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Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies
Drugs designed for a specific target are always found to have multiple effects. Rather than hope that one bullet can be designed to hit only one target, nonlinear interactions across genomic and proteomic networks could be used to design Combinatorial Multi-Component Therapies (CMCT) that are more t...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2008194/ https://www.ncbi.nlm.nih.gov/pubmed/17908289 http://dx.doi.org/10.1186/1753-4631-1-11 |
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author | Liebovitch, Larry S Tsinoremas, Nicholas Pandya, Abhijit |
author_facet | Liebovitch, Larry S Tsinoremas, Nicholas Pandya, Abhijit |
author_sort | Liebovitch, Larry S |
collection | PubMed |
description | Drugs designed for a specific target are always found to have multiple effects. Rather than hope that one bullet can be designed to hit only one target, nonlinear interactions across genomic and proteomic networks could be used to design Combinatorial Multi-Component Therapies (CMCT) that are more targeted with fewer side effects. We show here how computational approaches can be used to predict which combinations of drugs would produce the best effects. Using a nonlinear model of how the output effect depends on multiple input drugs, we show that an artificial neural network can accurately predict the effect of all 2(15 )= 32,768 combinations of drug inputs using only the limited data of the output effect of the drugs presented one-at-a-time and pairs-at-a-time. |
format | Text |
id | pubmed-2008194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-20081942007-10-10 Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies Liebovitch, Larry S Tsinoremas, Nicholas Pandya, Abhijit Nonlinear Biomed Phys Research Drugs designed for a specific target are always found to have multiple effects. Rather than hope that one bullet can be designed to hit only one target, nonlinear interactions across genomic and proteomic networks could be used to design Combinatorial Multi-Component Therapies (CMCT) that are more targeted with fewer side effects. We show here how computational approaches can be used to predict which combinations of drugs would produce the best effects. Using a nonlinear model of how the output effect depends on multiple input drugs, we show that an artificial neural network can accurately predict the effect of all 2(15 )= 32,768 combinations of drug inputs using only the limited data of the output effect of the drugs presented one-at-a-time and pairs-at-a-time. BioMed Central 2007-08-30 /pmc/articles/PMC2008194/ /pubmed/17908289 http://dx.doi.org/10.1186/1753-4631-1-11 Text en Copyright © 2007 Liebovitch et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Liebovitch, Larry S Tsinoremas, Nicholas Pandya, Abhijit Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies |
title | Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies |
title_full | Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies |
title_fullStr | Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies |
title_full_unstemmed | Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies |
title_short | Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies |
title_sort | developing combinatorial multi-component therapies (cmct) of drugs that are more specific and have fewer side effects than traditional one drug therapies |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2008194/ https://www.ncbi.nlm.nih.gov/pubmed/17908289 http://dx.doi.org/10.1186/1753-4631-1-11 |
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