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Antihypertensive Drugs Metabolism: An Update to Pharmacokinetic Profiles and Computational Approaches

Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics and nutritional supplements. Antihyperte...

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Autores principales: Zisaki, Aikaterini, Miskovic, Ljubisa, Hatzimanikatis, Vassily
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bentham Science Publishers 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4435036/
https://www.ncbi.nlm.nih.gov/pubmed/25341854
http://dx.doi.org/10.2174/1381612820666141024151119
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author Zisaki, Aikaterini
Miskovic, Ljubisa
Hatzimanikatis, Vassily
author_facet Zisaki, Aikaterini
Miskovic, Ljubisa
Hatzimanikatis, Vassily
author_sort Zisaki, Aikaterini
collection PubMed
description Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics and nutritional supplements. Antihypertensive drugs are used for the treatment of high blood pressure, which is one the most frequent symptoms of the patients that undergo cardiovascular diseases such as myocardial infraction and strokes. In current cardiovascular disease pharmacology, four drug clusters - Angiotensin Converting Enzyme Inhibitors, Beta-Blockers, Calcium Channel Blockers and Diuretics - cover the major therapeutic characteristics of the most antihypertensive drugs. The pharmacokinetic and specifically the metabolic profile of the antihypertensive agents are intensively studied because of the broad inter-individual variability on plasma concentrations and the diversity on the efficacy response especially due to the P450 dependent metabolic status they present. Several computational methods have been developed with the aim to: (i) model and better understand the human drug metabolism; and (ii) enhance the experimental investigation of the metabolism of small xenobiotic molecules. The main predictive tools these methods employ are rule-based approaches, quantitative structure metabolism/activity relationships and docking approaches. This review paper provides detailed metabolic profiles of the major clusters of antihypertensive agents, including their metabolites and their metabolizing enzymes, and it also provides specific information concerning the computational approaches that have been used to predict the metabolic profile of several antihypertensive drugs.
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spelling pubmed-44350362015-05-22 Antihypertensive Drugs Metabolism: An Update to Pharmacokinetic Profiles and Computational Approaches Zisaki, Aikaterini Miskovic, Ljubisa Hatzimanikatis, Vassily Curr Pharm Des Article Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics and nutritional supplements. Antihypertensive drugs are used for the treatment of high blood pressure, which is one the most frequent symptoms of the patients that undergo cardiovascular diseases such as myocardial infraction and strokes. In current cardiovascular disease pharmacology, four drug clusters - Angiotensin Converting Enzyme Inhibitors, Beta-Blockers, Calcium Channel Blockers and Diuretics - cover the major therapeutic characteristics of the most antihypertensive drugs. The pharmacokinetic and specifically the metabolic profile of the antihypertensive agents are intensively studied because of the broad inter-individual variability on plasma concentrations and the diversity on the efficacy response especially due to the P450 dependent metabolic status they present. Several computational methods have been developed with the aim to: (i) model and better understand the human drug metabolism; and (ii) enhance the experimental investigation of the metabolism of small xenobiotic molecules. The main predictive tools these methods employ are rule-based approaches, quantitative structure metabolism/activity relationships and docking approaches. This review paper provides detailed metabolic profiles of the major clusters of antihypertensive agents, including their metabolites and their metabolizing enzymes, and it also provides specific information concerning the computational approaches that have been used to predict the metabolic profile of several antihypertensive drugs. Bentham Science Publishers 2015-02 2015-02 /pmc/articles/PMC4435036/ /pubmed/25341854 http://dx.doi.org/10.2174/1381612820666141024151119 Text en © 2015 Bentham Science Publishers http://creativecommons.org/licenses/by-nc/3.0/ This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
spellingShingle Article
Zisaki, Aikaterini
Miskovic, Ljubisa
Hatzimanikatis, Vassily
Antihypertensive Drugs Metabolism: An Update to Pharmacokinetic Profiles and Computational Approaches
title Antihypertensive Drugs Metabolism: An Update to Pharmacokinetic Profiles and Computational Approaches
title_full Antihypertensive Drugs Metabolism: An Update to Pharmacokinetic Profiles and Computational Approaches
title_fullStr Antihypertensive Drugs Metabolism: An Update to Pharmacokinetic Profiles and Computational Approaches
title_full_unstemmed Antihypertensive Drugs Metabolism: An Update to Pharmacokinetic Profiles and Computational Approaches
title_short Antihypertensive Drugs Metabolism: An Update to Pharmacokinetic Profiles and Computational Approaches
title_sort antihypertensive drugs metabolism: an update to pharmacokinetic profiles and computational approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4435036/
https://www.ncbi.nlm.nih.gov/pubmed/25341854
http://dx.doi.org/10.2174/1381612820666141024151119
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