MODELING MASS RELEASE FROM NANOFIBERS – THEORY AND APPLICATION

This study investigates the drug release performance of electrospun composite nanofiber mats, which hold significant promise as drug carriers for site-specific delivery in therapeutic applications such as cancer therapy.Predicting drug release rates from poly(D,L-lactic-coglycolic acid) (PLGA) nanofibers produced via emulsion electrospinning is challenging due to the system's inherent complexity.To address this, two distinct implants were fabricated at the Faculty of Technology and Metallurgy, University of Belgrade: a PLGA implant and a composite scaffold consisting of a PLGA fibrous structure enclosed between two layers of poly(-caprolactone) (PCL), prepared via emulsion-based and sequential electrospinning techniques.Computational modeling was employed to evaluate the continuous drug release from these nanofibers.The study utilized the PAK software program and a CAD user interface to create nano-implant models, with post-processing by the PAK finite element (FE) solution.Two models were developed to simulate diffusive drug release from nanofibers into a threedimensional (3D) surrounding medium: (1) a one-dimensional (1D) finite elements with axial and radial diffusion representing the fibers, and (2) a 3D continuum discretized using composite smeared finite elements (CSFEs); with coupling of these models two models.Both models account for polymer degradation and drug hydrophobicity as partitioning at the fiber/surrounding interface.Experimental drug release rates from the scaffold were compared with computational predictions from the FE models.The results demonstrate the effectiveness of the proposed models in capturing the complex dynamics of drug release, providing valuable insights into the design and optimization of electrospun nanofiber-based drug delivery systems.This work highlights the potential of integrating experimental and computational approaches to advance the development of controlled drug release platforms for biomedical applications.