Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Cancer Chemotherapy and Pharmacology
Published in: Cancer Chemotherapy and Pharmacology 3/2022

Open Access 01-03-2022 | Pravastatin | Original Article

Prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling

Authors: Ming Chang, Sai Bathena, Lisa J. Christopher, Hong Shen, Amit Roy

Published in: Cancer Chemotherapy and Pharmacology | Issue 3/2022

Login to get access

Abstract

Purpose

Recent in vitro studies demonstrated that dasatinib inhibits organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATEs), and organic anion transporting polypeptide 1B1/1B3 (OATP1B1/1B3). We developed a physiologically based pharmacokinetic (PBPK) model to assess drug–drug interaction (DDI) potential between dasatinib and known substrates for these transporters in a virtual population.

Methods

The dasatinib PBPK model was constructed using Simcyp® Simulator by combining its physicochemical properties, in vitro data, in silico predictions, and pharmacokinetic (PK) results from clinical studies. Model validation against three independent clinical trials not used for model development included dasatinib DDI studies with ketoconazole, rifampin, and simvastatin. The validated model was used to simulate DDIs of dasatinib and known substrates for OCT2 and MATEs (metformin) and OATP1B1/1B3 (pravastatin and rosuvastatin).

Results

Simulations of metformin PK in the presence and absence of dasatinib, using inhibitor constant (Ki) values measured in vitro, produced estimated geometric mean ratios (GMRs) of the maximum observed concentration (Cmax) and area under the concentration–time curve (AUC) of 1.05 and 1.06, respectively. Sensitivity analysis showed metformin exposure increased < 30% in both AUC and Cmax when dasatinib Ki was reduced by tenfold for OCT2 and MATEs simultaneously, and < 40% with a 20-fold Ki reduction. The estimated GMRs of Cmax and AUC for pravastatin and rosuvastatin with co-administration of dasatinib were unity (1.00).

