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

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 fram...

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Autores principales:	Chao-Ting Cheng, Tsun-Yu Wang, Pei-Rong Chen, Wu-Hsiung Wu, Jin-Mei Lai, Peter Mu-Hsin Chang, Yi-Ren Hong, Chi-Ying F. Huang, Feng-Sheng Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	metabolite-centric target reaction-centric target fuzzy optimization two-target combination Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/de022faa0a5e41939696ea1bb0ac68a8
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spelling	oai:doaj.org-article:de022faa0a5e41939696ea1bb0ac68a82021-11-25T16:47:09ZComputer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer10.3390/biology101111152079-7737https://doaj.org/article/de022faa0a5e41939696ea1bb0ac68a82021-10-01T00:00:00Zhttps://www.mdpi.com/2079-7737/10/11/1115https://doaj.org/toc/2079-7737The 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>′</mo></msup></semantics></math></inline-formula>-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.Chao-Ting ChengTsun-Yu WangPei-Rong ChenWu-Hsiung WuJin-Mei LaiPeter Mu-Hsin ChangYi-Ren HongChi-Ying F. HuangFeng-Sheng WangMDPI AGarticlemetabolite-centric targetreaction-centric targetfuzzy optimizationtwo-target combinationBiology (General)QH301-705.5ENBiology, Vol 10, Iss 1115, p 1115 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	metabolite-centric target reaction-centric target fuzzy optimization two-target combination Biology (General) QH301-705.5
spellingShingle	metabolite-centric target reaction-centric target fuzzy optimization two-target combination Biology (General) QH301-705.5 Chao-Ting Cheng Tsun-Yu Wang Pei-Rong Chen Wu-Hsiung Wu Jin-Mei Lai Peter Mu-Hsin Chang Yi-Ren Hong Chi-Ying F. Huang Feng-Sheng Wang Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer
description	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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>′</mo></msup></semantics></math></inline-formula>-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.
format	article
author	Chao-Ting Cheng Tsun-Yu Wang Pei-Rong Chen Wu-Hsiung Wu Jin-Mei Lai Peter Mu-Hsin Chang Yi-Ren Hong Chi-Ying F. Huang Feng-Sheng Wang
author_facet	Chao-Ting Cheng Tsun-Yu Wang Pei-Rong Chen Wu-Hsiung Wu Jin-Mei Lai Peter Mu-Hsin Chang Yi-Ren Hong Chi-Ying F. Huang Feng-Sheng Wang
author_sort	Chao-Ting Cheng
title	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_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_sort	computer-aided design for identifying anticancer targets in genome-scale metabolic models of colon cancer
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/de022faa0a5e41939696ea1bb0ac68a8
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Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer

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