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2000
Volume 31, Issue 9
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

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 genetic factors (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 1/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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References

  1. GongI.Y. KimR.B. Importance of pharmacokinetic profile and variability as determinants of dose and response to dabigatran, rivaroxaban, and apixaban.Can. J. Cardiol.2013297S24S3310.1016/j.cjca.2013.04.00223790595
    [Google Scholar]
  2. RizosT. HorstmannS. VeltkampR. Intracerebral bleeding in patients on antithrombotic agents.Semin. Thromb. Hemost.201339896397110.1055/s‑0033‑135750624114010
    [Google Scholar]
  3. KimH.K. TantryU.S. SmithS.C.Jr JeongM.H. ParkS.J. KimM.H. LimD.S. ShinE.S. ParkD.W. HuoY. ChenS.L. BoZ. GotoS. KimuraT. YasudaS. ChenW.J. ChanM. AradiD. GeislerT. GorogD.A. SibbingD. LipG.Y.H. AngiolilloD.J. GurbelP.A. JeongY.H. The East Asian Paradox: An updated position statement on the challenges to the current antithrombotic strategy in patients with cardiovascular disease.Thromb. Haemost.2021121442243210.1055/s‑0040‑171872933171520
    [Google Scholar]
  4. MuG. ZhangH. LiuZ. XieQ. ZhouS. WangZ. WangZ. HuK. HouJ. ZhaoN. XiangQ. CuiY. Standard vs. low-dose rivaroxaban in patients with atrial fibrillation: A systematic review and meta-analysis.Eur. J. Clin. Pharmacol.202278218119010.1007/s00228‑021‑03226‑634651200
    [Google Scholar]
  5. AttelindS. HallbergP. WadeliusM. HambergA.K. SiegbahnA. GrangerC.B. LopesR.D. AlexanderJ.H. WallentinL. ErikssonN. Genetic determinants of apixaban plasma levels and their relationship to bleeding and thromboembolic events.Front. Genet.20221398295510.3389/fgene.2022.98295536186466
    [Google Scholar]
  6. MuG. XieQ. LiuZ. ZhangH. MengX. SongJ. ZhouS. WangZ. WangZ. ZhaoX. JiangJ. LiaoM. BaoJ. ZhangF. XiangQ. CuiY. Identification of genetic biomarkers associated with pharmacokinetics and pharmacodynamics of apixaban in Chinese healthy volunteers.Expert Opin. Drug Metab. Toxicol.2023191435110.1080/17425255.2023.218434436867504
    [Google Scholar]
  7. EdwinaA.E. DiaN. DreesenE. VanasscheT. VerhammeP. SprietI. Van der LindenL. TournoyJ. Insights into the pharmacokinetics and pharmacodynamics of direct oral anticoagulants in older adults with atrial fibrillation: A structured narrative review.Clin. Pharmacokinet.202362335137310.1007/s40262‑023‑01222‑w36862336
    [Google Scholar]
  8. OdaK. SaitoH. JonoH. Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring.Pharmacol. Ther.202324610843310.1016/j.pharmthera.2023.10843337149156
    [Google Scholar]
  9. Dilli BatchaJ. RajuA. MatchaS. Raj SE. UdupaK. GotaV. MallayasamyS. Factors influencing pharmacokinetics of tamoxifen in breast cancer patients: A systematic review of population pharmacokinetic models.Biology (Basel)20221215110.3390/biology1201005136671744
    [Google Scholar]
  10. ZwartT.C. GuchelaarH.J. van der BoogP.J.M. SwenJ.J. van GelderT. de FijterJ.W. MoesD.J.A.R. Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation.Drug Discov. Today202126112527254610.1016/j.drudis.2021.06.00134119665
    [Google Scholar]
  11. WillmannS. ZhangL. FredeM. KubitzaD. MueckW. SchmidtS. SolmsA. YanX. GarmannD. Integrated population pharmacokinetic analysis of rivaroxaban across multiple patient populations.CPT Pharmacometrics Syst. Pharmacol.20187530932010.1002/psp4.1228829660785
    [Google Scholar]
  12. ZhaoN. LiuZ. XieQ. WangZ. SunZ. XiangQ. CuiY. 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.20221381472410.3389/fphar.2022.81472435370683
    [Google Scholar]
  13. LiuY. XieQ. LiuZ. WangZ. MuG. ZhangY. ZhaoZ. YuanD. GuoL. WangN. XiangJ. SongH. JiangJ. XiangQ. CuiY. Population pharmacokinetic analysis for dabigatran etexilate in Chinese patients with non-valvular atrial fibrillation.Front. Cardiovasc. Med.2022999875110.3389/fcvm.2022.99875136386303
    [Google Scholar]
