Predictive Value of CYP3A and ABCB1 Phenotyping Probes for the Pharmacokinetics of Sunitinib: the ClearSun Study

Kloth, Jacqueline S. L.; Klümpen, Heinz Josef; Yu, Huixin; Eechoute, Karel; Samer, Caroline Flora; Kam, Boen L.R.; Huitema, Alwin D. R.; Daali, Youssef; Zwinderman, Aeilko H.; Balakrishnar, Bavanthi; Bennink, Roelof J.; Wong, Mark; Schellens, Jan H.M.; Mathijssen, Ron H.J.

doi:10.1007/s40262-013-0111-4

Cited by 24 publications

(24 citation statements)

References 25 publications

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“…No statistically significant differences were observed in the PK parameter estimates of sorafenib ( Figure d ). Although the mean ratio between 1’‐hydroxymidazolam and midazolam, used as a measure of CYP3A4 activity changes, was ∼2 times higher after rifampin intake ( P = 0.005; Figure e ), the extent of sorafenib‐N‐oxide formation was not different between treatment cycles ( Figure f ). During the course of the clinical study, no toxicities were observed that could be attributed to rifampin or midazolam.…”

Section: Resultsmentioning

confidence: 99%

Influence of OATP1B1 Function on the Disposition of Sorafenib‐β‐D‐Glucuronide

Bins

Doorn

Phelps

et al. 2017

Clinical Translational Sci

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The oral multikinase inhibitor sorafenib undergoes extensive UGT1A9‐mediated formation of sorafenib‐β‐D‐glucuronide (SG). Using transporter‐deficient mouse models, it was previously established that SG can be extruded into bile by ABCC2 or follow a liver‐to‐blood shuttling loop via ABCC3‐mediated efflux into the systemic circulation, and subsequent uptake in neighboring hepatocytes by OATP1B‐type transporters. Here we evaluated the possibility that this unusual process, called hepatocyte hopping, is also operational in humans and can be modulated through pharmacological inhibition. We found that SG transport by OATP1B1 or murine Oatp1b2 was effectively inhibited by rifampin, and that this agent can significantly increase plasma levels of SG in wildtype mice, but not in Oatp1b2‐deficient animals. In human subjects receiving sorafenib, rifampin acutely increased the systemic exposure to SG. Our study emphasizes the need to consider hepatic handling of xenobiotic glucuronides in the design of drug–drug interaction studies of agents that undergo extensive phase II conjugation.

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Section: Resultsmentioning

confidence: 99%

Influence of OATP1B1 Function on the Disposition of Sorafenib‐β‐D‐Glucuronide

Bins

Doorn

Phelps

et al. 2017

Clinical Translational Sci

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“…For sunitinib, several population PK models have been developed . The PK of sunitinib and SU012662 is described as a one‐ or two‐compartment model with first‐order absorption and elimination.…”

Section: Methodsmentioning

confidence: 99%

“…For sunitinib, several population PK models have been developed. 96,[113][114][115][116][117][118][119][120] The PK of sunitinib and SU012662 is described as a one-or two-compartment model with first-order absorption and elimination. Some covariates might explain part of the interpatient PK variability.…”

Section: Factors Identified In Pharmacokinetic Models That Explain Inmentioning

confidence: 99%

Imatinib, sunitinib and pazopanib: From flat‐fixed dosing towards a pharmacokinetically guided personalized dose

Westerdijk¹,

Desar²,

Steeghs

et al. 2020

Brit J Clinical Pharma

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Tyrosine kinase inhibitors (TKIs) are anti‐cancer drugs that target tyrosine kinases, enzymes that are involved in multiple cellular processes. Currently, multiple oral TKIs have been introduced in the treatment of solid tumours, all administered in a fixed dose, although large interpatient pharmacokinetic (PK) variability is described. For imatinib, sunitinib and pazopanib exposure‐treatment outcome (efficacy and toxicity) relationships have been established and therapeutic windows have been defined, therefore dose optimization based on the measured blood concentration, called therapeutic drug monitoring (TDM), can be valuable in increasing efficacy and reducing the toxicity of these drugs. In this review, an overview of the current knowledge on TDM guided individualized dosing of imatinib, sunitinib and pazopanib for the treatment of solid tumours is presented. We summarize preclinical and clinical data that have defined thresholds for efficacy and toxicity. Furthermore, PK models and factors that influence the PK of these drugs which partly explain the interpatient PK variability are summarized. Finally, pharmacological interventions that have been performed to optimize plasma concentrations are described. Based on current literature, we advise which methods should be used to optimize exposure to imatinib, sunitinib and pazopanib.

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“…Three PK models for sunitinib and SU12662 have been described thus far . Houk's model, albeit based on a rich dataset (590 subjects), disregarded irrationally the correlation between the parent and metabolite compounds which share similar PK pathways .…”

Section: Introductionmentioning

confidence: 99%

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

Steeghs

Kloth

et al. 2015

Brit J Clinical Pharma

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AIMSPreviously 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. METHODSOne 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. RESULTSSunitinib 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. CONCLUSIONSA 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. WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• The necessity and evidence of therapeutic drug monitoring has been reported for sunitinib and its active metabolite SU12662.• Pharmacokinetic modelling has proved its utility in therapeutic drug monitoring.• Limitations existed for a previous published pharmacokinetic model for sunitinib and SU12662 in the application of therapeutic drug monitoring. WHAT THIS STUDY ADDS• We presented a superior pharmacokinetic model for sunitinib and SU12662 including pre-systemic metabolism.• The simulation of the current model obtained the correct concentration range for sunitinib and SU12662.• The model predicted correctly the ratio of patients who did not reach proposed PK targets for efficacy in clinic observations.

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Predictive Value of CYP3A and ABCB1 Phenotyping Probes for the Pharmacokinetics of Sunitinib: the ClearSun Study

Abstract: 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.

Cited by 24 publications

References 25 publications

Influence of OATP1B1 Function on the Disposition of Sorafenib‐β‐D‐Glucuronide

Influence of OATP1B1 Function on the Disposition of Sorafenib‐β‐D‐Glucuronide

Imatinib, sunitinib and pazopanib: From flat‐fixed dosing towards a pharmacokinetically guided personalized dose

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

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