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Therapeutic Dosing of Acenocoumarol: Proposal of a Population Specific Pharmacogenetic Dosing Algorithm and Its Validation in North Indians

OBJECTIVES: To develop a population specific pharmacogenetic acenocoumarol dosing algorithm for north Indian patients and show its efficiency in dosage prediction. METHODS: Multiple and linear stepwise regression analyses were used to include age, sex, height, weight, body surface area, smoking stat...

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Autores principales: Rathore, Saurabh Singh, Agarwal, Surendra Kumar, Pande, Shantanu, Singh, Sushil Kumar, Mittal, Tulika, Mittal, Balraj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3358293/
https://www.ncbi.nlm.nih.gov/pubmed/22629463
http://dx.doi.org/10.1371/journal.pone.0037844
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author Rathore, Saurabh Singh
Agarwal, Surendra Kumar
Pande, Shantanu
Singh, Sushil Kumar
Mittal, Tulika
Mittal, Balraj
author_facet Rathore, Saurabh Singh
Agarwal, Surendra Kumar
Pande, Shantanu
Singh, Sushil Kumar
Mittal, Tulika
Mittal, Balraj
author_sort Rathore, Saurabh Singh
collection PubMed
description OBJECTIVES: To develop a population specific pharmacogenetic acenocoumarol dosing algorithm for north Indian patients and show its efficiency in dosage prediction. METHODS: Multiple and linear stepwise regression analyses were used to include age, sex, height, weight, body surface area, smoking status, VKORC1 -1639 G>A, CYP4F2 1347 G>A, CYP2C9*2,*3 and GGCX 12970 C>G polymorphisms as variables to generate dosing algorithms. The new dosing models were compared with already reported algorithms and also with the clinical data for various performance measures. Odds ratios for association of genotypes with drug sensitive and resistant groups were calculated. RESULTS: The pharmacogenetic dosing algorithm generated by multiple regression analysis explains 41.4% (p-value <0.001) of dosage variation. Validation of the new algorithm showed its predictive ability to be better than the already established algorithms based on similar variables. Its validity in our population is reflected by increased sensitivity, specificity, accuracy and decreased rates of over- and under- estimation in comparison to clinical data. The VKORC1-1639 G>A polymorphism was found to be strongly associated with acenocoumarol sensitivity according to recessive model. CONCLUSIONS: We have proposed an efficient north India specific pharmacogenetic acenocoumarol dosing algorithm which might become a baseline for personalised medicine approach for treatment of patients in future.
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spelling pubmed-33582932012-05-24 Therapeutic Dosing of Acenocoumarol: Proposal of a Population Specific Pharmacogenetic Dosing Algorithm and Its Validation in North Indians Rathore, Saurabh Singh Agarwal, Surendra Kumar Pande, Shantanu Singh, Sushil Kumar Mittal, Tulika Mittal, Balraj PLoS One Research Article OBJECTIVES: To develop a population specific pharmacogenetic acenocoumarol dosing algorithm for north Indian patients and show its efficiency in dosage prediction. METHODS: Multiple and linear stepwise regression analyses were used to include age, sex, height, weight, body surface area, smoking status, VKORC1 -1639 G>A, CYP4F2 1347 G>A, CYP2C9*2,*3 and GGCX 12970 C>G polymorphisms as variables to generate dosing algorithms. The new dosing models were compared with already reported algorithms and also with the clinical data for various performance measures. Odds ratios for association of genotypes with drug sensitive and resistant groups were calculated. RESULTS: The pharmacogenetic dosing algorithm generated by multiple regression analysis explains 41.4% (p-value <0.001) of dosage variation. Validation of the new algorithm showed its predictive ability to be better than the already established algorithms based on similar variables. Its validity in our population is reflected by increased sensitivity, specificity, accuracy and decreased rates of over- and under- estimation in comparison to clinical data. The VKORC1-1639 G>A polymorphism was found to be strongly associated with acenocoumarol sensitivity according to recessive model. CONCLUSIONS: We have proposed an efficient north India specific pharmacogenetic acenocoumarol dosing algorithm which might become a baseline for personalised medicine approach for treatment of patients in future. Public Library of Science 2012-05-22 /pmc/articles/PMC3358293/ /pubmed/22629463 http://dx.doi.org/10.1371/journal.pone.0037844 Text en Rathore et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Rathore, Saurabh Singh
Agarwal, Surendra Kumar
Pande, Shantanu
Singh, Sushil Kumar
Mittal, Tulika
Mittal, Balraj
Therapeutic Dosing of Acenocoumarol: Proposal of a Population Specific Pharmacogenetic Dosing Algorithm and Its Validation in North Indians
title Therapeutic Dosing of Acenocoumarol: Proposal of a Population Specific Pharmacogenetic Dosing Algorithm and Its Validation in North Indians
title_full Therapeutic Dosing of Acenocoumarol: Proposal of a Population Specific Pharmacogenetic Dosing Algorithm and Its Validation in North Indians
title_fullStr Therapeutic Dosing of Acenocoumarol: Proposal of a Population Specific Pharmacogenetic Dosing Algorithm and Its Validation in North Indians
title_full_unstemmed Therapeutic Dosing of Acenocoumarol: Proposal of a Population Specific Pharmacogenetic Dosing Algorithm and Its Validation in North Indians
title_short Therapeutic Dosing of Acenocoumarol: Proposal of a Population Specific Pharmacogenetic Dosing Algorithm and Its Validation in North Indians
title_sort therapeutic dosing of acenocoumarol: proposal of a population specific pharmacogenetic dosing algorithm and its validation in north indians
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3358293/
https://www.ncbi.nlm.nih.gov/pubmed/22629463
http://dx.doi.org/10.1371/journal.pone.0037844
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