Literature DB >> 29396738

Pharmacokinetic and pharmacodynamic re-evaluation of a genetic-guided warfarin trial.

Carlo Federico Zambon1, Vittorio Pengo2, Stefania Moz3, Dania Bozzato3, Paola Fogar4, Andrea Padoan3, Mario Plebani3, Francesca Groppa3, Giovanni De Rosa3, Roberto Padrini5.   

Abstract

PURPOSE: A previous trial failed to demonstrate the superiority of a demographic-genetic algorithm in predicting warfarin (W) dose over a standard clinical approach. The purpose of the present study is to re-analyse the results in subgroups of patients with differing baseline sensitivity to W, integrated with additional pharmacokinetic data.
METHODS: The original trial allocated 180 treatment-naïve patients with non-valvular atrial fibrillation to a control arm (CTL, n = 92) or a genetic-guided arm (GEN, n = 88). Before starting anticoagulation treatment, all patients were genotyped for CYP2C9, VKORC1 and CYP4F2 variants and classified into four quartiles (Q1, Q2, Q3, Q4) according to the algorithm-predicted W maintenance dose. International normalised ratios (INR) and plasma concentrations of S-warfarin [S-W]s and R-warfarin [R-W]s were measured at baseline and on days 5, 7, 9, 12, 15 and 19 of therapy.
RESULTS: In the lowest dose quartile (Q1), the number of INRs > 3 and mean INR values on days 5 and 7 were significantly higher in CTL than in GEN. In Q3 and Q4, the mean INR values reached therapeutic level (> 2) 2 days later in CTL than in GEN. During follow-up, the mean time courses of INRs and [S-W]s in GEN were remarkably stable in all dose quartiles. Thus, mean changes from starting to final doses were significantly smaller in GEN than in CTL. Plasma concentrations of R-W (a partially active enantiomer) steadily increased from day 5 to day 19 in all Qs in both CTL and GEN, except in the Q1 CTL group, due to the marked dose reduction required.
CONCLUSIONS: This analysis showed that the demographic-genetic algorithm used to predict the W dose can identify patients with differing degrees of sensitivity to W and to 'normalise' their average anticoagulant responses. The progressive rise in [R-W]s throughout the 19-day follow-up indicates that the (partial) contribution of R-W to the W anticoagulant effect changes continually during the early phase of treatment.

Entities:  

Keywords:  Algorithm; Pharmacodynamic; Pharmacogenetics; Pharmacokinetics; Warfarin

Mesh:

Substances:

Year:  2018        PMID: 29396738     DOI: 10.1007/s00228-018-2422-8

Source DB:  PubMed          Journal:  Eur J Clin Pharmacol        ISSN: 0031-6970            Impact factor:   2.953


  22 in total

1.  VKORC1, CYP2C9 and CYP4F2 genetic-based algorithm for warfarin dosing: an Italian retrospective study.

Authors:  Carlo-Federico Zambon; Vittorio Pengo; Roberto Padrini; Daniela Basso; Stefania Schiavon; Paola Fogar; Alessandra Nisi; Anna Chiara Frigo; Stefania Moz; Michela Pelloso; Mario Plebani
Journal:  Pharmacogenomics       Date:  2011-01       Impact factor: 2.533

2.  A PK-PD model for predicting the impact of age, CYP2C9, and VKORC1 genotype on individualization of warfarin therapy.

Authors:  A-K Hamberg; M-L Dahl; M Barban; M G Scordo; M Wadelius; V Pengo; R Padrini; E N Jonsson
Journal:  Clin Pharmacol Ther       Date:  2007-02-14       Impact factor: 6.875

3.  R-warfarin anticoagulant effect.

Authors:  Roberto Padrini; Luigi Quintieri
Journal:  Br J Clin Pharmacol       Date:  2017-05-11       Impact factor: 4.335

4.  Meta-analysis of Randomized Controlled Trials of Genotype-Guided vs Standard Dosing of Warfarin.

Authors:  Khagendra Dahal; Sharan P Sharma; Erik Fung; Juyong Lee; Jason H Moore; John N Unterborn; Scott M Williams
Journal:  Chest       Date:  2015-09       Impact factor: 9.410

5.  Clinical benefits of pharmacogenetic algorithm-based warfarin dosing: meta-analysis of randomized controlled trials.

Authors:  Xiaoqi Li; Jie Yang; Xuyun Wang; Qiang Xu; Yuxiao Zhang; Tong Yin
Journal:  Thromb Res       Date:  2015-01-17       Impact factor: 3.944

6.  Pharmacogenetics-based warfarin dosing algorithm decreases time to stable anticoagulation and the risk of major hemorrhage: an updated meta-analysis of randomized controlled trials.

Authors:  Zhi-Quan Wang; Rui Zhang; Peng-Pai Zhang; Xiao-Hong Liu; Jian Sun; Jun Wang; Xiang-Fei Feng; Qiu-Fen Lu; Yi-Gang Li
Journal:  J Cardiovasc Pharmacol       Date:  2015-04       Impact factor: 3.105

7.  The largest prospective warfarin-treated cohort supports genetic forecasting.

Authors:  Mia Wadelius; Leslie Y Chen; Jonatan D Lindh; Niclas Eriksson; Mohammed J R Ghori; Suzannah Bumpstead; Lennart Holm; Ralph McGinnis; Anders Rane; Panos Deloukas
Journal:  Blood       Date:  2008-06-23       Impact factor: 22.113

8.  A method to determine the optimal intensity of oral anticoagulant therapy.

Authors:  F R Rosendaal; S C Cannegieter; F J van der Meer; E Briët
Journal:  Thromb Haemost       Date:  1993-03-01       Impact factor: 5.249

9.  A Randomized Trial of Pharmacogenetic Warfarin Dosing in Naïve Patients with Non-Valvular Atrial Fibrillation.

Authors:  Vittorio Pengo; Carlo-Federico Zambon; Paola Fogar; Andrea Padoan; Giovanni Nante; Michela Pelloso; Stefania Moz; Anna Chiara Frigo; Francesca Groppa; Dania Bozzato; Enrico Tiso; Elisa Gnatta; Gentian Denas; Seena Padayattil Jose; Roberto Padrini; Daniela Basso; Mario Plebani
Journal:  PLoS One       Date:  2015-12-28       Impact factor: 3.240

10.  Estimation of the warfarin dose with clinical and pharmacogenetic data.

Authors:  T E Klein; R B Altman; N Eriksson; B F Gage; S E Kimmel; M-T M Lee; N A Limdi; D Page; D M Roden; M J Wagner; M D Caldwell; J A Johnson
Journal:  N Engl J Med       Date:  2009-02-19       Impact factor: 91.245

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