Synthesis of drug carrier smart ferrogels and application of artificial neural network in modeling their doxorubicin release behavior under alternating magnetic fields

Boztepe C., Vanlı T.

POLYMER ENGINEERING AND SCIENCE, vol.1, no.1, pp.1-18, 2023 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 1 Issue: 1
  • Publication Date: 2023
  • Doi Number: 10.1002/pen.26404
  • Journal Name: POLYMER ENGINEERING AND SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, Applied Science & Technology Source, Biotechnology Research Abstracts, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.1-18
  • Inonu University Affiliated: Yes

Abstract

The development of magnetic field-sensitive smart drug delivery systems with

superior properties has become an area of increasing academic and industrial

importance. In this study, smart poly(NIPAAm-co-VSA)-rGO/Fe3O4 ferrogels

with varying concentrations of reduced graphene oxide (rGO) were synthesized.

Fe3O4 nanoparticles loaded ferrogels were obtained by the in situ reduction

of Fe ions. The morphologic, structural, and magnetic properties of

ferrogel systems were characterized. Doxorubicin (DOX) was loaded to the

synthesized ferrogels by solution impregnation method and their heating and

drug release behavior over time under alternating magnetic field AMFs of

1.37, 1.64, and 1.91 millitesla (mT) were investigated. The drug loading and

releasing characteristics of the ferrogel series were calculated. When the experimental

results were examined, it was determined that the amount of rGO in

the structure of the developed ferrogel systems had a very high effect on the

magnetic, heating, and drug loading-release characteristics of the ferrogels. To

modeling their multivariable DOX release behavior, artificial neural network

modeling technique was used. Calculated model performance parameters have

shown that this developed artificial intelligence technique has great success in

modeling complex and nonlinear DOX release behaviors.