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Synergistic Interaction between Selective Drugs in Cell Populations Models

The design of selective drugs and combinatorial drug treatments are two of the main focuses in modern pharmacology. In this study we use a mathematical model of chimeric ligand-receptor interaction to show that the combination of selective drugs is synergistic in nature, providing a way to gain opti...

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Detalles Bibliográficos
Autores principales: Doldán-Martelli, Victoria, Míguez, David G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4324767/
https://www.ncbi.nlm.nih.gov/pubmed/25671700
http://dx.doi.org/10.1371/journal.pone.0117558
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author Doldán-Martelli, Victoria
Míguez, David G.
author_facet Doldán-Martelli, Victoria
Míguez, David G.
author_sort Doldán-Martelli, Victoria
collection PubMed
description The design of selective drugs and combinatorial drug treatments are two of the main focuses in modern pharmacology. In this study we use a mathematical model of chimeric ligand-receptor interaction to show that the combination of selective drugs is synergistic in nature, providing a way to gain optimal selective potential at reduced doses compared to the same drugs when applied individually. We use a cell population model of proliferating cells expressing two different amounts of a target protein to show that both selectivity and synergism are robust against variability and heritability in the cell population. The reduction in the total drug administered due to the synergistic performance of the selective drugs can potentially result in reduced toxicity and off-target interactions, providing a mechanism to improve the treatment of cell-based diseases caused by aberrant gene overexpression, such as cancer and diabetes.
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spelling pubmed-43247672015-02-18 Synergistic Interaction between Selective Drugs in Cell Populations Models Doldán-Martelli, Victoria Míguez, David G. PLoS One Research Article The design of selective drugs and combinatorial drug treatments are two of the main focuses in modern pharmacology. In this study we use a mathematical model of chimeric ligand-receptor interaction to show that the combination of selective drugs is synergistic in nature, providing a way to gain optimal selective potential at reduced doses compared to the same drugs when applied individually. We use a cell population model of proliferating cells expressing two different amounts of a target protein to show that both selectivity and synergism are robust against variability and heritability in the cell population. The reduction in the total drug administered due to the synergistic performance of the selective drugs can potentially result in reduced toxicity and off-target interactions, providing a mechanism to improve the treatment of cell-based diseases caused by aberrant gene overexpression, such as cancer and diabetes. Public Library of Science 2015-02-11 /pmc/articles/PMC4324767/ /pubmed/25671700 http://dx.doi.org/10.1371/journal.pone.0117558 Text en © 2015 Doldán-Martelli, Míguez 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
Doldán-Martelli, Victoria
Míguez, David G.
Synergistic Interaction between Selective Drugs in Cell Populations Models
title Synergistic Interaction between Selective Drugs in Cell Populations Models
title_full Synergistic Interaction between Selective Drugs in Cell Populations Models
title_fullStr Synergistic Interaction between Selective Drugs in Cell Populations Models
title_full_unstemmed Synergistic Interaction between Selective Drugs in Cell Populations Models
title_short Synergistic Interaction between Selective Drugs in Cell Populations Models
title_sort synergistic interaction between selective drugs in cell populations models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4324767/
https://www.ncbi.nlm.nih.gov/pubmed/25671700
http://dx.doi.org/10.1371/journal.pone.0117558
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