Eco-Friendly Tadalafil Surfactant-Free Dry Emulsion Tablets (SFDETs) Stabilized by In Situ Self-Assembled Aggregates of Natural Oil and Native Cyclodextrins

The main principles of green chemistry and engineering were extended to pharmaceutical formulations to prepare eco-friendly surfactant-free dry emulsion tablets (SFDETs) devoid of solvents or synthetic surfactants. Surfactant-free emulsions were stabilized by in situ cyclodextrins/sweet almond oil inclusion complexes and assessed for creaming stability. Formulation variables' effects on the emulsion droplet size and tadalafil solubility were studied using 2² × 3 factorial design. The emulsions exhibited nanometric and micrometric droplet sizes. The optimized nanoemulsion was loaded with tadalafil, morphologically evaluated, and utilized for preparing lyophilized SFDETs using different gelatin/Pearlitol® ratios. The tablets were characterized and the selected formulation was subjected to storage for 6 months. The emulsions prepared using β-cyclodextrin or higher concentrations of α-cyclodextrin showed little or no phase separation. Statistical analysis revealed significant influence of cyclodextrin type and amount on droplet size, while cyclodextrin type and oil volume exhibited significant effect on drug solubility. Morphological examination revealed non-aggregated spherical emulsion droplets. The prepared tablets showed satisfactory mechanical strength, short disintegration times, and enhanced drug dissolution. The selected tablet formulation (gelatin/Pearlitol®, 4:2 w/w) showed acceptable stability at 25°C/60% relative humidity. An overall conclusion claims that the absence of surfactants is expected to minimize the proposed tablets' in vivo toxicity and environmental concerns.

Modeling and Optimization of NLDH/PVDF Ultrafiltration Nanocomposite Membrane Using Artificial Neural Network-Genetic Algorithm Hybrid

In this research, MgAl-CO₃^2- nanolayered double hydroxide (NLDH) was synthesized through a facile coprecipitation method, followed by a hydrothermal treatment. The prepared NLDHs were used as a hydrophilic nanofiller for improving the performance of the PVDF-based ultrafiltration membranes. The main objective of this research was to obtain the optimized formula of NLDH/PVDF nanocomposite membrane presenting the best performance using computational techniques as a cost-effective method. For this aim, an artificial neural network (ANN) model was developed for modeling and expressing the relationship between the performance of the nanocomposite membrane (pure water flux, protein flux and flux recovery ratio) and the affecting parameters including the NLDH, PVP 29000 and polymer concentrations. The effects of the mentioned parameters and the interaction between the parameters were investigated using the contour plot predicted with the developed model. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and water contact angle techniques were applied to characterize the nanocomposite membranes and to interpret the predictions of the ANN model. The developed ANN model was introduced to genetic algorithm (GA) as a bioinspired optimizer to determine the optimum values of input parameters leading to high pure water flux, protein flux, and flux recovery ratio. The optimum values for NLDH, PVP 29000 and the PVDF concentration were determined to be 0.54, 1, and 18 wt %, respectively. The performance of the nanocomposite membrane prepared using the optimum values proposed by GA was investigated experimentally, in which the results were in good agreement with the values predicted by ANN model with error lower than 6%. This good agreement confirmed that the nanocomposite membranes prformance could be successfully modeled and optimized by ANN-GA system.