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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

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Date Issued:
2007-08-30
Title: Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies.
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Name(s): Liebovitch, Larry S., creator
Tsinoremas, Nicholas, creator
Pandya, Abhijit S., creator
Type of Resource: text
Genre: Article
Issuance: single unit
Date Issued: 2007-08-30
Publisher: BioMed Central Ltd.
Language(s): English
Identifier: 165790 (digitool), FADT165790 (IID), fau:2611 (fedora), 10.1186/1753-4631-1-11 (doi)
FAU Department/College: Department of Psychology Charles E. Schmidt College of Science
Note(s): 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. The authors 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, the research shows that an artificial neural network can accurately predict the effect of all 215 = 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.
This manuscript is a version of an article published in Nonlinear Biomedial Physics v. 1, no.11 (2007) http://www.nonlinearbiomedphys.com/content/1/1/11
Subject(s): Drug Therapy
Biomathematics
Combinatorial dynamics
Drugs--Research
Medicine-Mathematics
Biophysics--Research
Nonlinear systems
Persistent Link to This Record: http://purl.flvc.org/fau/165790
Links: http://dx.doi.org/10.1186/1753-4631-1-11
Restrictions on Access: ©2007 Liebovitch et al; licensee BioMed Central Ltd.
Host Institution: FAU

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