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... Show moreImatinib 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. Show less
Kloth, J.S.L.; Verboom, M.C.; Swen, J.J.; Straaten, T. van der; Sleijfer, S.; Reyners, A.K.L.; ... ; Mathijssen, R.H.J. 2018
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... Show moreAIMS: 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. Show less
