In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs

Detalhes bibliográficos
Autor(a) principal: Cruz, Sara
Data de Publicação: 2018
Outros Autores: Gomes, Sofia E., Borralho, Pedro M., Rodrigues, Cecília M. P., Gaudêncio, Susana P., Pereira, Florbela
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.3390/biom8030056
Resumo: Financial support from Fundacao para a Ciencia e Tecnologia (FCT) Portugal, under Project PTDC/QUIQUI/119116/2010 and grants SFRH/BPD/108237/2015 (F.P.) and IF/00700/2014 (S.P.G.) are greatly appreciated. This work was also supported by the LAQV and UCIBIO, which are financed by national funds from FCT/MEC (UID/QUI/50006/2013 and UID/Multi/04378/2013), respectively) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007265 and POCI-010145-FEDER-007728, respectively), and by Programme grant SAICTPAC/0019/2015 funded by European Structural and Investment Funds through the COMPETE Programme and by National Funds through FCT. The NMR spectrometers are part of The National NMR Facility, supported by FCT (RECI/BBB-BQB/0230/2012).
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spelling In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugsAnticancer activityHCT116 cell lineMachine learning (ML)Marine natural products (MNPs)Marine-derived actinobacteriaMolecular descriptorsNMR descriptorsQuantitative structure-Activity relationship (QSAR)BiochemistryMolecular BiologySDG 3 - Good Health and Well-beingSDG 14 - Life Below WaterFinancial support from Fundacao para a Ciencia e Tecnologia (FCT) Portugal, under Project PTDC/QUIQUI/119116/2010 and grants SFRH/BPD/108237/2015 (F.P.) and IF/00700/2014 (S.P.G.) are greatly appreciated. This work was also supported by the LAQV and UCIBIO, which are financed by national funds from FCT/MEC (UID/QUI/50006/2013 and UID/Multi/04378/2013), respectively) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007265 and POCI-010145-FEDER-007728, respectively), and by Programme grant SAICTPAC/0019/2015 funded by European Structural and Investment Funds through the COMPETE Programme and by National Funds through FCT. The NMR spectrometers are part of The National NMR Facility, supported by FCT (RECI/BBB-BQB/0230/2012).To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure–activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC50). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R2 of 0.75 and 0.73 and root mean square error (RMSE) of 0.66 and 0.69 for the training and test sets, respectively. With the inherent time-consuming efforts of working with natural products (NPs), we conceived a new NP drug hit discovery strategy that consists in frontloading samples with 1D NMR descriptors to predict compounds with anticancer activity prior to bioactivity screening for NPs discovery, approach B. The NMR QSAR classification models were built using 1D NMR data (1H and13C) as descriptors, from 50 crude extracts, 55 fractions and five pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 63% for both training and test sets.LAQV@REQUIMTEDQ - Departamento de QuímicaUCIBIO - Applied Molecular Biosciences UnitDCV - Departamento de Ciências da VidaRUNCruz, SaraGomes, Sofia E.Borralho, Pedro M.Rodrigues, Cecília M. P.Gaudêncio, Susana P.Pereira, Florbela2019-01-29T23:44:48Z2018-09-012018-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.3390/biom8030056eng2218-273XPURE: 5704499http://www.scopus.com/inward/record.url?scp=85050362659&partnerID=8YFLogxKhttps://doi.org/10.3390/biom8030056info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:28:22Zoai:run.unl.pt:10362/59003Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:33:19.378041Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs
title In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs
spellingShingle In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs
Cruz, Sara
Anticancer activity
HCT116 cell line
Machine learning (ML)
Marine natural products (MNPs)
Marine-derived actinobacteria
Molecular descriptors
NMR descriptors
Quantitative structure-Activity relationship (QSAR)
Biochemistry
Molecular Biology
SDG 3 - Good Health and Well-being
SDG 14 - Life Below Water
title_short In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs
title_full In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs
title_fullStr In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs
title_full_unstemmed In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs
title_sort In silico HCT116 human colon cancer cell-based models en route to the discovery of lead-like anticancer drugs
author Cruz, Sara
author_facet Cruz, Sara
Gomes, Sofia E.
Borralho, Pedro M.
Rodrigues, Cecília M. P.
Gaudêncio, Susana P.
Pereira, Florbela
author_role author
author2 Gomes, Sofia E.
Borralho, Pedro M.
Rodrigues, Cecília M. P.
Gaudêncio, Susana P.
Pereira, Florbela
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
DQ - Departamento de Química
UCIBIO - Applied Molecular Biosciences Unit
DCV - Departamento de Ciências da Vida
RUN
dc.contributor.author.fl_str_mv Cruz, Sara
Gomes, Sofia E.
Borralho, Pedro M.
Rodrigues, Cecília M. P.
Gaudêncio, Susana P.
Pereira, Florbela
dc.subject.por.fl_str_mv Anticancer activity
HCT116 cell line
Machine learning (ML)
Marine natural products (MNPs)
Marine-derived actinobacteria
Molecular descriptors
NMR descriptors
Quantitative structure-Activity relationship (QSAR)
Biochemistry
Molecular Biology
SDG 3 - Good Health and Well-being
SDG 14 - Life Below Water
topic Anticancer activity
HCT116 cell line
Machine learning (ML)
Marine natural products (MNPs)
Marine-derived actinobacteria
Molecular descriptors
NMR descriptors
Quantitative structure-Activity relationship (QSAR)
Biochemistry
Molecular Biology
SDG 3 - Good Health and Well-being
SDG 14 - Life Below Water
description Financial support from Fundacao para a Ciencia e Tecnologia (FCT) Portugal, under Project PTDC/QUIQUI/119116/2010 and grants SFRH/BPD/108237/2015 (F.P.) and IF/00700/2014 (S.P.G.) are greatly appreciated. This work was also supported by the LAQV and UCIBIO, which are financed by national funds from FCT/MEC (UID/QUI/50006/2013 and UID/Multi/04378/2013), respectively) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007265 and POCI-010145-FEDER-007728, respectively), and by Programme grant SAICTPAC/0019/2015 funded by European Structural and Investment Funds through the COMPETE Programme and by National Funds through FCT. The NMR spectrometers are part of The National NMR Facility, supported by FCT (RECI/BBB-BQB/0230/2012).
publishDate 2018
dc.date.none.fl_str_mv 2018-09-01
2018-09-01T00:00:00Z
2019-01-29T23:44:48Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.3390/biom8030056
url https://doi.org/10.3390/biom8030056
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2218-273X
PURE: 5704499
http://www.scopus.com/inward/record.url?scp=85050362659&partnerID=8YFLogxK
https://doi.org/10.3390/biom8030056
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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