  14. KeizerR.J. KarlssonM.O. HookerA. Modeling and simulation workbench for NONMEM: Tutorial on pirana, PsN, and xpose.CPT Pharmacometrics Syst. Pharmacol.2013261910.1038/psp.2013.2423836189
    [Google Scholar]
  15. ZhangL. BealS.L. SheinerL.B. Simultaneous vs. sequential analysis for population PK/PD data I: Best-case performance.J. Pharmacokinet. Pharmacodyn.200330638740410.1023/B:JOPA.0000012998.04442.1f15000421
    [Google Scholar]
  16. ZhangL. BealS.L. SheinerL.B. Simultaneous vs. sequential analysis for population PK/PD data II: Robustness of methods.J. Pharmacokinet. Pharmacodyn.200330640541610.1023/B:JOPA.0000012999.36063.4e15000422
    [Google Scholar]
  17. CirincioneB. KowalskiK. NielsenJ. RoyA. ThanneerN. ByonW. BoydR. WangX. LeilT. LaCretaF. UenoT. OishiM. FrostC. Population pharmacokinetics of apixaban in subjects with nonvalvular atrial fibrillation.CPT Pharmacometrics Syst. Pharmacol.201871172873810.1002/psp4.1234730259707
    [Google Scholar]
  18. ByonW. SweeneyK. FrostC. BoydR.A. Population pharmacokinetics, pharmacodynamics, and exploratory exposure–response analyses of apixaban in subjects treated for venous thromboembolism.CPT Pharmacometrics Syst. Pharmacol.20176534034910.1002/psp4.1218428547774
    [Google Scholar]
  19. UeshimaS. HiraD. KimuraY. FujiiR. TomitsukaC. YamaneT. TabuchiY. OzawaT. ItohH. OhnoS. HorieM. TeradaT. KatsuraT. Population pharmacokinetics and pharmacogenomics of apixaban in Japanese adult patients with atrial fibrillation.Br. J. Clin. Pharmacol.20188461301131210.1111/bcp.1356129457840
    [Google Scholar]
  20. CometsE. BrendelK. MentréF. Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: The npde add-on package for R.Comput. Methods Programs Biomed.200890215416610.1016/j.cmpb.2007.12.00218215437
    [Google Scholar]
  21. JonssonE.N. KarlssonM.O. Xpose-an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM.Comput. Methods Programs Biomed.1998581516410.1016/S0169‑2607(98)00067‑410195646
    [Google Scholar]
  22. GotoE. HorinakaS. IshimitsuT. KatoT. Factor Xa inhibitors in clinical practice: Comparison of pharmacokinetic profiles.Drug Metab. Pharmacokinet.202035115115910.1016/j.dmpk.2019.10.00532007354
    [Google Scholar]
  23. LeilT.A. FrostC. WangX. PfisterM. LaCretaF. Model-based exposure-response analysis of apixaban to quantify bleeding risk in special populations of subjects undergoing orthopedic surgery.CPT Pharmacometrics Syst. Pharmacol.2014391910.1038/psp.2014.3425229619
    [Google Scholar]
  24. PokorneyS.D. ChertowG.M. Al-KhalidiH.R. GallupD. DignaccoP. MussinaK. BansalN. GadegbekuC.A. GarciaD.A. GaronzikS. LopesR.D. MahaffeyK.W. MatsudaK. MiddletonJ.P. RymerJ.A. SandsG.H. ThadhaniR. ThomasK.L. WashamJ.B. WinkelmayerW.C. GrangerC.B. Apixaban for patients with atrial fibrillation on hemodialysis: A multicenter randomized controlled trial.Circulation2022146231735174510.1161/CIRCULATIONAHA.121.05499036335914
    [Google Scholar]
  25. McNallyJ.S. McLaughlinM.S. HinckleyP.J. TreimanS.M. StoddardG.J. ParkerD.L. TreimanG.S. Intraluminal thrombus, intraplaque hemorrhage, plaque thickness, and current smoking optimally predict carotid stroke.Stroke2015461849010.1161/STROKEAHA.114.00628625406146
    [Google Scholar]
  26. GlantzS.A. ParmleyW.W. Passive smoking and heart disease. Mechanisms and risk.JAMA1995273131047105310.1001/jama.1995.035203700890437897790
    [Google Scholar]
  27. MatetzkyS. TaniS. KangavariS. DimayugaP. YanoJ. XuH. ChyuK.Y. FishbeinM.C. ShahP.K. CercekB. Smoking increases tissue factor expression in atherosclerotic plaques: Implications for plaque thrombogenicity.Circulation2000102660260410.1161/01.CIR.102.6.60210931797
    [Google Scholar]
  28. RaijL. DeMasterE.G. JaimesE.A. Cigarette smoke-induced endothelium dysfunction: Role of superoxide anion.J. Hypertens.200119589189710.1097/00004872‑200105000‑0000911393672
    [Google Scholar]
  29. SimpsonA.J. GrayR.S. MooreN.R. BoothN.A. The effects of chronic smoking on the fibrinolytic potential of plasma and platelets.Br. J. Haematol.199797120821310.1046/j.1365‑2141.1997.d01‑2137.x9136967
    [Google Scholar]
