinib trough concentration (C min) values in 28 patients with gastrointestinal stromal tumours, at 1, 3 and 12 months after the start of imatinib treatment. At the same time points, AGP levels were measured. RESULTS: Overall, imatinib C min and AGP levels were correlated (r 2 = 0.656; P < 0.001). However, AGP levels did not fluctuate significantly over time, nor did the change in AGP levels correlate with the change in the imatinib C min. CONCLUSION: We showed that systemic AGP levels are not likely to be a key player in the decrease in systemic imatinib exposure over time. As long as intra-individual changes in imatinib exposure remain unexplained, researchers should standardize the sampling times for imatinib in order to be able to assess the clinical applicability of therapeutic drug monitoring.

Genetic polymorphisms in angiogenesis-related genes are associated with worse progression-free survival of patients with advanced gastrointestinal stromal tumours treated with imatinib.

BACKGROUND: Imatinib 400 mg per day is first-line therapy for patients with gastrointestinal stromal tumours (GISTs). Although clinical benefit is high, progression-free survival (PFS) is variable. This study explores the relationship of single nucleotide polymorphisms (SNPs) in genes related to imatinib pharmacokinetics and pharmacodynamics and PFS in imatinib-treated patients with advanced GIST. METHODS: In 227 patients a pharmacogenetic pathway analysis was performed. Genotype data from 36 SNPs in 18 genes were tested in univariate analyses to investigate their relationship with PFS. Genetic variables which showed a trend (p < 0.1) were tested in a multivariate model, in which each singular SNP was added to clinicopathological factors. RESULTS: In univariate analyses, PFS was associated with synchronous metastases (p = 0.0008) and the mutational status (p = 0.004). Associations with rs1870377 in KDR (additive model, p = 0.0009), rs1570360 in VEGFA (additive model, p = 0.053) and rs4149117 in SLCO1B3 (mutant dominant model, 0.027) were also found. In the multivariate model, significant associations and trends with shorter PFS were found for synchronous metastases (HR 1.94, p = 0.002), KIT exon 9 mutation (HR 2.45, p = 0.002) and the SNPs rs1870377 (AA genotype, HR 2.61, p = 0.015), rs1570360 (AA genotype, HR 2.02, p = 0.037) and rs4149117 (T allele, HR 0.62, p = 0.083). CONCLUSION: In addition to KIT exon 9 mutation and synchronous metastases, SNPs in KDR, VEGFA and SLCO1B3 appear to be associated with PFS in patients with advanced GIST receiving 400-mg imatinib. If validated, specific SNPs may serve as predictive biomarkers to identify patients with an increased risk for progressive disease during imatinib therapy.

Macrocytosis as a potential parameter associated with survival after tyrosine kinase inhibitor treatment.

AIM OF THE STUDY: As a rise in mean corpuscular volume (MCV) of the erythrocyte is frequently seen during treatment with imatinib and sunitinib, we investigated whether macrocytosis (MCV > 100 fl) also occurs as a class effect in other tyrosine kinase inhibitors (TKIs) and whether occurrence of macrocytosis is associated with outcome. MATERIALS AND METHODS: In 533 patients, using 5 TKIs, we investigated if macrocytosis and an increase in MCV were associated with progression-free survival and overall survival (OS) in specific tumour-treatment combinations. RESULTS: Macrocytosis as well as an increase in MCV from baseline of >10 fl (ΔMCV +10 fl), when included as a time-dependent covariate, were associated with improved OS in patients with renal cell cancer (RCC) treated with sunitinib (macrocytosis, hazard ratio [HR] = 0.61, p = 0.031, and ΔMCV +10 fl, HR = 0.58, p = 0.016). CONCLUSION: In sunitinib-treated patients with RCC, the occurrence of macrocytosis, or a substantial increase in MCV levels after start of treatment, could potentially serve as a positive prognostic factor for survival, if validated prospectively.

