Literature DB >> 24149944

Midazolam as a phenotyping probe to predict sunitinib exposure in patients with cancer.

D de Wit1, H Gelderblom, A Sparreboom, J den Hartigh, M den Hollander, J M C König-Quartel, T Hessing, H J Guchelaar, N P van Erp.   

Abstract

PURPOSE: Patients treated with sunitinib show substantial inter-patient variability in drug exposure (~30-40 %), which is largely unexplained. Since sunitinib is metabolized by cytochrome P450(CYP)3A4, variability in the activity of this enzyme may explain a considerable proportion of this inter-patient variability. Midazolam is widely used as a phenotyping probe to assess CYP3A4-activity. The objective of this study was to prospectively evaluate the relationship between midazolam and sunitinib exposure. Additionally, the correlation between sunitinib trough levels and exposure and the influence of sunitinib on midazolam exposure was determined.
METHODS: Thirteen patients treated with sunitinib in a 4 weeks "on"-2 weeks "off" regimen received twice 7.5 mg midazolam; once with and once without sunitinib. Steady-state sunitinib, its active metabolite SU12662 and midazolam exposures were determined.
RESULTS: A significant correlation between midazolam exposure (AUC(0-7h)) and steady-state sunitinib and sunitinib + SU12662 exposure (AUC(0-24h)) was found (p = 0.006 and p = 0.0018, respectively); midazolam exposure explained 51 and 41 % of the inter-patient variability in sunitinib and sunitinib + SU12622 exposure. Furthermore, C trough was highly correlated (r(2) = 0.94) with sunitinib AUC(0-24h). Sunitinib decreased midazolam exposure with 24 % (p = 0.034).
CONCLUSION: Midazolam exposure is highly correlated with sunitinib exposure and explains a large proportion of the observed inter-patient variability in sunitinib pharmacokinetics. Consequently, midazolam could be used to identify patients that are at risk of under- or overtreatment, respectively, at the start of sunitinib therapy. Moreover, sunitinib and sunitinib + SU12662 trough levels are highly correlated with drug exposure and can thus be used in clinical practice to individualize sunitinib therapy. The decrease in midazolam exposure by sunitinib needs further investigation.

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Year:  2013        PMID: 24149944     DOI: 10.1007/s00280-013-2322-7

Source DB:  PubMed          Journal:  Cancer Chemother Pharmacol        ISSN: 0344-5704            Impact factor:   3.333


  9 in total

1.  Integrated semi-physiological pharmacokinetic model for both sunitinib and its active metabolite SU12662.

Authors:  Huixin Yu; Neeltje Steeghs; Jacqueline S L Kloth; Djoeke de Wit; J G Coen van Hasselt; Nielka P van Erp; Jos H Beijnen; Jan H M Schellens; Ron H J Mathijssen; Alwin D R Huitema
Journal:  Br J Clin Pharmacol       Date:  2015-05       Impact factor: 4.335

2.  Sunitinib treatment in a patient with metastatic renal cell carcinoma and bariatric surgery.

Authors:  Caroline M J van Kinschot; Nielka P van Erp; Tanja Feberwee; Vincent O Dezentjé
Journal:  Eur J Clin Pharmacol       Date:  2015-07-16       Impact factor: 2.953

3.  Use of microdose phenotyping to individualise dosing of patients.

Authors:  Nicolas Hohmann; Walter E Haefeli; Gerd Mikus
Journal:  Clin Pharmacokinet       Date:  2015-09       Impact factor: 6.447

4.  Sunitinib tissue distribution changes after coadministration with ketoconazole in mice.

Authors:  Evelyn Li-Ching Chee; Adeline Yi Ling Lim; Pilar Modamio; Cecilia Fernandez-Lastra; Ignacio Segarra
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2015-02-06       Impact factor: 2.441

5.  Effect of the CYP3A5 and ABCB1 genotype on exposure, clinical response and manifestation of toxicities from sunitinib in Asian patients.

Authors:  Y L Teo; H L Wee; X P Chue; N M Chau; M-H Tan; R Kanesvaran; H L Wee; H K Ho; A Chan
Journal:  Pharmacogenomics J       Date:  2015-03-17       Impact factor: 3.550

6.  Interindividual Variability in Cytochrome P450 3A and 1A Activity Influences Sunitinib Metabolism and Bioactivation.

Authors:  Elizabeth A Burnham; Arsany A Abouda; Jennifer E Bissada; Dasean T Nardone-White; Jessica L Beers; Jonghwa Lee; Matthew J Vergne; Klarissa D Jackson
Journal:  Chem Res Toxicol       Date:  2022-04-28       Impact factor: 3.973

7.  Interindividual Variation in CYP3A Activity Influences Lapatinib Bioactivation.

Authors:  Jennifer E Bissada; Vivian Truong; Arsany A Abouda; Kahari J Wines; Rachel D Crouch; Klarissa D Jackson
Journal:  Drug Metab Dispos       Date:  2019-09-06       Impact factor: 3.922

8.  Towards better dose individualisation: metabolic phenotyping to predict cabazitaxel pharmacokinetics in men with prostate cancer.

Authors:  A Janssen; C P M Verkleij; A van der Vlist; R H J Mathijssen; H J Bloemendal; R Ter Heine
Journal:  Br J Cancer       Date:  2017-04-11       Impact factor: 7.640

Review 9.  Imatinib, sunitinib and pazopanib: From flat-fixed dosing towards a pharmacokinetically guided personalized dose.

Authors:  Kim Westerdijk; Ingrid M E Desar; Neeltje Steeghs; Winette T A van der Graaf; Nielka P van Erp
Journal:  Br J Clin Pharmacol       Date:  2020-01-21       Impact factor: 4.335

  9 in total

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