Jacqueline S. L. Kloth

Association Analysis of Genetic Polymorphisms in Genes Related to Sunitinib Pharmacokinetics, Specifically Clearance of Sunitinib and SU12662

Interpatient variability in the pharmacokinetics (PK) of sunitinib is high. Single nucleotide polymorphisms (SNPs) in PK candidate genes have been associated with the efficacy and toxicity of sunitinib, but whether these SNPs truly affect the PK of sunitinib remains to be elucidated. This multicenter study involving 114 patients investigated whether these SNPs and haplotypes in genes encoding metabolizing enzymes or efflux transporters are associated with the clearance of sunitinib and its active metabolite SU12662. SNPs were tested as covariates in a population PK model. From univariate analysis, we found that the SNPs in CYP3A4, CYP3A5, and ABCB1 were associated with the clearance of both sunitinib and SU12662. In multivariate analysis, CYP3A4*22 was found to be eliminated last with an effect size of −22.5% on clearance. Observed effect sizes are below the interindividual variability in clearance and are therefore too limited to directly guide individual dosing of sunitinib.

Pharmacokinetically guided sunitinib dosing: a feasibility study in patients with advanced solid tumours

Background:Plasma exposure of sunitinib shows large inter-individual variation. Therefore, a pharmacokinetic (PK) study was performed to determine safety and feasibility of sunitinib dosing based on PK levels.Methods:Patients were treated with sunitinib 37.5 mg once daily. At days 15 and 29 of treatment, plasma trough levels of sunitinib and N-desethyl sunitinib were measured. If the total trough level (TTL) was <50 ng ml−1 and the patient did not show any grade ⩾3 toxicity, the daily sunitinib dose was increased by 12.5 mg. If the patient suffered from grade ⩾3 toxicity, the sunitinib dose was lowered by 12.5 mg.Results:Twenty-nine out of 43 patients were evaluable for PK assessments. Grade ⩾3 adverse events were experienced in seven patients (24%) at the starting dose and in nine patients (31%) after dose escalation. TTLs were below target in 15 patients (52%) at the starting dose. Of these, five patients (17%) reached target TTL after dose escalation without additional toxicity.Conclusions:In a third of the patients that were below target TTL at standard dose, the sunitinib dose could be increased without additional toxicities. This could be the basis for future studies and the implementation of a PK-guided dosing strategy in clinical practice.

Incidence and relevance of QTc-interval prolongation caused by tyrosine kinase inhibitors.

Background:Tyrosine kinase inhibitors (TKIs) are associated with prolongation of the QTc interval on the electrocardiogram (ECG). The QTc-interval prolongation increases the risk of life-threatening arrhythmias. However, studies evaluating the effects of TKIs on QTc intervals are limited and only consist of small patient numbers.Methods:In this multicentre trial in four centres in the Netherlands and Italy we screened all patients who were treated with any TKI. To evaluate the effects of TKIs on the QTc interval, we investigated ECGs before and during treatment with erlotinib, gefitinib, imatinib, lapatinib, pazopanib, sorafenib, sunitinib, or vemurafenib.Results:A total of 363 patients were eligible for the analyses. At baseline measurement, QTc intervals were significantly longer in females than in males (QTcfemales=404 ms vs QTcmales=399 ms, P=0.027). A statistically significant increase was observed for the individual TKIs sunitinib, vemurafenib, sorafenib, imatinib, and erlotinib, after the start of treatment (median ΔQTc ranging from +7 to +24 ms, P<0.004). The CTCAE grade for QTc intervals significantly increased after start of treatment (P=0.0003). Especially patients who are treated with vemurafenib are at increased risk of developing a QTc of ⩾470 ms, a threshold associated with an increased risk for arrhythmias.Conclusions:These observations show that most TKIs significantly increase the QTc interval. Particularly in vemurafenib-treated patients, the incidence of patients at risk for arrhythmias is increased. Therefore, especially in case of combined risk factors, ECG monitoring in patients treated with TKIs is strongly recommended.

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.

Genetic polymorphisms as predictive biomarker of survival in patients with gastrointestinal stromal tumors treated with sunitinib.

This study aimed to identify single-nucleotide polymorphisms (SNPs) that are associated with outcome to treatment with sunitinib in patients with advanced gastrointestinal stromal tumors (GIST). Forty-nine SNPS involved in the pharmacokinetic and pharmacodynamic pathway of sunitinib were associated with progression-free survival (PFS) and overall survival (OS) in 127 patients with advanced GIST who have been treated with sunitinib. PFS was significantly longer in carriers of the TT genotype in POR rs1056878 (hazards ratio (HR) 4.310, 95% confidence interval (CI):1.457–12.746, P=0.008). The presence of the T-allele in SLCO1B3 rs4149117 (HR 2.024, 95% CI:1.013–4.044, P=0.046), the CCC-CCC alleles in SLC22A5 haplotype (HR 2.603, 95% CI: 1.216–5.573, P=0.014), and the GC-GC alleles in the IL4 R haplotype (HR 7.131, 95% CI:1.518–33.496, P=0.013) were predictive for OS. This shows that polymorphisms in the pharmacokinetic and pharmacodynamic pathways of sunitinib are associated with survival in GIST. This may help to identify patients that benefit more from treatment with sunitinib.