Application of arti ficial intelligence in modeling of the doxorubicin release behavior of pH and temperature responsive poly(NIPAAm-co-AAc)-PEG IPN hydrogel

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dc.authoridKünkül, Asım/0000-0002-6080-2588
dc.authoridYuceer, Mehmet/0000-0002-2648-3931
dc.authoridBoztepe, Cihangir/0000-0001-5019-2010
dc.authorwosidKünkül, Asım/ABG-8608-2020
dc.authorwosidBoztepe, Cihangir/H-5877-2018
dc.authorwosidYuceer, Mehmet/E-5110-2012
dc.contributor.authorBoztepe, Cihangir
dc.contributor.authorKunkul, Asim
dc.contributor.authorYuceer, Mehmet
dc.date.accessioned2024-08-04T20:47:11Z
dc.date.available2024-08-04T20:47:11Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractModeling of the drug release behavior of stimuli-responsive hydrogels is a domain of steadily increasing academic and industrial importance. It is very difficult to accurately predict the drug release kinetic of this type drug carrier materials due to environmental variables in the body such as pH and temperature. In this study, a pH- and temperature-responsive poly(N-Isopropyl acrylamide-co-Acrylic acid)/Poly(ethylene glycol) (poly(NIPAAm-co-AAc)/PEG) interpenetrating polymer network (IPN) hydrogel was synthesized by free radical solution polymerization in the presence of poly(NIPAAm-co-AAc) microgels and PEG. The synthesized IPN hydrogels showed rapid pH- and temperature-responsive deswelling behavior. The textural properties and surface morphology of poly(NIPAAm-co-AAc) IPN hydrogel were characterized by scanning electron microscopy (SEM) analysis technique. The doxorubicin (DOX) was loaded to the hydrogels by swelling the hydrogels in the DOX solution. The cumulative release of DOX has been investigated as a function of time, pH and temperature. Experimental DOX release data obtained were successfully modeled using ANNs, LS-SVM and SVR methodologies. To evaluate the performance of these models, four statistical parameters: correlation coefficient (R), root mean squared error (RMSE), mean square error (MSE) and mean absolute percentage error (MAPE) were calculated. It was found that the developed ANN model show best performance in modeling the DOX release behavior of poly(NIPAAm-co-AAc)/PEG IPN hydrogels.en_US
dc.identifier.doi10.1016/j.jddst.2020.101603
dc.identifier.issn1773-2247
dc.identifier.issn2588-8943
dc.identifier.scopus2-s2.0-85080043213en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jddst.2020.101603
dc.identifier.urihttps://hdl.handle.net/11616/99199
dc.identifier.volume57en_US
dc.identifier.wosWOS:000538406300002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Drug Delivery Science and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSupport Vector Machineen_US
dc.subjectArtificial Neural-Networken_US
dc.subjectDrug-Releaseen_US
dc.subjectLiposomal Doxorubicinen_US
dc.subjectMechanical-Propertiesen_US
dc.subjectMathematical-Modelen_US
dc.subjectDeliveryen_US
dc.subjectPredictionen_US
dc.subjectSystemsen_US
dc.subjectOptimizationen_US
dc.titleApplication of arti ficial intelligence in modeling of the doxorubicin release behavior of pH and temperature responsive poly(NIPAAm-co-AAc)-PEG IPN hydrogelen_US
dc.typeArticleen_US

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