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image of Establishment of an Integrated Population Pharmacokinetic/ Pharmacodynamics Model of Apixaban in Chinese Healthy Population Adjusting for Key Genetic Variants

Abstract

Aims

To improve the understanding of pharmacokinetic/pharmacodynamic (PK/PD) profiles of apixaban, supporting personalised drug prescriptions for future patients.

Background

Genetic as well as nongenetic factors can affect the predictable PK and PD characteristics of apixaban.

Objective

Establish a integrated popPK/PD model that adjusts for critical genetic variant.

Methods

The integrated PK/PD models was characterized on the basis of PK (apixaban blood concentration) and PD (prothrombin time (PT), activated partial thromboplastin time (APTT), and anti-FXa activity) data from 181 healthy Chinese volunteers. Other investigated covariate variables included: Meaningful intrinsic and extraneous determinants, correlated markers (ABCG2, F13A1, C3, .). A total of 2877 PK concentration observations were included in the modeling dataset.

Results

The PK model of apixaban is adopted by single compartment model with first-order oral absorption. The estimated values of total clearance rate (CL/F), apparent distribution volume (V/F), and absorption rate constant (KA) in the final model are 3.37 l/h, 28.2 l, and 0.781 l/h, respectively. The PK model includes significance covariates such as FOOD, RBC, WT, and gene (ABCG2). The PD model of apixaban is adopted by a linear direct effect model with additive error, which was used to describe the relationship between markers such as APTT, PT, anti-FXa, plasma concentration. PK simulation within the modelled dose range is similar to clinical real date, while PD simulation results also show that the simulated exposure parameters is within the range of the literature.

Conclusion

We established a comprehensive PK/PD model and used it to simulate markers level such as APTT, PT, and anti-FXa of apixaban. Individual predictive values with a dose of 2.5 mg are basically within the expected recommended range.

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2024-10-24
2025-01-08
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References

