Cellulose acetate nanofibers with different degrees of alignment (randomly aligned (RA), partially aligned (PA), and highly aligned (HA)) were produced using an electrospinning technique. The different degrees of alignment were obtained by adjusting the rotation speed of the collector. Alpha-arbutin (3% w/w) employed as a model water-soluble compound was incorporated into the nanofibers during the fabrication process. The drug release characteristics were investigated using the nanofiber mats with the same size and weight. The prepared nanofibers with different degrees of alignment showed similar physical characteristics, including the fiber diameter, drug loading efficiency and capacity, and molecular form of the drug in the fibers. Interestingly, alpha-arbutin was released from HA nanofibers at a significantly faster rate than the PA and RA nanofibers. Eighty percent of the drug was released into the medium in 1.7, 4.2, and 9.4 min for HA, PA, and RA nanofibers, respectively. The orientation of nanofibers played a crucial role in governing the drug release, probably by creating network meshes with different degrees of entanglement, affecting the diffusion of drug to the external medium. Consequently, this approach can be used as a simple means of achieving immediate-release or fast-acting characteristics of cellulose-based formulations containing a water-soluble drug.

The application of Artificial Intelligence (AI) has the potential to revolutionize the formulation development of nanomedicine. This study investigated the physicochemical characteristics of progesterone-loaded solid-lipid nanoparticles (PG-SLNs) produced through an emulsification–ultrasonication process, with a focus on demonstrating the efficacy of this controlled preparation method via the Design of Experiments (DoE) and Artificial Neural Networks (ANN). Critical quality factors, including stearic acid, medium chain triglycerides (MCT), Pluronic F-127, and the amount of propylene glycol (PG), were explored using DoE to streamline experimental setups. The concentration of stearic acid was identified as a crucial factor influencing PG-SLN physicochemical properties, impacting particle size (PS), polydispersity index (PDI), zeta potential (ZP), and %drug loading (%DL). Optimal conditions for PS, PDI, ZP, and %DL were identified. DoE revealed acceptable values across multiple runs, and the ANN model demonstrates high prediction accuracy, surpassing Response Surface Methodology (RSM). The selected PG-SLN formulation was tested for transdermal drug delivery, showing improved permeation compared to PG suspension. Loading with limonene further enhances transdermal drug delivery, attributed to limonene’s role as a penetration enhancer. Moreover, the selected PG-SLN formulation was found to be safe and non-toxic to neuronal cells. The combination of DoE and ANN was proposed to enhance predictive ability. This research highlights the potential of PG-SLNs in transdermal drug delivery, emphasizing the role of limonene as a safe and effective enhancer. The study contributes to the growing interest in applying AI tools in pharmaceutical and biomedical fields for improved predictive modeling.