Relationship Between Sunitinib Pharmacokinetics and Administration Time: Preclinical and Clinical Evidence.

BACKGROUND AND OBJECTIVE: Circadian rhythms may influence the pharmacokinetics of drugs. This study aimed to elucidate whether the pharmacokinetics of the orally administered drug sunitinib are subject to circadian variation. METHODS: We performed studies in male FVB-mice aged 8-12 weeks, treated with single-dose sunitinib at six dosing times. Plasma and tissue samples were obtained for pharmacokinetic analysis and to monitor messenger RNA (mRNA) expression of metabolizing enzymes and drug transporters. A prospective randomized crossover study was performed in which patients took sunitinib once daily at 8 a.m., 1 p.m., and 6 p.m at three subsequent courses. Patients were blindly randomized into two groups, which determined the sequence of the sunitinib dosing time. The primary endpoint in both studies was the difference in plasma area under the concentration-time curve (AUC) of sunitinib and its active metabolite SU12662 between dosing times. RESULTS: Sunitinib and SU12662 plasma AUC in mice followed an ~12-h rhythm as a function of administration time (p ≤ 0.04). The combined AUC from time zero to 10 h (AUC10) was 14-27 % higher when sunitinib was administered at 4 a.m. and 4 p.m. than at 8 a.m. and 8 p.m. Twenty-four-hour rhythms were seen in the mRNA levels of drug transporters and metabolizing enzymes. In 12 patients, sunitinib trough concentrations (C trough) were higher when the drug was taken at 1 p.m. or 6 p.m. than when taken at 8 a.m. (C trough-1 p.m. 66.0 ng/mL; C trough-6 p.m. 58.9 ng/mL; C trough-8 a.m. 50.7 ng/mL; p = 0.006). The AUC was not significantly different between dosing times. CONCLUSIONS: Our results indicate that sunitinib pharmacokinetics follow an ~12-h rhythm in mice. In humans, morning dosing resulted in lower C trough values, probably resulting from differences in elimination. This can have implications for therapeutic drug monitoring.

Predictive value of CYP3A and ABCB1 phenotyping probes for the pharmacokinetics of sunitinib: the ClearSun study.

BACKGROUND AND OBJECTIVE: The wide inter-patient variability in drug exposure partly explains the toxicity and efficacy profile of sunitinib treatment. In this prospective study cytochrome P450 (CYP) 3A and adenosine triphosphate binding cassette (ABC) B1 phenotypes were correlated to the pharmacokinetics of sunitinib and its active metabolite N-desethylsunitinib. METHODS: A correlation analysis was performed between sunitinib pharmacokinetics and 1'OH-midazolam/midazolam ratio and parameters derived from technetium-99m-2-methoxy isobutyl isonitrile ((99m)Tc-MIBI) scans, respectively. A population pharmacokinetic model using non-linear mixed-effects modeling software NONMEM was built, which included the phenotype tests as covariate. RESULTS: In 52 patients, the mean trough concentration of sunitinib plus metabolite increased from 21.4 ng/mL at day 1 of a cycle to 88.1 ng/mL in the fourth week of treatment. A trend for a correlation was observed between (99m)Tc-MIBI elimination constant and trough concentrations of N-desethylsunitinib; however, this was not significant. Correlations were found between 1'OH-midazolam/midazolam ratio and sunitinib clearance (P = 0.008) and day 1 N-desethylsunitinib trough concentrations (P = 0.005), respectively. Moreover, patients suffering from grade 3 toxicities had significant lower clearance of sunitinib than patients without grade 3 toxicities (34.4 vs. 41.4 L/h; P = 0.025). CONCLUSIONS: Phenotype tests for ABCB1 and CYP3A4 did not explain inter-individual variability of sunitinib exposure sufficiently. However, the correlation between sunitinib clearance and the occurrence of severe toxicity suggests a direct exposure-toxicity relationship.