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An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice
The study of the magnitude and variation of drug response is defined as pharmacodynamics (PDs). PD models examine plasma concentration and effect relationship. It can predict the archetypal effect ([Formula: see text]) of a drug as a function of the drug concentration ([Formula: see text]) and estim...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355565/ https://www.ncbi.nlm.nih.gov/pubmed/28344466 http://dx.doi.org/10.1016/j.jsps.2016.07.002 |
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author | Salahudeen, Mohammed Saji Nishtala, Prasad S. |
author_facet | Salahudeen, Mohammed Saji Nishtala, Prasad S. |
author_sort | Salahudeen, Mohammed Saji |
collection | PubMed |
description | The study of the magnitude and variation of drug response is defined as pharmacodynamics (PDs). PD models examine plasma concentration and effect relationship. It can predict the archetypal effect ([Formula: see text]) of a drug as a function of the drug concentration ([Formula: see text]) and estimate an unknown PD parameter ([Formula: see text]). The PD models have been described as fixed, linear, log-linear, [Formula: see text] , sigmoid [Formula: see text] , and indirect PD response. Ligand binding model is an example of a PD model that works on the underpinning PD principle of a drug, eliciting its pharmacological effect at the receptor site. The pharmacological effect is produced by the drug binding to the receptor to either activate or antagonise the receptor. Ligand binding models describe a system of interacting components, i.e. the interaction of one or more ligands with one or more binding sites. The [Formula: see text] model is the central method that provides an empirical justification for the concentration/dose-effect relationship. However, for ligand binding models justification is provided by theory of receptor occupancy. In essence, for ligand binding models, the term [Formula: see text] is best used to describe the fraction of receptors occupied at a particular ligand concentration. It is stated that the [Formula: see text] , which means the effect of a drug should depend on the fraction of receptors that are occupied. In the future, network-based systems pharmacology models using ligand binding principles could be an effective way of understanding drug-related adverse effects. This will facilitate and strengthen the development of rational drug therapy in clinical practice. |
format | Online Article Text |
id | pubmed-5355565 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-53555652017-03-24 An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice Salahudeen, Mohammed Saji Nishtala, Prasad S. Saudi Pharm J Review The study of the magnitude and variation of drug response is defined as pharmacodynamics (PDs). PD models examine plasma concentration and effect relationship. It can predict the archetypal effect ([Formula: see text]) of a drug as a function of the drug concentration ([Formula: see text]) and estimate an unknown PD parameter ([Formula: see text]). The PD models have been described as fixed, linear, log-linear, [Formula: see text] , sigmoid [Formula: see text] , and indirect PD response. Ligand binding model is an example of a PD model that works on the underpinning PD principle of a drug, eliciting its pharmacological effect at the receptor site. The pharmacological effect is produced by the drug binding to the receptor to either activate or antagonise the receptor. Ligand binding models describe a system of interacting components, i.e. the interaction of one or more ligands with one or more binding sites. The [Formula: see text] model is the central method that provides an empirical justification for the concentration/dose-effect relationship. However, for ligand binding models justification is provided by theory of receptor occupancy. In essence, for ligand binding models, the term [Formula: see text] is best used to describe the fraction of receptors occupied at a particular ligand concentration. It is stated that the [Formula: see text] , which means the effect of a drug should depend on the fraction of receptors that are occupied. In the future, network-based systems pharmacology models using ligand binding principles could be an effective way of understanding drug-related adverse effects. This will facilitate and strengthen the development of rational drug therapy in clinical practice. Elsevier 2017-02 2016-07-09 /pmc/articles/PMC5355565/ /pubmed/28344466 http://dx.doi.org/10.1016/j.jsps.2016.07.002 Text en © 2016 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Review Salahudeen, Mohammed Saji Nishtala, Prasad S. An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice |
title | An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice |
title_full | An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice |
title_fullStr | An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice |
title_full_unstemmed | An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice |
title_short | An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice |
title_sort | overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355565/ https://www.ncbi.nlm.nih.gov/pubmed/28344466 http://dx.doi.org/10.1016/j.jsps.2016.07.002 |
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