Development of a Standardized Method for Measuring Bioadhesion and Mucoadhesion That Is Applicable to Various Pharmaceutical Dosage Forms.

Although some methods for measuring bioadhesion/mucoadhesion have been proposed, a standardized method is not yet available. This is expected to hinder systematic comparisons of results across studies. This study aimed to design a single/systematic in vitro method for measuring bioadhesion/mucoadhesion that is applicable to various pharmaceutical dosage forms. To this end, we measured the peak force and work of adhesion of minitablets, pellets, and a bioadhesive emulsion using a texture analyzer. Porcine tissue was used to simulate human stomach/skin conditions. The results of these formulations were then compared to those for formulations without the bioadhesive product. We conducted a case study to assess the stability of a bioadhesive emulsion. The results for the two parameters assessed were contact time = 60 s and contact force = 0.5 N at a detachment speed of 0.1 mm/s. Significant differences were observed between the bioadhesive and control formulations, thus demonstrating the adhesive capacity of the bioadhesive formulations. In this way, a systematic method for assessing the bioadhesive capacity of pharmaceutical dosage forms was developed. The method proposed here may enable comparisons of results across studies, i.e., results obtained using the same and different pharmaceutical formulations (in terms of their bioadhesion/mucoadhesion capacity). This method may also facilitate the selection of potentially suitable formulations and adhesive products (in terms of bioadhesive properties).

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations.

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.