Literature DB >> 34073681

Clinical-Based vs. Model-Based Adaptive Dosing Strategy: Retrospective Comparison in Real-World mRCC Patients Treated with Sunitinib.

Florent Ferrer1,2, Jonathan Chauvin3, Bénédicte DeVictor2, Bruno Lacarelle1,2, Jean-Laurent Deville4, Joseph Ciccolini1,2.   

Abstract

Different target exposures with sunitinib have been proposed in metastatic renal cell carcinoma (mRCC) patients, such as trough concentrations or AUCs. However, most of the time, rather than therapeutic drug monitoring (TDM), clinical evidence is preferred to tailor dosing, i.e., by reducing the dose when treatment-related toxicities show, or increasing dosing if no signs of efficacy are observed. Here, we compared such empirical dose adjustment of sunitinib in mRCC patients, with the parallel dosing proposals of a PK/PD model with TDM support. In 31 evaluable patients treated with sunitinib, 53.8% had an empirical change in dosing after treatment started (i.e., 46.2% decrease in dosing, 7.6% increase in dosing). Clinical benefit was observed in 54.1% patients, including 8.3% with complete response. Overall, 58.1% of patients experienced treatment discontinuation eventually, either because of toxicities or progressive disease. When choosing 50-100 ng/mL trough concentrations as a target exposure (i.e., sunitinib + active metabolite N-desethyl sunitinib), 45% patients were adequately exposed. When considering 1200-2150 ng/mL.h as a target AUC (i.e., sunitinib + active metabolite N-desethyl sunitinib), only 26% patients were in the desired therapeutic window. TDM with retrospective PK/PD modeling would have suggested decreasing sunitinib dosing in a much larger number of patients as compared with empirical dose adjustment. Indeed, when using target trough concentrations, the model proposed reducing dosing for 61% patients, and up to 84% patients based upon target AUC. Conversely, the model proposed increasing dosing in 9.7% of patients when using target trough concentrations and in 6.5% patients when using target AUC. Overall, TDM with adaptive dosing would have led to tailoring sunitinib dosing in a larger number of patients (i.e., 53.8% vs. 71-91%, depending on the chosen metrics for target exposure) than a clinical-based decision. Interestingly, sunitinib dosing was empirically reduced in 41% patients who displayed early-onset severe toxicities, whereas model-based recommendations would have immediately proposed to reduce dosing in more than 80% of those patients. This observation suggests that early treatment-related toxicities could have been partly avoided using prospective PK/PD modeling with adaptive dosing. Conversely, the possible impact of model-based adapted dosing on efficacy could not be fully evaluated because no clear relationship was found between baseline exposure levels and sunitinib efficacy measured at 3 months.

Entities:  

Keywords:  PK/PD modeling; model-based adaptive dosing; oncology; oral targeted therapy; pharmacokinetics; precision medicine; sunitinib; therapeutic drug monitoring

Year:  2021        PMID: 34073681     DOI: 10.3390/ph14060494

Source DB:  PubMed          Journal:  Pharmaceuticals (Basel)        ISSN: 1424-8247


  22 in total

Review 1.  Individualized dosing of oral targeted therapies in oncology is crucial in the era of precision medicine.

Authors:  Stefanie L Groenland; Ron H J Mathijssen; Jos H Beijnen; Alwin D R Huitema; Neeltje Steeghs
Journal:  Eur J Clin Pharmacol       Date:  2019-06-07       Impact factor: 2.953

2.  Safety, pharmacokinetic, and antitumor activity of SU11248, a novel oral multitarget tyrosine kinase inhibitor, in patients with cancer.

Authors:  Sandrine Faivre; Catherine Delbaldo; Karina Vera; Caroline Robert; Stéphanie Lozahic; Nathalie Lassau; Carlo Bello; Samuel Deprimo; Nicoletta Brega; Giorgio Massimini; Jean-Pierre Armand; Paul Scigalla; Eric Raymond
Journal:  J Clin Oncol       Date:  2005-11-28       Impact factor: 44.544

3.  In vivo antitumor activity of SU11248, a novel tyrosine kinase inhibitor targeting vascular endothelial growth factor and platelet-derived growth factor receptors: determination of a pharmacokinetic/pharmacodynamic relationship.

