Neuro-fuzzy model for predicting insulin delivery from crosslinked agar-carbomer hydrogels.

This study focuses on the innovation of an inhaled sustained release form of insulin and the development of a neuro-fuzzy model specifically tailored to predict insulin release kinetics from polycondensed agar-carbomer hydrogels. These were synthesized by blending agar and carbomer, incorporating propylene glycol and glycerol, and then cross-linking by polycondensation. The structure and morphology of the hydrogel were analyzed via Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy and Proton Nuclear Magnetic Resonance Spectroscopy. The neuro-fuzzy model, a combination of artificial neural networks and fuzzy logic, employs inputs such as concentrations of crosslinking agents, polycondensation time, and release time, with the output being the rate of insulin release. The model demonstrated a strong correlation with experimental data, highlighting its effectiveness and precision in predicting insulin delivery from hydrogel compositions and temporal parameters. This emphasizes the importance of intelligent modelling for forecasting the kinetic release of therapeutic agents from novel drug delivery systems.

Artificial neural network for modeling formulation and drug permeation of topical patches containing diclofenac sodium.

In this work, topical matrix patches of diclofenac sodium (DS) were formulated by the solvent casting method using different ratios of chitosan (CTS) and kappa carrageenan (KC). Propylene glycol and tween 80 were used as a plasticizer and permeation enhancer, respectively. The drug matrix film was cast on a polyvinyl alcohol backing membrane. All the patches were evaluated for their physicochemical characteristics (thickness, folding endurance, flatness, drug content, tensile strength, bioadhesion, moisture content, and moisture uptake), along with their in vitro release and in vitro skin permeation studies. Franz diffusion cells were used to conduct the in vitro permeation studies. The artificial neural network (ANN) model was applied to simultaneously predict the DS release and the ex vitro skin permeation kinetics. The formulated patches showed good physicochemical properties. Out of all the studied patches, F6 presented sustained permeation in 32 h and was selected as the best formulation. The ANN model accurately predicted both the kinetic release and the skin permeability of DS from each formulation. This performance was demonstrated by the obtained R2 = 0.9994 and R2 = 0.9798 for release and permeation kinetics modeling, respectively, with root mean square error (RMSE) = 3.46 × 10-5.

Neuro-fuzzy modeling of ibuprofen-sustained release from tablets based on different cellulose derivatives.

In the present study, we investigated the drug release behavior from cellulose derivative (CD) matrices in the oral form of tablets. We used the adaptive neural-fuzzy inference system (ANFIS) to predict the best formulation parameters to get the perfect sustained drug delivery using ibuprofen (IB) as a model drug. The different formulations were prepared with different CDs, namely CMC, HEC, HPC, HPMC, and MC. The amount of the active ingredient varied between 20 and 50%. The flow properties of the powder mixtures were evaluated for their angle of repose, compressibility index, and Hausner ratio, while the tablets were evaluated for weight uniformity, hardness, friability, drug content, disintegration time, and release ratio. All tablet formulations presented acceptable pharmacotechnical properties. In general, the results showed that the drug release rate increases with an increase in the loaded drug. Kinetic studies using the Korsmeyer-Peppas equation showed that different drug release mechanisms were involved in controlling the drug dissolution from tablets. The drug release mechanism was influenced by the gel layer strength of the CDs formed in the dissolution medium. The mean dissolution time (MDT) was determined and the highest MDT value was obtained for the HPMC formulations. Moreover, HPMC exhibited release profiles adequate for sustained release formulations for over 14 h. The intelligent model based on the experimental data was used to predict the effect of the polymer's nature, the amount of the active ingredient, and the kinetic release profile and rate (R2 = 0.9999 and RMSE = 5.7 × 10-3). The ANFIS model developed in this work could accurately model the relationship between IB release behavior and tablet formulation parameters. The proposed model was able to successfully describe this phenomenon and can be considered an efficient tool with predictive capabilities that is useful for the designing and testing of new dosage systems based on polymers.