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DeCoST: A New Approach in Drug Repurposing From Control System Theory
In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repur...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996185/ https://www.ncbi.nlm.nih.gov/pubmed/29922160 http://dx.doi.org/10.3389/fphar.2018.00583 |
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author | Nguyen, Thanh M. Muhammad, Syed A. Ibrahim, Sara Ma, Lin Guo, Jinlei Bai, Baogang Zeng, Bixin |
author_facet | Nguyen, Thanh M. Muhammad, Syed A. Ibrahim, Sara Ma, Lin Guo, Jinlei Bai, Baogang Zeng, Bixin |
author_sort | Nguyen, Thanh M. |
collection | PubMed |
description | In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from “one: disease-gene-drug” to “multi: gene, dru” and from “lazy guilt-by-association” to “systematic model-based pattern matching,” mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear–quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.). |
format | Online Article Text |
id | pubmed-5996185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-59961852018-06-19 DeCoST: A New Approach in Drug Repurposing From Control System Theory Nguyen, Thanh M. Muhammad, Syed A. Ibrahim, Sara Ma, Lin Guo, Jinlei Bai, Baogang Zeng, Bixin Front Pharmacol Pharmacology In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from “one: disease-gene-drug” to “multi: gene, dru” and from “lazy guilt-by-association” to “systematic model-based pattern matching,” mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear–quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.). Frontiers Media S.A. 2018-06-05 /pmc/articles/PMC5996185/ /pubmed/29922160 http://dx.doi.org/10.3389/fphar.2018.00583 Text en Copyright © 2018 Nguyen, Muhammad, Ibrahim, Ma, Guo, Bai and Zeng. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Nguyen, Thanh M. Muhammad, Syed A. Ibrahim, Sara Ma, Lin Guo, Jinlei Bai, Baogang Zeng, Bixin DeCoST: A New Approach in Drug Repurposing From Control System Theory |
title | DeCoST: A New Approach in Drug Repurposing From Control System Theory |
title_full | DeCoST: A New Approach in Drug Repurposing From Control System Theory |
title_fullStr | DeCoST: A New Approach in Drug Repurposing From Control System Theory |
title_full_unstemmed | DeCoST: A New Approach in Drug Repurposing From Control System Theory |
title_short | DeCoST: A New Approach in Drug Repurposing From Control System Theory |
title_sort | decost: a new approach in drug repurposing from control system theory |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996185/ https://www.ncbi.nlm.nih.gov/pubmed/29922160 http://dx.doi.org/10.3389/fphar.2018.00583 |
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