Publication:
Parameters influencing gene delivery efficiency of PEGylated chitosan nanoparticles: experimental and modeling approach

dc.contributor.coauthorBozüyük, Uğur
dc.contributor.coauthorErkoç, Pelin
dc.contributor.coauthorKaracakol, Alp Can
dc.contributor.departmentDepartment of Mechanical Engineering
dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.departmentSchool of Medicine
dc.contributor.kuauthorCingöz, Ahmet
dc.contributor.kuauthorDoğan, Nihal Olcay
dc.contributor.kuauthorKızılel, Seda
dc.contributor.kuauthorNazeer, Muhammad Anwaar
dc.contributor.kuauthorÖnder, Tuğba Bağcı
dc.contributor.kuauthorŞeker-Polat, Fidan
dc.contributor.kuauthorSitti, Metin
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteSCHOOL OF MEDICINE
dc.date.accessioned2024-11-09T12:26:51Z
dc.date.issued2022
dc.description.abstractExperimentation of nanomedicine is labor-intensive, time-consuming, and requires costly laboratory consumables. Constructing a reliable mathematical model for such systems is also challenging due to the difficulties in gathering a sufficient number of data points. Artificial neural networks (ANNs) are indicated as an efficient approach in nanomedicine to investigate the cause-effect relationships and predict output variables. Herein, an ANN is adapted into plasmid DNA (pDNA) encapsulated and PEGylated chitosan nanoparticles cross-linked with sodium tripolyphosphate (TPP) to investigate the effects of critical parameters on the transfection efficiencies of nanoparticles. The ANN model is developed based on experimental results with three independent input variables: 1) polyethylene glycol (PEG) molecular weight, 2) PEG concentration, and 3) nanoparticle concentration, along with one output variable as a percentage of green fluorescent protein (GFP) expression, which refers to transfection efficiency. The constructed model is further validated with the leave-p-out cross-validation method. The results indicate that the developed model has good prediction capability and is influential in capturing the transfection efficiencies of different nanoparticle groups. Overall, this study reveals that the ANN could be an efficient tool for nanoparticle-mediated gene delivery systems to investigate the impacts of critical parameters in detail with reduced experimental effort and cost.
dc.description.fulltextYES
dc.description.indexedbyWOS
dc.description.issue1
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuEU - TÜBİTAK
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TÜBİTAK)
dc.description.sponsorshipEuropean Union (EU)
dc.description.sponsorshipInternational Support Program (COST Action)
dc.description.sponsorshipEuropean Cooperation in Science and Technology
dc.description.versionPublisher version
dc.description.volume2
dc.identifier.doi10.1002/anbr.202100033
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR04062
dc.identifier.issn2699-9307
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-85164458516
dc.identifier.urihttps://doi.org/10.1002/anbr.202100033
dc.identifier.wos782101500001
dc.keywordsArtificial neural networks
dc.keywordschitosan
dc.keywordsPEGylated chitosan nanoparticles
dc.keywordsPlasmid DNA
dc.keywordspolyethylene glycol
dc.keywordsSodium tripolyphosphate
dc.keywordsTransfection
dc.language.isoeng
dc.publisherWiley
dc.relation.grantnoCA15216
dc.relation.grantno116M995
dc.relation.ispartofAdvanced NanoBiomed Research
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/10952
dc.subjectEngineering, biomedical
dc.subjectNanoscience and nanotechnology
dc.subjectMaterials science
dc.subjectBiomaterials
dc.titleParameters influencing gene delivery efficiency of PEGylated chitosan nanoparticles: experimental and modeling approach
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorKızılel, Seda
local.contributor.kuauthorSitti, Metin
local.contributor.kuauthorÖnder, Tuğba Bağcı
local.contributor.kuauthorDoğan, Nihal Olcay
local.contributor.kuauthorCingöz, Ahmet
local.contributor.kuauthorŞeker-Polat, Fidan
local.contributor.kuauthorNazeer, Muhammad Anwaar
local.publication.orgunit1College of Engineering
local.publication.orgunit1SCHOOL OF MEDICINE
local.publication.orgunit2Department of Chemical and Biological Engineering
local.publication.orgunit2Department of Mechanical Engineering
local.publication.orgunit2School of Medicine
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