The optimal design of novel microneedles (MNs) for the ocular delivery system is necessary and useful for improving the effectiveness of medication. The objective of this study was to design and develop the optimal fluconazole (FLUZ)-microemulsions (MEs)-loaded two-layered dissolving MNs as a potential treatment for fungal eye infection. The experimental designs using the simplex-lattice design were used to select the optimal formulation. The two-layered dissolving MNs were fabricated from 3% chitosan and 20% polyvinyl alcohol (PVA) in a weight ratio of 1:4 as an outer layer and FLUZ-loaded MEs containing eugenol, tween 80, PEG400, and water as an inner layer. The physical appearance, mechanical properties, penetration ability, dissolution time, in vitro/ex vivo ocular drug delivery, and antifungal activity were evaluated. From the results, the optimal two-layered dissolving MNs exhibited good physical properties, complete insertion, minimally invasive ocular tissue, and high stability at 4 °C and 25 °C for 3 months. Moreover, the optimal two-layered dissolving MNs showed significantly higher FLUZ permeation into the ocular tissue than other formulations, while providing highly potential antifungal activity. In conclusion, the optimal MEs-loaded two-layered MNs’ formulation had appropriate properties for ocular delivery of FLUZ, resulting in an improvement of fungal keratitis treatment.

The aims of this study were to investigate the skin regeneration potential of bioactive placenta (deer placenta (DP), goat placenta (GP), and porcine placenta (PP)) and fabricate bioactive extract-loaded dissolving microneedles (DMNs) as a dermal delivery approach. The placentas were water-extracted, and the active compounds were evaluated. Bioactivity studies were performed in dermal fibroblasts and keratinocytes. DMNs were fabricated to deliver the potent bioactive placenta extract into the skin. All placental extracts expressed high amounts of protein, growth factors (EGF, FGF, IGF-1 and TGF-β1), and amino acids. These extracts were not toxic to the skin cells, while the proliferation of fibroblast cells significantly increased in a time-dependent manner. GP extract that exhibited the maximum proliferation, migration, and regeneration effect on fibroblast cells was loaded into DMN patch. The suitable physical properties of DMNs led to increased skin permeation and deposition of bioactive macromolecules. Moreover, GP extract-loaded DMNs showed minimal invasiveness to the skin and were safe for application to human skin. In conclusion, placental extracts act as potent bioactive compounds for skin cells, and the highest bioactive potential of GP-loaded DMNs might be a novel approach to regenerate the skin.

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.