Structural findings of phenylindoles as cytotoxic antimitotic agents in human breast cancer cell lines through multiple validated QSAR studies
Copyright © 2015 Elsevier Ltd. All rights reserved..
Antimitotic agents are potential compounds for the treatment of breast cancer. Cytotoxicity is one of the parameters required for anticancer activity. A validated comparative molecular modeling study was performed on a set of phenylindole derivatives through R-group QSAR (RQSAR), regression-based and linear discriminant analysis (LDA)-based 2D QSAR studies and kernel-based partial least square (KPLS) analyses as well as CoMSIA 3D-QSAR study. Antiproliferative activities against two breast cancer cell lines (MDA-MB-231 and MCF7) were separately used as dependent variables. The RQSAR analysis highlighted different E-state indices and pharmacophoric requirements of important substitutions. The best 2D-QSAR model is established on the basis of three machine learning tools – MLR, SVM and ANN. The 2D-QSAR models depicted importance of different structural, physicochemical and topological descriptors. While RQSAR analyses demonstrated the fingerprint requirements of various substitutions, the KPLS analyses showed these requirements for the entire molecule. The CoMSIA model further refines these interpretations and reveals how subtle variations in these structures may influence biological activities. Observations of different modeling techniques complied with each other. The current QSAR study may be used to design potential antimitotic agents. It also demonstrates the utilities of different molecular modeling tools to elucidate the SAR.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2015 |
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| Erschienen: |
2015 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
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| Enthalten in: |
Toxicology in vitro : an international journal published in association with BIBRA - 29(2015), 7 vom: 01. Okt., Seite 1392-404 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Adhikari, Nilanjan [VerfasserIn] |
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| Links: |
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| Themen: |
2D-QSAR |
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| Anmerkungen: |
Date Completed 07.06.2016 Date Revised 10.12.2019 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.tiv.2015.05.017 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM24949339X |
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| 520 | |a Copyright © 2015 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Antimitotic agents are potential compounds for the treatment of breast cancer. Cytotoxicity is one of the parameters required for anticancer activity. A validated comparative molecular modeling study was performed on a set of phenylindole derivatives through R-group QSAR (RQSAR), regression-based and linear discriminant analysis (LDA)-based 2D QSAR studies and kernel-based partial least square (KPLS) analyses as well as CoMSIA 3D-QSAR study. Antiproliferative activities against two breast cancer cell lines (MDA-MB-231 and MCF7) were separately used as dependent variables. The RQSAR analysis highlighted different E-state indices and pharmacophoric requirements of important substitutions. The best 2D-QSAR model is established on the basis of three machine learning tools – MLR, SVM and ANN. The 2D-QSAR models depicted importance of different structural, physicochemical and topological descriptors. While RQSAR analyses demonstrated the fingerprint requirements of various substitutions, the KPLS analyses showed these requirements for the entire molecule. The CoMSIA model further refines these interpretations and reveals how subtle variations in these structures may influence biological activities. Observations of different modeling techniques complied with each other. The current QSAR study may be used to design potential antimitotic agents. It also demonstrates the utilities of different molecular modeling tools to elucidate the SAR | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a 2D-QSAR | |
| 650 | 4 | |a Antimitotic agents | |
| 650 | 4 | |a CoMSIA | |
| 650 | 4 | |a Kernel-based PLS | |
| 650 | 4 | |a R-group QSAR | |
| 650 | 4 | |a Support vector machine | |
| 650 | 7 | |a Antimitotic Agents |2 NLM | |
| 650 | 7 | |a Cytotoxins |2 NLM | |
| 650 | 7 | |a Indoles |2 NLM | |
| 700 | 1 | |a Halder, Amit Kumar |e verfasserin |4 aut | |
| 700 | 1 | |a Saha, Achintya |e verfasserin |4 aut | |
| 700 | 1 | |a Das Saha, Krishna |e verfasserin |4 aut | |
| 700 | 1 | |a Jha, Tarun |e verfasserin |4 aut | |
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