Artificial Intelligence Deep Exploration of Influential Parameters on Physicochemical Properties of Curcumin‐Loaded Electrospun Nanofibers

Abstract

Artificial intelligence (AI) methods are explosively considered in the design and optimization of drug discovery and delivery systems. Herein, machine learning methods are used for optimizing the production of curcumin (CUR)‐loaded nanofibers. The required data are mined through the literature survey and two categories, including material‐ and machine‐based parameters, are detected and studied as effective parameters on the final outcomes. AI results show that high‐density polymers have a lower CUR release rate; however, with the increase in polymer density, CUR encapsulation efficiency (EE) increases in many types of polymers. The smallest diameter, highest EE, and highest drug release percentage are obtained at a molecular weight between 100 and 150 kDa and a CUR concentration of 10–15 wt%, with the polymer density in the range of 1.2–1.5 g mL−1. Also, the optimal distance of ≈23 cm, the flow rate of 3.5–4.5 mL h−1, and the voltage at the range of 12.5–15 kV result in the highest release rate, highest EE, and the lowest average diameter for fibers. These findings open up new roads for future design and production of drug‐loaded polymeric nanofibers with desirable properties and performances by AI methods.

Keywords

• artificial intelligence
• curcumin-loaded nanofibers
• electrospinning
• machine learning