  30. PetrikP.V. GelabertH.A. MooreW.S. Quinones-BaldrichW. LawM.M. Cigarette smoking accelerates carotid artery intimal hyperplasia in a dose-dependent manner.Stroke19952681409141410.1161/01.STR.26.8.14097631346
    [Google Scholar]
  31. GrassiG. SeravalleG. CalhounD.A. BollaG.B. GiannattasioC. MarabiniM. Del BoA. ManciaG. Mechanisms responsible for sympathetic activation by cigarette smoking in humans.Circulation199490124825310.1161/01.CIR.90.1.2488026005
    [Google Scholar]
  32. LiuZ. XieQ. ZhaoX. TanY. WangW. CaoY. WeiX. MuG. ZhangH. ZhouS. WangX. CaoY. LiX. ChenS. CaoD. CuiY. XiangQ. The pharmacogenetic variability associated with the pharmacokinetics and pharmacodynamics of rivaroxaban in healthy Chinese subjects: A National Multicenter Exploratory study.Clin. Ther.202446431332110.1016/j.clinthera.2024.02.00938553322
    [Google Scholar]
  33. TerrierJ. GasparF. GosselinP. RaboudO. LenoirC. RollasonV. CsajkaC. SamerC. FontanaP. DaaliY. RenyJ.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.202312121872188310.1002/psp4.1303637794718
    [Google Scholar]
  34. LiuX. ZhangY. DingH. YanM. JiaoZ. ZhongM. MaC. Population pharmacokinetic and pharmacodynamic analysis of rivaroxaban in Chinese patients with non-valvular atrial fibrillation.Acta Pharmacol. Sin.202243102723273410.1038/s41401‑022‑00892‑935354961
    [Google Scholar]
  35. SongS. KangD. HalimA.B. MillerR. Population pharmacokinetic-pharmacodynamic modeling analysis of intrinsic FXa and bleeding from edoxaban treatment.J. Clin. Pharmacol.201454891091610.1002/jcph.30624706516
    [Google Scholar]
  36. MueckW. BeckaM. KubitzaD. VoithB. ZuehlsdorfM. Population model of the pharmacokinetics and pharmacodynamics of rivaroxaban – an oral, direct Factor Xa inhibitor – in healthy subjects.Int. J. Clin. Pharmacol. Ther.200745633534410.5414/CPP4533517595891
    [Google Scholar]
  37. JanuaryC.T. WannL.S. CalkinsH. ChenL.Y. CigarroaJ.E. ClevelandJ.C.Jr EllinorP.T. EzekowitzM.D. FieldM.E. FurieK.L. HeidenreichP.A. MurrayK.T. SheaJ.B. TracyC.M. YancyC.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.201974110413210.1016/j.jacc.2019.01.01130703431
    [Google Scholar]
  38. MithoowaniS. MoffatK.A. GuptaA. CarlinoS.A. CrowtherM.A. Low molecular weight heparin anti-Xa assays can identify patients with clinically important apixaban and rivaroxaban drug levels.Thromb. Res.20222151410.1016/j.thromres.2022.05.00535580465
    [Google Scholar]
  39. LevyJ.H. AgenoW. ChanN.C. CrowtherM. VerhammeP. WeitzJ.I. When and how to use antidotes for the reversal of direct oral anticoagulants: Guidance from the SSC of the ISTH.J. Thromb. Haemost.201614362362710.1111/jth.1322726911798
    [Google Scholar]
  40. GrangerC.B. AlexanderJ.H. McMurrayJ.J.V. LopesR.D. HylekE.M. HannaM. Al-KhalidiH.R. AnsellJ. AtarD. AvezumA. BahitM.C. DiazR. EastonJ.D. EzekowitzJ.A. FlakerG. GarciaD. GeraldesM. GershB.J. GolitsynS. GotoS. HermosilloA.G. HohnloserS.H. HorowitzJ. MohanP. JanskyP. LewisB.S. Lopez-SendonJ.L. PaisP. ParkhomenkoA. VerheugtF.W.A. ZhuJ. WallentinL. Apixaban versus warfarin in patients with atrial fibrillation.N. Engl. J. Med.20113651198199210.1056/NEJMoa110703921870978
    [Google Scholar]
  41. KalhoriF. YazdyaniH. KhademorezaeianF. HamzkanlooN. MokaberiP. HosseiniS. ChamaniJ. Enzyme activity inhibition properties of new cellulose nanocrystals from Citrus medica L. pericarp: A perspective of cholesterol lowering.Luminescence202237111836184510.1002/bio.436035946171
    [Google Scholar]
  42. El-ShenawyA.A. MahmoudR.A. MahmoudE.A. MohamedM.S. Intranasal in situ gel of apixaban-loaded nanoethosomes: Preparation, optimization, and in vivo evaluation.AAPS PharmSciTech202122414710.1208/s12249‑021‑02020‑y33948767
    [Google Scholar]
  43. MaheriH. HashemzadehF. ShakibapourN. KamelniyaE. Malaekeh-NikoueiB. MokaberiP. ChamaniJ. Glucokinase activity enhancement by cellulose nanocrystals isolated from jujube seed: A novel perspective for type II diabetes mellitus treatment (in vitro).J. Mol. Struct.2022126913380310.1016/j.molstruc.2022.133803
    [Google Scholar]
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