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
Background: Oral anticancer drugs show a high interpatient variability in pharmacokinetics (PK), leading to large differences in drug exposure. For many of these drugs, exposure has been linked to... Show moreBackground: Oral anticancer drugs show a high interpatient variability in pharmacokinetics (PK), leading to large differences in drug exposure. For many of these drugs, exposure has been linked to efficacy and toxicity. Despite this knowledge, these drugs are still administered in a one-size-fits-all approach. Consequently, individual patients have a high probability to be either underdosed, which can lead to decreased antitumor efficacy, or overdosed, which could potentially result in increased toxicity. Therapeutic drug monitoring (TDM), personalized dosing based on measured drug levels, could be used to circumvent underdosing and overdosing and thereby optimize treatment outcomes.Methods: In this prospective clinical study (www.trialregister.nl; NL6695), the feasibility, tolerability, and efficacy of TDM of oral anticancer drugs will be evaluated. In total, at least 600 patients will be included for (at least) 23 different compounds. Patients starting regular treatment with one of these compounds at the approved standard dose can be included. PK sampling will be performed at 4, 8, and 12 weeks after the start of treatment and every 12 weeks thereafter. Drug concentrations will be measured, and trough concentrations (C-min) will be calculated. In cases where C-min, falls below the predefined target and acceptable toxicity, a PK-guided intervention will be recommended. This could include emphasizing compliance, adapting concomitant medication (due to drug-drug interactions), instructing to take the drug concomitant with food, splitting intake moments, or recommending a dose increase.Discussion: Despite a strong rationale for the use of TDM for oral anticancer drugs, this is currently not yet widely adopted in routine patient care. This prospective study will be a valuable contribution to demonstrate the additional value of dose optimization on treatment outcome for these drugs. Show less
Bins, S.; Huitema, A.D.R.; Laven, P.; Bouazzaoui, S. el; Yu, H.X.; Erp, N. van; ... ; Koolen, S.L.W. 2019
Background and ObjectiveAs pazopanib plasma trough concentrations are correlated with treatment outcome, we explored whether single nucleotide polymorphisms in the elimination pathway of pazopanib... Show moreBackground and ObjectiveAs pazopanib plasma trough concentrations are correlated with treatment outcome, we explored whether single nucleotide polymorphisms in the elimination pathway of pazopanib affect systemic pazopanib concentrations.MethodsThe decreased function alleles CYP3A4 15389 C>T (*22), ABCB1 3435 C>T, ABCG2 421 C>A, and ABCG2 34G>A were analyzed within a recently developed population-pharmacokinetic model.ResultsIncorporation of CYP3A4*22 in the model resulted in a 35% lower clearance for variant carriers (0.18 vs. 0.27 L/h; difference in objective function value: -9.7; p<0.005). Simulated median trough concentrations of cancer patients with CYP3A4*22 with 600mg once daily or 800mg once daily were 31 and 35mg/L, respectively. The simulated trough concentrations for the population excluding the CYP3A4*22 carriers after 600mg once daily or 800mg once daily were 18 and 20mg/L, respectively.ConclusionThis analysis shows that CYP3A4*22 heterozygotes have a substantial lower pazopanib clearance and that dose adjustments based on CYP3A4*22 status could be considered. Show less
Purpose: Pazopanib is active in soft-tissue sarcoma (STS). Because pazopanib absorption is pH-dependent, coadministration with gastric acid-suppressive (GAS) agents such as proton pump inhibitors... Show morePurpose: Pazopanib is active in soft-tissue sarcoma (STS). Because pazopanib absorption is pH-dependent, coadministration with gastric acid-suppressive (GAS) agents such as proton pump inhibitors could affect exposure of pazopanib, and thereby its therapeutic effects.Patients and Methods: The EORTC 62043 and 62072 were single-arm phase II and placebo-controlled phase III studies, respectively, of pazopanib in advanced STS. We first compared the outcome of patients treated with pazopanib with or without GAS agents for >= 80% of treatment duration, and subsequently using various thresholds. The impact of concomitant GAS therapy was assessed on progression-free survival (PFS) and overall survival (OS) using multivariate Cox models, exploring and comparing also the potential effect on placebo-treated patients.Results: Of 333 eligible patients, 59 (17.7%) received concomitant GAS therapy for > 80% of pazopanib treatment duration. Median PFS was shorter in GAS therapy users versus nonusers: 2.8 vs. 4.6 months, respectively [HR, 1.49; 95% confidence interval (CI), 1.11-1.99; P = 0.01]. Concomitant administration of GAS therapy was also associated with a shorter median OS: 8.0 vs. 12.6 months (HR, 1.81; 95% CI, 1.31-2.49; P < 0.01). The longer the overlapping use of GAS agents and pazopanib, the worse the outcome with pazopanib. These effects were not observed in placebo-treated patients (HR, 0.82; 95% CI, 0.51-1.34; P = 0.43 for PFS and HR, 0.84; 95% CI, 0.48-1.48; P = 0.54 for OS).Conclusions: Coadministration of long-term GAS therapy with pazopanib was associated with significantly shortened PFS and OS. Withdrawal of GAS agents must be considered whenever possible. Therapeutic drug monitoring of pazopanib plasma concentrations may be helpful for patients on pazopanib and GAS therapy. Show less
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