  1. Gong I.Y. Kim R.B. Importance of pharmacokinetic profile and variability as determinants of dose and response to dabigatran, rivaroxaban, and apixaban. Can. J. Cardiol. 2013 29 7 S24 S33 10.1016/j.cjca.2013.04.002 23790595
    [Google Scholar]
  2. Rizos T. Horstmann S. Veltkamp R. Intracerebral bleeding in patients on antithrombotic agents. Semin. Thromb. Hemost. 2013 39 8 963 971 10.1055/s‑0033‑1357506 24114010
    [Google Scholar]
  3. Kim H.K. Tantry U.S. Smith S.C. Jr Jeong M.H. Park S.J. Kim M.H. Lim D.S. Shin E.S. Park D.W. Huo Y. Chen S.L. Bo Z. Goto S. Kimura T. Yasuda S. Chen W.J. Chan M. Aradi D. Geisler T. Gorog D.A. Sibbing D. Lip G.Y.H. Angiolillo D.J. Gurbel P.A. Jeong Y.H. The East Asian Paradox: An Updated Position Statement on the Challenges to the Current Antithrombotic Strategy in Patients with Cardiovascular Disease. Thromb. Haemost. 2021 121 4 422 432 10.1055/s‑0040‑1718729 33171520
    [Google Scholar]
  4. Mu G. Zhang H. Liu Z. Xie Q. Zhou S. Wang Z. Wang Z. Hu K. Hou J. Zhao N. Xiang Q. Cui Y. Standard vs. low-dose rivaroxaban in patients with atrial fibrillation: A systematic review and meta-analysis. Eur. J. Clin. Pharmacol. 2022 78 2 181 190 10.1007/s00228‑021‑03226‑6 34651200
    [Google Scholar]
  5. Attelind S. Hallberg P. Wadelius M. Hamberg A.K. Siegbahn A. Granger C.B. Lopes R.D. Alexander J.H. Wallentin L. Eriksson N. Genetic determinants of apixaban plasma levels and their relationship to bleeding and thromboembolic events. Front. Genet. 2022 13 982955 10.3389/fgene.2022.982955 36186466
    [Google Scholar]
  6. Mu G. Xie Q. Liu Z. Zhang H. Meng X. Song J. Zhou S. Wang Z. Wang Z. Zhao X. Jiang J. Liao M. Bao J. Zhang F. Xiang Q. Cui Y. Identification of genetic biomarkers associated with pharmacokinetics and pharmacodynamics of apixaban in Chinese healthy volunteers. Expert Opin. Drug Metab. Toxicol. 2023 19 1 43 51 10.1080/17425255.2023.2184344 36867504
    [Google Scholar]
  7. Edwina A.E. Dia N. Dreesen E. Vanassche T. Verhamme P. Spriet I. Van der Linden L. Tournoy J. Insights into the pharmacokinetics and pharmacodynamics of direct oral anticoagulants in older adults with atrial fibrillation: A structured narrative review. Clin. Pharmacokinet. 2023 62 3 351 373 10.1007/s40262‑023‑01222‑w 36862336
    [Google Scholar]
  8. Oda K. Saito H. Jono H. Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring. Pharmacol. Ther. 2023 246 108433 10.1016/j.pharmthera.2023.108433 37149156
    [Google Scholar]
  9. Dilli Batcha J. Raju A. Matcha S. Raj S E. Udupa K. Gota V. Mallayasamy S. Factors influencing pharmacokinetics of tamoxifen in breast cancer patients: A systematic review of population pharmacokinetic models. Biology (Basel) 2022 12 1 51 10.3390/biology12010051 36671744
    [Google Scholar]
  10. Zwart T.C. Guchelaar H.J. van der Boog P.J.M. Swen J.J. van Gelder T. de Fijter J.W. Moes D.J.A.R. Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation. Drug Discov. Today 2021 26 11 2527 2546 10.1016/j.drudis.2021.06.001 34119665
    [Google Scholar]
  11. Willmann S. Zhang L. Frede M. Kubitza D. Mueck W. Schmidt S. Solms A. Yan X. Garmann D. Integrated population pharmacokinetic analysis of rivaroxaban across multiple patient populations. CPT Pharmacometrics Syst. Pharmacol. 2018 7 5 309 320 10.1002/psp4.12288 29660785
    [Google Scholar]
  12. Zhao N. Liu Z. Xie Q. Wang Z. Sun Z. Xiang Q. Cui Y. A Combined Pharmacometrics Analysis of Biomarker Distribution Under Treatment With Standard- or Low-Dose Rivaroxaban in Real-World Chinese Patients With Nonvalvular Atrial Fibrillation. Front. Pharmacol. 2022 13 814724 10.3389/fphar.2022.814724 35370683
    [Google Scholar]
  13. Liu Y. Xie Q. Liu Z. Wang Z. Mu G. Zhang Y. Zhao Z. Yuan D. Guo L. Wang N. Xiang J. Song H. Jiang J. Xiang Q. Cui Y. Population pharmacokinetic analysis for dabigatran etexilate in Chinese patients with non-valvular atrial fibrillation. Front. Cardiovasc. Med. 2022 9 998751 10.3389/fcvm.2022.998751 36386303
    [Google Scholar]
  14. Keizer R.J. Karlsson M.O. Hooker A. Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose. CPT Pharmacometrics Syst. Pharmacol. 2013 2 6 1 9 10.1038/psp.2013.24 23836189
    [Google Scholar]