Authors:  Dirk B Mendel; A Douglas Laird; Xiaohua Xin; Sharianne G Louie; James G Christensen; Guangmin Li; Randall E Schreck; Tinya J Abrams; Theresa J Ngai; Leslie B Lee; Lesley J Murray; Jeremy Carver; Emily Chan; Katherine G Moss; Joshua O Haznedar; Juthamas Sukbuntherng; Robert A Blake; Li Sun; Cho Tang; Todd Miller; Sheri Shirazian; Gerald McMahon; Julie M Cherrington
Journal:  Clin Cancer Res       Date:  2003-01       Impact factor: 12.531

4.  Pazopanib versus sunitinib in metastatic renal-cell carcinoma.

Authors:  Robert J Motzer; Thomas E Hutson; David Cella; James Reeves; Robert Hawkins; Jun Guo; Paul Nathan; Michael Staehler; Paul de Souza; Jaime R Merchan; Ekaterini Boleti; Kate Fife; Jie Jin; Robert Jones; Hirotsugu Uemura; Ugo De Giorgi; Ulrika Harmenberg; Jinwan Wang; Cora N Sternberg; Keith Deen; Lauren McCann; Michelle D Hackshaw; Rocco Crescenzo; Lini N Pandite; Toni K Choueiri
Journal:  N Engl J Med       Date:  2013-08-22       Impact factor: 91.245

5.  Therapeutic drug monitoring in cancer--are we missing a trick?

Authors:  Christophe Bardin; Gareth Veal; Angelo Paci; Etienne Chatelut; Alain Astier; Dominique Levêque; Nicolas Widmer; Jos Beijnen
Journal:  Eur J Cancer       Date:  2014-05-27       Impact factor: 9.162

6.  Drug monitoring of sunitinib in patients with advanced solid tumors: a monocentric observational French study.

Authors:  Luc Cabel; Benoit Blanchet; Audrey Thomas-Schoemann; Olivier Huillard; Audrey Bellesoeur; Anatole Cessot; Julie Giroux; Pascaline Boudou-Rouquette; Romain Coriat; Michel Vidal; Nathaniel E B Saidu; Lisa Golmard; Jérome Alexandre; Francois Goldwasser
Journal:  Fundam Clin Pharmacol       Date:  2017-11-10       Impact factor: 2.748

7.  RandomizEd phase II trial of Sunitinib four weeks on and two weeks off versus Two weeks on and One week off in metastatic clear-cell type REnal cell carcinoma: RESTORE trial.

Authors:  J L Lee; M K Kim; I Park; J-H Ahn; D H Lee; H M Ryoo; C Song; B Hong; J H Hong; H Ahn
Journal:  Ann Oncol       Date:  2015-09-07       Impact factor: 32.976

8.  Population Modeling Integrating Pharmacokinetics, Pharmacodynamics, Pharmacogenetics, and Clinical Outcome in Patients With Sunitinib-Treated Cancer.

Authors:  M H Diekstra; A Fritsch; F Kanefendt; J J Swen; Djar Moes; F Sörgel; M Kinzig; C Stelzer; D Schindele; T Gauler; S Hauser; D Houtsma; M Roessler; B Moritz; K Mross; L Bergmann; E Oosterwijk; L A Kiemeney; H J Guchelaar; U Jaehde
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2017-07-13

9.  PK-PD modeling of individual lesion FDG-PET response to predict overall survival in patients with sunitinib-treated gastrointestinal stromal tumor.

Authors:  E Schindler; M A Amantea; M O Karlsson; L E Friberg
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2016-03-16

Review 10.  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

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  1 in total

1.  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

  1 in total

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