Repository logo
 
Publication

Sorting hidden patterns in nanoparticle performance for glioblastoma using machine learning algorithms

2021-01Journal article Open access
Simple item page
dc.contributor.authorBasso, João
dc.contributor.authorMendes, Maria
dc.contributor.authorSilva, Jessica
dc.contributor.authorCova, Tânia
dc.contributor.authorLuque-Michel, Edurne
dc.contributor.authorJorge, Andreia F.
dc.contributor.authorGrijalvo, Santiago
dc.contributor.authorGonçalves, Lídia
dc.contributor.authorEritja, Ramon
dc.contributor.authorBlanco-Prieto, María J.
dc.contributor.authorAlmeida, António José
dc.contributor.authorPais, Alberto
dc.contributor.authorVitorino, Carla
dc.date.accessioned2025-08-14T19:07:31Z
dc.date.available2025-08-14T19:07:31Z
dc.date.issued2021-01
dc.date.updated2023-02-27T16:01:29Z
dc.description.abstractCationic compounds have been described to readily penetrate cell membranes. Assigning positive charge to nanosystems, e.g. lipid nanoparticles, has been identified as a key feature to promote electrostatic binding and design ligand-based constructs for tumour targeting. However, their intrinsic high cytotoxicity has hampered their biomedical application. This paper seeks to establish which cationic compounds and properties are compelling for interface modulation, in order to improve the design of tumour targeted nanoparticles against glioblastoma. How can intrinsic features (e.g. nature, structure, conformation) shape efficacy outcomes? In the quest for safer alternative cationic compounds, we evaluate the effects of two novel glycerol-based lipids, GLY1 and GLY2, on the architecture and performance of nanostructured lipid carriers (NLCs). These two molecules, composed of two alkylated chains and a glycerol backbone, differ only in their polar head and proved to be efficient in reversing the zeta potential of the nanosystems to positive values. The use of unsupervised and supervised machine learning (ML) techniques unraveled their structural similarities: in spite of their common backbone, GLY1 exhibited a better performance in increasing zeta potential and cytotoxicity, while decreasing particle size. Furthermore, NLCs containing GLY1 showed a favorable hemocompatible profile, as well as an improved uptake by tumour cells. Summing-up, GLY1 circumvents the intrinsic cytotoxicity of a common surfactant, CTAB, is effective at increasing glioblastoma uptake, and exhibits encouraging anticancer activity. Moreover, the use of ML is strongly incited for formulation design and optimization.pt_PT
dc.description.sponsorshipThe authors acknowledge Fundação para a Ciência e a Tecnologia (FCT), the Portuguese Agency for Scientific Research for the financial support through the projects POCI-01-0145-FEDER-016648, PEst-UID/NEU/04539/2013 and COMPETE (Ref. POCI-01-0145-FEDER-007440). FCT also supports the Coimbra Chemistry Centre (UIDP/00313/2020) and iMed.ULisboa (UID/DTP/04138/2019). Tania Cova the Junior Researcher Grant CEECIND/00915/2018 both assigned by FCT. João Basso and Maria Mendes acknowledge the PhD research Grants SFRH/BD/149138/2019 and SFRH/BD/133996/2017, respectively, assigned by FCT.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.citationBasso J, Mendes M, Silva J, Cova T, Luque-Michel E, Jorge AF, et al. Sorting hidden patterns in nanoparticle performance for glioblastoma using machine learning algorithms. International Journal of Pharmaceutics [Internet]. 5 de janeiro de 2021;592:120095. Disponível em: https://www.sciencedirect.com/science/article/pii/S0378517320310802pt_PT
dc.identifier.doi10.1016/j.ijpharm.2020.120095pt_PT
dc.identifier.issn0378-5173
dc.identifier.slugcv-prod-2118916
dc.identifier.urihttp://hdl.handle.net/10400.5/102920
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationPOCI-01-0145-FEDER-016648pt_PT
dc.relationCNC. IBILI
dc.relationCoimbra Chemistry Center
dc.relationResearch Institute for Medicines
dc.relationNot Available
dc.relationBiohybrid Nanosystems for Drug and Nucleic Acid co-Delivery in Glioblastoma
dc.relationLipid nanoparticles as a multifunctional platform for brain tumor therapy
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0378517320310802pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectGlycerol-based cationic lipidspt_PT
dc.subjectGlioblastomapt_PT
dc.subjectMachine learningpt_PT
dc.subjectNanostructured lipid carrierspt_PT
dc.subjectNeural networkspt_PT
dc.titleSorting hidden patterns in nanoparticle performance for glioblastoma using machine learning algorithmspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCNC. IBILI
oaire.awardTitleCoimbra Chemistry Center
oaire.awardTitleResearch Institute for Medicines
oaire.awardTitleNot Available
oaire.awardTitleBiohybrid Nanosystems for Drug and Nucleic Acid co-Delivery in Glioblastoma
oaire.awardTitleLipid nanoparticles as a multifunctional platform for brain tumor therapy
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FNEU%2F04539%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00313%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FDTP%2F04138%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC IND 2018/CEECIND%2F00915%2F2018%2FCP1585%2FCT0005/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F149138%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/POR_CENTRO/SFRH%2FBD%2F133996%2F2017/PT
oaire.citation.startPage120095pt_PT
oaire.citation.titleInternational Journal of Pharmaceuticspt_PT
oaire.citation.volume592pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamCEEC IND 2018
oaire.fundingStreamPOR_CENTRO
person.familyNameDiogo Gonçalves
person.familyNameLeitão das Neves Almeida
person.givenNameLídia Maria
person.givenNameAntónio José
person.identifierI-6590-2012
person.identifier.ciencia-id7211-22BA-86AD
person.identifier.ciencia-id8312-F853-C47A
person.identifier.orcid0000-0002-6799-2740
person.identifier.orcid0000-0002-7807-4726
person.identifier.scopus-author-id57195052432
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaid7211-22BA-86AD | Lídia Maria Diogo Gonçalves
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication3265148d-dc2d-4601-a832-2e72f7be5662
relation.isAuthorOfPublicationdab457ee-7a1d-4c3d-a906-1e8df92fffd6
relation.isAuthorOfPublication.latestForDiscovery3265148d-dc2d-4601-a832-2e72f7be5662
relation.isProjectOfPublication0e50d086-63ad-4636-8fdf-c202b25a3c0c
relation.isProjectOfPublication9b023c49-b1f5-449b-879e-bd1c8f394cbc
relation.isProjectOfPublicationf7ed2a0a-c67e-4c12-ad63-b550724e24bf
relation.isProjectOfPublication534a7d05-ef42-4cb6-b131-f5523719fa97
relation.isProjectOfPublicationf39e37d0-b0ac-45fa-9224-9d4799468193
relation.isProjectOfPublicationa21a9ce2-89c2-4ab9-ba59-f7d0c4250a47
relation.isProjectOfPublication.latestForDiscoverya21a9ce2-89c2-4ab9-ba59-f7d0c4250a47

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Basso_et_al_2020.pdf
Size:
1.59 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.2 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

FF - CiênciaVitae - Faculdade de Farmácia