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      Implementation of a Physiologically Based Pharmacokinetic Modeling Approach to Guide Optimal Dosing Regimens for Imatinib and Potential Drug Interactions in Paediatrics

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          Abstract

          Long-term use of imatinib is effective and well-tolerated in children with chronic myeloid leukaemia (CML) yet defining an optimal dosing regimen for imatinib in younger patients is a challenge. The potential interactions between imatinib and coadministered drugs in this “special” population also remains largely unexplored. This study implements a physiologically based pharmacokinetic (PBPK) modeling approach to investigate optimal dosing regimens and potential drug interactions with imatinib in the paediatric population. A PBPK model for imatinib was developed in the Simcyp Simulator (version 17) utilizing in silico, in vitro drug metabolism, and in vivo pharmacokinetic data and verified using an independent set of published clinical pharmacokinetic data. The model was then extrapolated to children and adolescents (aged 2–18 years) by incorporating developmental changes in organ size and maturation of drug-metabolising enzymes and plasma protein responsible for imatinib disposition. The PBPK model described imatinib pharmacokinetics in adult and paediatric populations and predicted drug interaction with carbamazepine, a cytochrome P450 (CYP)3A4 and 2C8 inducer, with a good accuracy (evaluated by visual inspections of the simulation results and predicted pharmacokinetic parameters that were within 1.25-fold of the clinically observed values). The PBPK simulation suggests that the optimal dosing regimen range for imatinib is 230–340 mg/m 2/d in paediatrics, which is supported by the recommended initial dose for treatment of childhood CML. The simulations also highlighted that children and adults being treated with imatinib have similar vulnerability to CYP modulations. A PBPK model for imatinib was successfully developed with an excellent performance in predicting imatinib pharmacokinetics across age groups. This PBPK model is beneficial to guide optimal dosing regimens for imatinib and predict drug interactions with CYP modulators in the paediatric population.

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          Impact of OATP transporters on pharmacokinetics.

          Membrane transporters are now recognized as important determinants of the transmembrane passage of drugs. Organic anion transporting polypeptides (OATP) form a family of influx transporters expressed in various tissues important for pharmacokinetics. Of the 11 human OATP transporters, OATP1B1, OATP1B3 and OATP2B1 are expressed on the sinusoidal membrane of hepatocytes and can facilitate the liver uptake of their substrate drugs. OATP1A2 is expressed on the luminal membrane of small intestinal enterocytes and at the blood-brain barrier, potentially mediating drug transport at these sites. Several clinically used drugs have been identified as substrates of OATP transporters (e.g. many statins are substrates of OATP1B1). Some drugs may inhibit OATP transporters (e.g. cyclosporine) causing pharmacokinetic drug-drug interactions. Moreover, genetic variability in genes encoding OATP transporters can result in marked inter-individual differences in pharmacokinetics. For example, a single nucleotide polymorphism (c.521T > C, p.Val174Ala) in the SLCO1B1 gene encoding OATP1B1 decreases the ability of OATP1B1 to transport active simvastatin acid from portal circulation into the liver, resulting in markedly increased plasma concentrations of simvastatin acid and an enhanced risk of simvastatin-induced myopathy. SLCO1B1 polymorphism also affects the pharmacokinetics of many other, but not all (fluvastatin), statins and that of the antidiabetic drug repaglinide, the antihistamine fexofenadine and the endothelin A receptor antagonist atrasentan. This review compiles the current knowledge about the expression and function of human OATP transporters, their substrate and inhibitor specificities, as well as pharmacogenetics.
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            Imatinib pharmacokinetics and its correlation with response and safety in chronic-phase chronic myeloid leukemia: a subanalysis of the IRIS study.

            Imatinib at 400 mg daily is standard treatment for chronic myeloid leukemia in chronic phase. We here describe the correlation of imatinib trough plasma concentrations (C(mins)) with clinical responses, event-free survival (EFS), and adverse events (AEs). Trough level plasma samples were obtained on day 29 (steady state, n = 351). Plasma concentrations of imatinib and its metabolite CGP74588 were determined by liquid chromatography/mass spectrometry. The overall mean (+/- SD, CV%) steady-state C(min) for imatinib and CGP74588 were 979 ng/mL (+/- 530 ng/mL, 54.1%) and 242 ng/mL (+/- 106 ng/mL, 43.6%), respectively. Cumulative estimated complete cytogenetic response (CCyR) and major molecular response (MMR) rates differed among the quartiles of imatinib trough levels (P = .01 for CCyR, P = .02 for MMR). C(min) of imatinib was significantly higher in patients who achieved CCyR (1009 +/- 544 ng/mL vs 812 +/- 409 ng/mL, P = .01). Patients with high imatinib exposure had better rates of CCyR and MMR and EFS. An exploratory analysis demonstrated that imatinib trough levels were predictive of higher CCyR independently of Sokal risk group. AE rates were similar among the imatinib quartile categories except fluid retention, rash, myalgia, and anemia, which were more common at higher imatinib concentrations. These results suggest that an adequate plasma concentration of imatinib is important for a good clinical response. This study is registered at http://clinicaltrials.gov as NCT00333840.
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              Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model

              The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment.
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                Author and article information

                Contributors
                Journal
                Front Pharmacol
                Front Pharmacol
                Front. Pharmacol.
                Frontiers in Pharmacology
                Frontiers Media S.A.
                1663-9812
                30 January 2020
                2019
                : 10
                : 1672
                Affiliations
                [1] 1 Sydney Pharmacy School, The University of Sydney , Sydney, NSW, Australia
                [2] 2 School of Pharmacy and Medical Sciences, University of South Australia , Adelaide, SA, Australia
                [3] 3 University of South Australia Cancer Research Institute, University of South Australia , Adelaide, SA, Australia
                Author notes

                Edited by: Rob ter Heine, Radboud University Nijmegen Medical Centre, Netherlands

                Reviewed by: Geoffrey Thomas Tucker, University of Sheffield, United Kingdom; Muhammad Usman, University of Veterinary and Animal Sciences, Pakistan

                *Correspondence: Andrew J. McLachlan, andrew.mclachlan@ 123456sydney.edu.au

                This article was submitted to Pharmaceutical Medicine and Outcomes Research, a section of the journal Frontiers in Pharmacology

                Article
                10.3389/fphar.2019.01672
                7002565
                32082165
                69fbd08f-87ab-4907-b2e6-1eec47fb8dda
                Copyright © 2020 Adiwidjaja, Boddy and McLachlan

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 17 October 2019
                : 23 December 2019
                Page count
                Figures: 7, Tables: 4, Equations: 6, References: 114, Pages: 18, Words: 9165
                Categories
                Pharmacology
                Original Research

                Pharmacology & Pharmaceutical medicine
                imatinib,physiologically based pharmacokinetic (pbpk),simulation,paediatrics,drug interactions

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