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Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer

SIMPLE SUMMARY: Discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also met...

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Autores principales: Cheng, Chao-Ting, Wang, Tsun-Yu, Chen, Pei-Rong, Wu, Wu-Hsiung, Lai, Jin-Mei, Chang, Peter Mu-Hsin, Hong, Yi-Ren, Huang, Chi-Ying F., Wang, Feng-Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614794/
https://www.ncbi.nlm.nih.gov/pubmed/34827109
http://dx.doi.org/10.3390/biology10111115
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author Cheng, Chao-Ting
Wang, Tsun-Yu
Chen, Pei-Rong
Wu, Wu-Hsiung
Lai, Jin-Mei
Chang, Peter Mu-Hsin
Hong, Yi-Ren
Huang, Chi-Ying F.
Wang, Feng-Sheng
author_facet Cheng, Chao-Ting
Wang, Tsun-Yu
Chen, Pei-Rong
Wu, Wu-Hsiung
Lai, Jin-Mei
Chang, Peter Mu-Hsin
Hong, Yi-Ren
Huang, Chi-Ying F.
Wang, Feng-Sheng
author_sort Cheng, Chao-Ting
collection PubMed
description SIMPLE SUMMARY: Discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells. ABSTRACT: The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5 [Formula: see text]-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.
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spelling pubmed-86147942021-11-26 Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer Cheng, Chao-Ting Wang, Tsun-Yu Chen, Pei-Rong Wu, Wu-Hsiung Lai, Jin-Mei Chang, Peter Mu-Hsin Hong, Yi-Ren Huang, Chi-Ying F. Wang, Feng-Sheng Biology (Basel) Article SIMPLE SUMMARY: Discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells. ABSTRACT: The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5 [Formula: see text]-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells. MDPI 2021-10-29 /pmc/articles/PMC8614794/ /pubmed/34827109 http://dx.doi.org/10.3390/biology10111115 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cheng, Chao-Ting
Wang, Tsun-Yu
Chen, Pei-Rong
Wu, Wu-Hsiung
Lai, Jin-Mei
Chang, Peter Mu-Hsin
Hong, Yi-Ren
Huang, Chi-Ying F.
Wang, Feng-Sheng
Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer
title Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer
title_full Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer
title_fullStr Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer
title_full_unstemmed Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer
title_short Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer
title_sort computer-aided design for identifying anticancer targets in genome-scale metabolic models of colon cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614794/
https://www.ncbi.nlm.nih.gov/pubmed/34827109
http://dx.doi.org/10.3390/biology10111115
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