Abstract
This study explores novel alginate-konjac glucomannan core-shell aerogel particles for drug delivery systems fabricated via air-assisted coaxial prilling. A systematic approach is needed for the optimization of this method due to the numerous processing variables involved. This study investigated the influence of six variables: alginate and konjac glucomannan concentrations, compressed airflow, liquid pump pressures, and nozzle configuration. A hybrid software using Artificial Neural Networks and genetic algorithms was used to model and optimize the hydrogel formation, achieving a 100% desirable solution. The optimal formulation identified resulted in particles displaying a log-normal size distribution (R(2) = 0.967) with an average diameter of 1.57 mm. Supercritical CO(2) drying yielded aerogels with macropores and mesopores and a high specific surface area (201 ± 10 m(2)/g). The loading of vancomycin hydrochloride (Van) or a dexamethasone base (DX) into the aerogel cores during the process was tested. The aerogels exhibited appropriate structural characteristics, and both drugs showed burst release profiles with ca. 80% release within 10 min for DX and medium-dependent release for Van. This study demonstrates the feasibility of producing konjac aerogel particles for delivery systems and the high potential of AI-driven optimization methods, highlighting the need for coating modifications to achieve the desired release profiles.