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      Examination of Physiologically‐Based Pharmacokinetic Models of Rosuvastatin

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          Abstract

          Physiologically‐based pharmacokinetic (PBPK) modeling is increasingly used to predict drug disposition and drug–drug interactions (DDIs). However, accurately predicting the pharmacokinetics of transporter substrates and transporter‐mediated DDIs (tDDIs) is still challenging. Rosuvastatin is a commonly used substrate probe in DDI risk assessment for new molecular entities (NMEs) that are potential organic anion transporting polypeptide 1B or breast cancer resistance protein transporter inhibitors, and as such, several rosuvastatin PBPK models have been developed to try to predict the clinical DDI and support NME drug labeling. In this review, we examine five representative PBPK rosuvastatin models, discuss common challenges that the models have come across, and note remaining gaps. These shared learnings will help with the continuing efforts of rosuvastatin model validation, provide more information to understand transporter‐mediated drug disposition, and increase confidence in tDDI prediction.

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          Most cited references66

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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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            Drug and bile acid transporters in rosuvastatin hepatic uptake: function, expression, and pharmacogenetics.

            The 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, or statins, target liver HMG-CoA and are of proven benefit in the prevention of coronary heart disease. Rosuvastatin is an effective statin notable for liver selectivity and lack of significant metabolism. We assessed the extent and relevance of hepatic transporters to rosuvastatin uptake. Transporters involved in rosuvastatin uptake were determined through heterologous expression of multiple human and rat uptake transporters. Human organic anion transporting polypeptide (OATP) 1B1 and sodium-dependent taurocholate cotransporting polypeptide (NTCP) allelic variants were also assessed. Expression of OATP and NTCP messenger RNA and protein was determined from a bank of human liver samples. Multiple OATP family members, including 1B1, 1B3, 2B1, and 1A2, were capable of rosuvastatin transport. Naturally occurring polymorphisms in OATP1B1, including *5, *9, *15, and *18, were associated with profound loss of activity toward rosuvastatin. Interestingly, the major human hepatic bile acid uptake transporter NTCP, but not rat Ntcp, also transported rosuvastatin. Human hepatocyte studies suggested that NTCP alone accounted for approximately 35% of rosuvastatin uptake. Remarkably, NTCP*2, a variant known to have a near complete loss of function for bile acids, exhibited a profound gain of function for rosuvastatin. Quantitative messenger RNA analysis revealed marked intersubject variability in expression of OATPs and NTCP. Multiple transporters mediate the overall hepatic uptake of rosuvastatin, and NTCP may be a heretofore unrecognized transporter important to the disposition of rosuvastatin and possibly other drugs/statins in clinical use. Accordingly, transporter expression and polymorphisms may be key determinants of intersubject variability in response to statin therapy in general.
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              Mechanistic approaches to volume of distribution predictions: understanding the processes.

              To use recently developed mechanistic equations to predict tissue-to-plasma water partition coefficients (Kpus), apply these predictions to whole body unbound volume of distribution at steady state (Vu(ss)) determinations, and explain the differences in the extent of drug distribution both within and across the various compound classes. Vu(ss) values were predicted for 92 structurally diverse compounds in rats and 140 in humans by two approaches. The first approach incorporated Kpu values predicted for 13 tissues whereas the second was restricted to muscle. The prediction accuracy was good for both approaches in rats and humans, with 64-78% and 82-92% of the predicted Vu(ss) values agreeing with in vivo data to within factors of +/-2 and 3, respectively. Generic distribution processes were identified as lipid partitioning and dissolution where the former is higher for lipophilic unionised drugs. In addition, electrostatic interactions with acidic phospholipids can predominate for ionised bases when affinities (reflected by binding to constituents within blood) are high. For acidic drugs albumin binding dominates when plasma protein binding is high. This ability to explain drug distribution and link it to physicochemical properties can help guide the compound selection process.
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                Author and article information

                Contributors
                bowman.christine@gene.com
                chen.yuan@gene.com
                Journal
                CPT Pharmacometrics Syst Pharmacol
                CPT Pharmacometrics Syst Pharmacol
                10.1002/(ISSN)2163-8306
                PSP4
                CPT: Pharmacometrics & Systems Pharmacology
                John Wiley and Sons Inc. (Hoboken )
                2163-8306
                15 December 2020
                January 2021
                : 10
                : 1 ( doiID: 10.1002/psp4.v10.1 )
                : 5-17
                Affiliations
                [ 1 ] Department of Drug Metabolism and Pharmacokinetics Genentech, Inc. South San Francisco California USA
                Author notes
                [*] [* ] Correspondence: Yuan Chen ( chen.yuan@ 123456gene.com ) and Christine M. Bowman ( bowman.christine@ 123456gene.com )

                Article
                PSP412571
                10.1002/psp4.12571
                7825190
                33220025
                88de7598-6a43-418c-9337-e65aacd812f2
                © 2020 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 05 August 2020
                : 19 October 2020
                Page count
                Figures: 4, Tables: 4, Pages: 14, Words: 10678
                Categories
                Review
                Review
                Review
                Custom metadata
                2.0
                January 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.6 mode:remove_FC converted:23.01.2021

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