  15. Zhang L. Beal S.L. Sheiner L.B. Simultaneous vs. sequential analysis for population PK/PD data I: Best-case performance. J. Pharmacokinet. Pharmacodyn. 2003 30 6 387 404 10.1023/B:JOPA.0000012998.04442.1f 15000421
    [Google Scholar]
  16. Zhang L. Beal S.L. Sheiner L.B. Simultaneous vs. sequential analysis for population PK/PD data II: Robustness of methods. J. Pharmacokinet. Pharmacodyn. 2003 30 6 405 416 10.1023/B:JOPA.0000012999.36063.4e 15000422
    [Google Scholar]
  17. Cirincione B. Kowalski K. Nielsen J. Roy A. Thanneer N. Byon W. Boyd R. Wang X. Leil T. LaCreta F. Ueno T. Oishi M. Frost C. Population pharmacokinetics of apixaban in subjects with nonvalvular atrial fibrillation. CPT Pharmacometrics Syst. Pharmacol. 2018 7 11 728 738 10.1002/psp4.12347 30259707
    [Google Scholar]
  18. Byon W. Sweeney K. Frost C. Boyd R.A. Population pharmacokinetics, pharmacodynamics, and exploratory exposure–response analyses of apixaban in subjects treated for venous thromboembolism. CPT Pharmacometrics Syst. Pharmacol. 2017 6 5 340 349 10.1002/psp4.12184 28547774
    [Google Scholar]
  19. Ueshima S. Hira D. Kimura Y. Fujii R. Tomitsuka C. Yamane T. Tabuchi Y. Ozawa T. Itoh H. Ohno S. Horie M. Terada T. Katsura T. Population pharmacokinetics and pharmacogenomics of apixaban in Japanese adult patients with atrial fibrillation. Br. J. Clin. Pharmacol. 2018 84 6 1301 1312 10.1111/bcp.13561 29457840
    [Google Scholar]
  20. Comets E. Brendel K. Mentré F. Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: The npde add-on package for R. Comput. Methods Programs Biomed. 2008 90 2 154 166 10.1016/j.cmpb.2007.12.002 18215437
    [Google Scholar]
  21. Jonsson E.N. Karlsson M.O. Xpose—an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput. Methods Programs Biomed. 1998 58 1 51 64 10.1016/S0169‑2607(98)00067‑4 10195646
    [Google Scholar]
  22. Goto E. Horinaka S. Ishimitsu T. Kato T. Factor Xa inhibitors in clinical practice: Comparison of pharmacokinetic profiles. Drug Metab. Pharmacokinet. 2020 35 1 151 159 10.1016/j.dmpk.2019.10.005 32007354
    [Google Scholar]
  23. Leil T.A. Frost C. Wang X. Pfister M. LaCreta F. Model-based exposure-response analysis of apixaban to quantify bleeding risk in special populations of subjects undergoing orthopedic surgery. CPT Pharmacometrics Syst. Pharmacol. 2014 3 9 1 9 10.1038/psp.2014.34 25229619
    [Google Scholar]
  24. Pokorney S.D. Chertow G.M. Al-Khalidi H.R. Gallup D. Dignacco P. Mussina K. Bansal N. Gadegbeku C.A. Garcia D.A. Garonzik S. Lopes R.D. Mahaffey K.W. Matsuda K. Middleton J.P. Rymer J.A. Sands G.H. Thadhani R. Thomas K.L. Washam J.B. Winkelmayer W.C. Granger C.B. Apixaban for patients with atrial fibrillation on hemodialysis: A multicenter randomized controlled trial. Circulation 2022 146 23 1735 1745 10.1161/CIRCULATIONAHA.121.054990 36335914
    [Google Scholar]
  25. McNally J.S. McLaughlin M.S. Hinckley P.J. Treiman S.M. Stoddard G.J. Parker D.L. Treiman G.S. Intraluminal thrombus, intraplaque hemorrhage, plaque thickness, and current smoking optimally predict carotid stroke. Stroke 2015 46 1 84 90 10.1161/STROKEAHA.114.006286 25406146
    [Google Scholar]
  26. Glantz S.A. Parmley W.W. Passive smoking and heart disease. Mechanisms and risk. JAMA 1995 273 13 1047 1053 10.1001/jama.1995.03520370089043 7897790
    [Google Scholar]
  27. Matetzky S. Tani S. Kangavari S. Dimayuga P. Yano J. Xu H. Chyu K.Y. Fishbein M.C. Shah P.K. Cercek B. Smoking increases tissue factor expression in atherosclerotic plaques: Implications for plaque thrombogenicity. Circulation 2000 102 6 602 604 10.1161/01.CIR.102.6.602 10931797
    [Google Scholar]
  28. Raij L. DeMaster E.G. Jaimes E.A. Cigarette smoke-induced endothelium dysfunction: role of superoxide anion. J. Hypertens. 2001 19 5 891 897 10.1097/00004872‑200105000‑00009 11393672
    [Google Scholar]
  29. Simpson A.J. Gray R.S. Moore N.R. Booth N.A. The effects of chronic smoking on the fibrinolytic potential of plasma and platelets. Br. J. Haematol. 1997 97 1 208 213 10.1046/j.1365‑2141.1997.d01‑2137.x 9136967
    [Google Scholar]
  30. Petrik P.V. Gelabert H.A. Moore W.S. Quinones-Baldrich W. Law M.M. Cigarette smoking accelerates carotid artery intimal hyperplasia in a dose-dependent manner. Stroke 1995 26 8 1409 1414 10.1161/01.STR.26.8.1409 7631346
    [Google Scholar]
