Electrospun Drug-Eluting Fibers for Biomedical Applications

Abstract

AbstractElectrospinning is a simple and versatile method to produce fibers using charged polymer solutions. As drug delivery systems, electrospun fibers are an excellent choice because of easy drug entrapment, high surface area, morphology control and biomimetic characteristics. Various drugs and biomolecules can be easily encapsulated inside or on fiber surface either during electrospinning or through post-processing of the fibers. Multicomponent fibers have attracted special attention because new properties and morphologies can be easily obtained through the combination of different polymers. The factors that affect the drug release such as construct geometry and thickness, diameter and porosity, composition, crystallinity, swelling capacity, drug loading, drug state, drug molecular weight, drug solubility in the release medium, drug–polymer–electrospinning solvent interactions are discussed. Mathematical models of drug release from electrospun fibers are reviewed and strategies to attain zero-order release and control of burst stage are considered. Finally, some results concerning release control in bicomponent fibers composed of poly(\(\varepsilon\)-caprolactone) and Lutrol F127 (poly(oxyethylene-b-oxypropylene-b-oxyethylene) are presented. The properties of the bicomponent fibers were studied in order to determine the effect of electrospinning processing on crystallinity, hydrophilicity and degradation. Acetazolamide and timolol maleate were loaded in the fibers in different concentrations in order to determine the effect of drug solubility in polymer, drug state, drug loading and fiber composition on morphology, drug distribution and release kinetics. Such electrospun drug eluting fibers can be used as basic elements of various implants and scaffolds for tissue regeneration.KeywordsDrug ReleaseFiber SurfaceWater Contact AngleDrug SolubilityElectrospun FiberThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.