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      Quantitative prediction of breast cancer resistant protein mediated drug‐drug interactions using physiologically‐based pharmacokinetic modeling

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          Abstract

          Quantitative assessment of drug‐drug interactions (DDIs) involving breast cancer resistance protein (BCRP) inhibition is challenged by overlapping substrate/inhibitor specificity. This study used physiologically‐based pharmacokinetic (PBPK) modeling to delineate the effects of inhibitor drugs on BCRP‐ and organic anion transporting polypeptide (OATP)1B‐mediated disposition of rosuvastatin, which is a recommended BCRP clinical probe. Initial static model analysis using in vitro inhibition data suggested BCRP/OATP1B DDI risk while considering regulatory cutoff criteria for a majority of inhibitors assessed (25 of 27), which increased rosuvastatin plasma exposure to varying degree (~ 0–600%). However, rosuvastatin area under plasma concentration‐time curve (AUC) was minimally impacted by BCRP inhibitors with calculated G‐value (= gut concentration/inhibition potency) below 100. A comprehensive PBPK model accounting for intestinal (OATP2B1 and BCRP), hepatic (OATP1B, BCRP, and MRP4), and renal (OAT3) transport mechanisms was developed for rosuvastatin. Adopting in vitro inhibition data, rosuvastatin plasma AUC changes were predicted within 25% error for 9 of 12 inhibitors evaluated via PBPK modeling. This study illustrates the adequacy and utility of a mechanistic model‐informed approach in quantitatively assessing BCRP‐mediated DDIs.

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          Most cited references47

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          Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions.

          A key component of whole body physiologically based pharmacokinetic (WBPBPK) models is the tissue-to-plasma water partition coefficients (Kpu's). The predictability of Kpu values using mechanistically derived equations has been investigated for 7 very weak bases, 20 acids, 4 neutral drugs and 8 zwitterions in rat adipose, bone, brain, gut, heart, kidney, liver, lung, muscle, pancreas, skin, spleen and thymus. These equations incorporate expressions for dissolution in tissue water and, partitioning into neutral lipids and neutral phospholipids. Additionally, associations with acidic phospholipids were incorporated for zwitterions with a highly basic functionality, or extracellular proteins for the other compound classes. The affinity for these cellular constituents was determined from blood cell data or plasma protein binding, respectively. These equations assume drugs are passively distributed and that processes are nonsaturating. Resultant Kpu predictions were more accurate when compared to published equations, with 84% as opposed to 61% of the predicted values agreeing with experimental values to within a factor of 3. This improvement was largely due to the incorporation of distribution processes related to drug ionisation, an issue that is not addressed in earlier equations. Such advancements in parameter prediction will assist WBPBPK modelling, where time, cost and labour requirements greatly deter its application. (c) 2006 Wiley-Liss, Inc. and the American Pharmacists Association
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            ABCG2 polymorphism markedly affects the pharmacokinetics of atorvastatin and rosuvastatin.

            The ABCG2 c.421C>A single-nucleotide polymorphism (SNP) was determined in 660 healthy Finnish volunteers, of whom 32 participated in a pharmacokinetic crossover study involving the administration of 20 mg atorvastatin and rosuvastatin. The frequency of the c.421A variant allele was 9.5% (95% confidence interval 8.1-11.3%). Subjects with the c.421AA genotype (n = 4) had a 72% larger mean area under the plasma atorvastatin concentration-time curve from time 0 to infinity (AUC(0-infinity)) than individuals with the c.421CC genotype had (n = 16; P = 0.049). In participants with the c.421AA genotype, the rosuvastatin AUC(0-infinity) was 100% greater than in those with c.421CA (n = 12) and 144% greater than in those with the c.421CC genotype. Also, those with the c.421AA genotype showed peak plasma rosuvastatin concentrations 108% higher than those in the c.421CA genotype group and 131% higher than those in the c.421CC genotype group (P < or = 0.01). In MDCKII-ABCG2 cells, atorvastatin transport was increased in the apical direction as compared with vector control cells (transport ratio 1.9 +/- 0.1 vs. 1.1 +/- 0.1). These results indicate that the ABCG2 polymorphism markedly affects the pharmacokinetics of atorvastatin and, even more so, of rosuvastatin-potentially affecting the efficacy and toxicity of statin therapy.
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              Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration.

              The US Food and Drug Administration (FDA) has seen a recent increase in the application of physiologically based pharmacokinetic (PBPK) modeling towards assessing the potential of drug-drug interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, we evaluated the predictive performance of PBPK modeling in predicting cytochrome P450 (CYP)-mediated DDI.
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                Author and article information

                Contributors
                manthena.v.varma@pfizer.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
                20 July 2021
                September 2021
                : 10
                : 9 ( doiID: 10.1002/psp4.v10.9 )
                : 1018-1031
                Affiliations
                [ 1 ] Pharmacokinetics, Dynamics and Metabolism, Medicine Design Worldwide R&D Pfizer Inc Groton CT USA
                [ 2 ] Pharmacokinetics, Dynamics and Metabolism, Medicine Design Worldwide R&D Pfizer Inc San Diego CA USA
                [ 3 ] Pharmacokinetics, Dynamics and Metabolism, Medicine Design Worldwide R&D Pfizer Inc Cambridge MA USA
                [ 4 ] Discovery Science, Medicine Design Worldwide R&D Pfizer Inc Groton CT USA
                [ 5 ]Present address: Drug Metabolism & Pharmacokinetics Janssen Research & Development, Pharmaceutical Companies of Johnson & Johnson San Diego CA USA
                Author notes
                [*] [* ] Correspondence

                Manthena V. S. Varma, Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide Research and Development, Pfizer Inc., 558 Eastern Point Road, Groton, CT 06340, USA

                Email: manthena.v.varma@ 123456pfizer.com

                Author information
                https://orcid.org/0000-0003-1284-2769
                https://orcid.org/0000-0001-7112-2812
                https://orcid.org/0000-0002-9541-6329
                Article
                PSP412672
                10.1002/psp4.12672
                8452302
                34164937
                56cdd24b-14ac-4176-bd81-12bb5bf21be2
                © 2021 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of 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
                : 18 May 2021
                : 04 May 2021
                : 24 May 2021
                Page count
                Figures: 4, Tables: 3, Pages: 14, Words: 9264
                Funding
                Funded by: Pfizer, Inc , doi 10.13039/100004319;
                Categories
                Article
                Research
                Articles
                Custom metadata
                2.0
                September 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.7 mode:remove_FC converted:20.09.2021

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