  31. Grassi G. Seravalle G. Calhoun D.A. Bolla G.B. Giannattasio C. Marabini M. Del Bo A. Mancia G. Mechanisms responsible for sympathetic activation by cigarette smoking in humans. Circulation 1994 90 1 248 253 10.1161/01.CIR.90.1.248 8026005
    [Google Scholar]
  32. Liu Z. Xie Q. Zhao X. Tan Y. Wang W. Cao Y. Wei X. Mu G. Zhang H. Zhou S. Wang X. Cao Y. Li X. Chen S. Cao D. Cui Y. Xiang Q. The Pharmacogenetic Variability Associated with the Pharmacokinetics and Pharmacodynamics of Rivaroxaban in Healthy Chinese Subjects: A National Multicenter Exploratory Study. Clin. Ther. 2024 46 4 313 321 10.1016/j.clinthera.2024.02.009 38553322
    [Google Scholar]
  33. Terrier J. Gaspar F. Gosselin P. Raboud O. Lenoir C. Rollason V. Csajka C. Samer C. Fontana P. Daali Y. Reny J.L. Apixaban and rivaroxaban’s physiologically‐based pharmacokinetic model validation in hospitalized patients: A first step for larger use of a priori modeling approach at bed side. CPT Pharmacometrics Syst. Pharmacol. 2023 12 12 1872 1883 10.1002/psp4.13036 37794718
    [Google Scholar]
  34. Liu X. Zhang Y. Ding H. Yan M. Jiao Z. Zhong M. Ma C. Population pharmacokinetic and pharmacodynamic analysis of rivaroxaban in Chinese patients with non-valvular atrial fibrillation. Acta Pharmacol. Sin. 2022 43 10 2723 2734 10.1038/s41401‑022‑00892‑9 35354961
    [Google Scholar]
  35. Song S. Kang D. Halim A.B. Miller R. Population pharmacokinetic‐pharmacodynamic modeling analysis of intrinsic FXa and bleeding from edoxaban treatment. J. Clin. Pharmacol. 2014 54 8 910 916 10.1002/jcph.306 24706516
    [Google Scholar]
  36. Mueck W. Becka M. Kubitza D. Voith B. Zuehlsdorf M. Population model of the pharmacokinetics and pharmacodynamics of rivaroxaban – an oral, direct Factor Xa inhibitor – in healthy subjects. Int. J. Clin. Pharmacol. Ther. 2007 45 6 335 344 10.5414/CPP45335 17595891
    [Google Scholar]
  37. January C.T. Wann L.S. Calkins H. Chen L.Y. Cigarroa J.E. Cleveland J.C. Jr Ellinor P.T. Ezekowitz M.D. Field M.E. Furie K.L. Heidenreich P.A. Murray K.T. Shea J.B. Tracy C.M. Yancy C.W. 2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation. J. Am. Coll. Cardiol. 2019 74 1 104 132 10.1016/j.jacc.2019.01.011 30703431
    [Google Scholar]
  38. Mithoowani S. Moffat K.A. Gupta A. Carlino S.A. Crowther M.A. Low molecular weight heparin anti-Xa assays can identify patients with clinically important apixaban and rivaroxaban drug levels. Thromb. Res. 2022 215 1 4 10.1016/j.thromres.2022.05.005 35580465
    [Google Scholar]
  39. Levy J.H. Ageno W. Chan N.C. Crowther M. Verhamme P. Weitz J.I. When and how to use antidotes for the reversal of direct oral anticoagulants: Guidance from the SSC of the ISTH. J. Thromb. Haemost. 2016 14 3 623 627 10.1111/jth.13227 26911798
    [Google Scholar]
  40. Granger C.B. Alexander J.H. McMurray J.J.V. Lopes R.D. Hylek E.M. Hanna M. Al-Khalidi H.R. Ansell J. Atar D. Avezum A. Bahit M.C. Diaz R. Easton J.D. Ezekowitz J.A. Flaker G. Garcia D. Geraldes M. Gersh B.J. Golitsyn S. Goto S. Hermosillo A.G. Hohnloser S.H. Horowitz J. Mohan P. Jansky P. Lewis B.S. Lopez-Sendon J.L. Pais P. Parkhomenko A. Verheugt F.W.A. Zhu J. Wallentin L. Apixaban versus warfarin in patients with atrial fibrillation. N. Engl. J. Med. 2011 365 11 981 992 10.1056/NEJMoa1107039 21870978
    [Google Scholar]
  41. Kalhori F. Yazdyani H. Khademorezaeian F. Hamzkanloo N. Mokaberi P. Hosseini S. Chamani J. Enzyme activity inhibition properties of new cellulose nanocrystals from Citrus medica L. pericarp: A perspective of cholesterol lowering. Luminescence 2022 37 11 1836 1845 10.1002/bio.4360 35946171
    [Google Scholar]
  42. El-Shenawy A.A. Mahmoud R.A. Mahmoud E.A. Mohamed M.S. Intranasal in situ gel of apixaban-loaded nanoethosomes: Preparation, optimization, and in vivo evaluation. AAPS PharmSciTech 2021 22 4 147 10.1208/s12249‑021‑02020‑y 33948767
    [Google Scholar]
  43. Maheri H. Hashemzadeh F. Shakibapour N. Kamelniya E. Malaekeh-Nikouei B. Mokaberi P. Chamani J. Glucokinase activity enhancement by cellulose nanocrystals isolated from jujube seed: A novel perspective for type II diabetes mellitus treatment (in vitro). J. Mol. Struct. 2022 1269 133803 10.1016/j.molstruc.2022.133803
    [Google Scholar]
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