Oral administration of docetaxel is an attractive alternative for conventional intravenous (IV) administration. The low bioavailability of docetaxel, however, hinders the application of oral... Show moreOral administration of docetaxel is an attractive alternative for conventional intravenous (IV) administration. The low bioavailability of docetaxel, however, hinders the application of oral docetaxel in the clinic. The aim of the current study was to develop a population pharmacokinetic (PK) model for docetaxel and ritonavir based on the phase 1 studies and to support drug development of this combination treatment. PK data were collected from 191 patients who received IV docetaxel and different oral docetaxel formulations (drinking solution, ModraDoc001 capsule, and ModraDoc006 tablet) coadministered with ritonavir. A PK model was first developed for ritonavir. Subsequently, a semiphysiological PK model was developed for docetaxel, which incorporated the inhibition of docetaxel metabolism by ritonavir. The uninhibited intrinsic clearance of docetaxel was estimated based on data on IV docetaxel as 1980 L/h (relative standard error, 11%). Ritonavir coadministration extensively inhibited the hepatic metabolism of docetaxel to 9.3%, which resulted in up to 12-fold higher docetaxel plasma concentrations compared to oral docetaxel coadministered without ritonavir. In conclusion, a semiphysiological PK model for docetaxel and ritonavir was successfully developed. Coadministration of ritonavir resulted in increased plasma concentrations of docetaxel after administration of the oral formulations of ModraDoc. Furthermore, the oral ModraDoc formulations showed lower variability in plasma concentrations between and within patients compared to the drinking solution. Comparable exposure could be reached with the oral ModraDoc formulations compared to IV administration. Show less
Velden, D.L. van der; Hoes, L.R.; Wijngaart, H. van der; Henegouwen, J.M.V.; Werkhoven, E. van; Roepman, P.; ... ; Voest, E.E. 2019
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
Doorn-Khosrovani, S.B.V. van; Pisters-van Roy, A.; Saase, L. van; Graaff, M. van der; Gijzen, J.; Sleijfer, S.; ... ; Voest, E.E. 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
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
