Drug release profile in core-shell nanofibrous structures: a study on Peppas equation and artificial neural network...

Area of Science:

• Biomaterials Science
• Drug Delivery Systems
• Computational Modeling

Background:

• Electrospun core-shell nanofibers offer controlled drug release.
• Predicting drug release kinetics is crucial for optimizing delivery systems.
• Tetracycline hydrochloride (TCH) is a model drug for evaluating release profiles.

Purpose of the Study:

• To model the release profile of tetracycline hydrochloride (TCH) from electrospun core-shell nanofibrous mats.
• To compare the predictive accuracy of the Peppas equation and an artificial neural network (ANN) for drug release.
• To demonstrate the viability of an ANN as a prediction tool for drug release from nanofibrous scaffolds.

Main Methods:

• Fabrication of core-shell nanofibers via co-axial electrospinning using TCH as the core and PLGA or PCL as shell materials.
• Utilizing an artificial neural network (ANN) with parameters including shell polymer properties, feed rates, concentrations, TCH contribution, and electrical field.
• Modeling drug release using the Peppas equation and ANN to predict Peppas constants and derive release patterns.

Main Results:

• The ANN successfully predicted Peppas constants, enabling the derivation of drug release patterns.
• The study demonstrated the viability of the ANN as a prediction tool for drug release from electrospun core-shell nanofibrous scaffolds.
• Core-shell nanofiber characteristics influenced the drug release kinetics, as captured by the models.

Conclusions:

• Artificial neural networks provide a viable tool for predicting drug release from electrospun core-shell nanofibrous scaffolds.
• The developed ANN model can accurately determine drug release profiles, aiding in the design of optimized drug delivery systems.
• This approach facilitates the understanding and control of drug release kinetics in advanced nanofibrous materials.