Conclusions

This PBPK model accurately described the observed PK profiles of dasatinib. The validated PBPK model predicts low risk of clinically significant DDIs between dasatinib and metformin, pravastatin, or rosuvastatin.
Appendix
Available only for authorised users
Literature
1.
2.
Cortes JE et al (2016) Final 5-year study results of DASISION: the dasatinib versus imatinib study in treatment-naïve chronic myeloid leukemia patients trial. J Clin Oncol 34(20):2333–2340CrossRef
3.
Christopher LJ et al (2008) Metabolism and disposition of dasatinib after oral administration to humans. Drug Metab Dispos 36(7):1357–1364CrossRef
4.
Wang L et al (2008) Identification of the human enzymes involved in the oxidative metabolism of dasatinib: an effective approach for determining metabolite formation kinetics. Drug Metab Dispos 36(9):1828–1839CrossRef
5.
Duckett DR, Cameron MD (2010) Metabolism considerations for kinase inhibitors in cancer treatment. Expert Opin Drug Metab Toxicol 6(10):1175–1193CrossRef
6.
Mikus G, Isabelle Foerster K (2017) Role of CYP3A4 in kinase inhibitor metabolism and assessment of CYP3A4 activity. Transl Cancer Res 6:S1592–S1599CrossRef
7.
Kamath AV et al (2008) Preclinical pharmacokinetics and in vitro metabolism of dasatinib (BMS-354825): a potent oral multi-targeted kinase inhibitor against SRC and BCR-ABL. Cancer Chemother Pharmacol 61(3):365–376CrossRef
8.
Giacomini KM et al (2010) Membrane transporters in drug development. Nat Rev Drug Discov 9(3):215–236CrossRef
9.
Hillgren KM et al (2013) Emerging transporters of clinical importance: an update from the International transporter consortium. Clin Pharmacol Ther 94(1):52–63CrossRef
10.
Chu X et al (2018) Clinical probes and endogenous biomarkers as substrates for transporter drug-drug interaction evaluation: perspectives from the international transporter consortium. Clin Pharmacol Ther 104(5):836–864CrossRef
11.
Sager JE et al (2015) Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metab Dispos 43(11):1823–1837CrossRef
12.
Shardlow CE et al (2013) Impact of physiologically based pharmacokinetic modeling and simulation in drug development. Drug Metab Dispos 41(12):1994–2003CrossRef
13.
Wu F, Krishna G, Surapaneni S (2020) Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib. Cancer Chemother Pharmacol 86(4):461–473CrossRef
14.
Pahwa S et al (2017) Pretreatment with rifampicin and tyrosine kinase inhibitor dasatinib potentiates the inhibitory effects toward OATP1B1- and OATP1B3-mediated transport. J Pharm Sci 106(8):2123–2135CrossRef
15.
Amidon GL et al (1995) A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res 12(3):413–420CrossRef
16.
17.
Prediction of fa, ka and Fg and their inter-individual variability. Simcyp Workshop 2019. Washington, DC
18.
Pelis RM, Wright SH (2011) Renal transport of organic anions and cations. Compr Physiol 1(4):1795–1835CrossRef
19.
Taskar KS et al (2020) Physiologically-based pharmacokinetic models for evaluating membrane transporter mediated drug-drug interactions: current capabilities, case studies, future opportunities, and recommendations. Clin Pharmacol Ther 107(5):1082–1115CrossRef
20.
Burt HJ et al (2016) Metformin and cimetidine: physiologically based pharmacokinetic modelling to investigate transporter mediated drug-drug interactions. Eur J Pharm Sci 88:70–82CrossRef
21.
Ito S et al (2012) Competitive inhibition of the luminal efflux by multidrug and toxin extrusions, but not basolateral uptake by organic cation transporter 2, is the likely mechanism underlying the pharmacokinetic drug-drug interactions caused by cimetidine in the kidney. J Pharmacol Exp Ther 340(2):393–403CrossRef
22.
Tsuda M et al (2009) Involvement of human multidrug and toxin extrusion 1 in the drug interaction between cimetidine and metformin in renal epithelial cells. J Pharmacol Exp Ther 329(1):185–191CrossRef
23.
Somogyi A, Gugler R (1983) Clinical pharmacokinetics of cimetidine. Clin Pharmacokinet 8(6):463–495CrossRef
24.
Vaidyanathan J et al (2016) Comparing various in vitro prediction criteria to assess the potential of a new molecular entity to inhibit organic anion transporting polypeptide 1B1. J Clin Pharmacol 56(Suppl 7):S59-72CrossRef
25.
Nishiyama K et al (2019) Physiologically-based pharmacokinetic modeling analysis for quantitative prediction of renal transporter-mediated interactions between metformin and cimetidine. CPT Pharmacometrics Syst Pharmacol 8(6):396–406CrossRef
26.
Zack J et al (2015) Pharmacokinetic drug-drug interaction study of ranolazine and metformin in subjects with type 2 diabetes mellitus. Clin Pharmacol Drug Dev 4(2):121–129CrossRef
27.
Haouala A et al (2011) Drug interactions with the tyrosine kinase inhibitors imatinib, dasatinib, and nilotinib. Blood 117(8):e75-87CrossRef
28.
Mills JG et al (1997) The safety of ranitidine in over a decade of use. Aliment Pharmacol Ther 11(1):129–137CrossRef
29.
Metadata
Title
Prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling
Authors
Ming Chang
Sai Bathena
Lisa J. Christopher
Hong Shen
Amit Roy
Publication date
01-03-2022
Publisher
Springer Berlin Heidelberg
Published in
Cancer Chemotherapy and Pharmacology / Issue 3/2022
Print ISSN: 0344-5704
Electronic ISSN: 1432-0843
DOI
https://doi.org/10.1007/s00280-021-04394-z

Other articles of this Issue 3/2022

Cancer Chemotherapy and Pharmacology 3/2022 Go to the issue

Original Article

Differential tumor inhibitory effects induced by HER3 extracellular subdomain-specific mouse monoclonal antibodies

Original Article

Efficacy and safety exposure–response analyses of entrectinib in patients with advanced or metastatic solid tumors

Original Article

Cabozantinib with or without Panitumumab for RAS wild-type metastatic colorectal cancer: impact of MET amplification on clinical outcomes and circulating biomarkers

Original Article

Pharmacokinetics and pharmacodynamics of MEDI0680, a fully human anti-PD-1 monoclonal antibody, in patients with advanced malignancies

Original Article

Isoprenylcysteine carboxyl methyltransferase is critical for glioblastoma growth and survival by activating Ras/Raf/Mek/Erk

Original Article

Evaluation of cytogenetic and molecular markers with MTX-mediated toxicity in pediatric acute lymphoblastic leukemia patients
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits