Modeling of latent structure of indomethacin solid dispersion tablet using Bayesian networks

Study of the contribution of the state of water to the gel properties of a photocrosslinked polyacrylic acid hydrogel using magnetic resonance imaging

Photocrosslinked polyacrylic acid (PAA-HEMA) hydrogels are a promising candidate for use in dermatological patch adhesives. To gain further knowledge about the properties of this gel, we investigated the T1 relaxation time and the diffusion coefficient (D) of water in the hydrogels using magnetic resonance (MR) imaging. Hydrogels with different formulations and process factors were prepared and tested. The observed data were analyzed by ANOVA, which clarified the mode of action of the formulation and process factors based on these MR parameters. Various gel properties (i.e., gel fraction, swelling capacity, gel strength, and water-retention ability) were also measured, followed by a Bayesian network (BN) analysis. The BN allowed us to summarize well the relationships between the formulation and process factors, MR parameters, and gel properties. T1 was associated with the swelling and water-retention properties of the hydrogel, whereas D was associated with gel formation and gel strength. Furthermore, this study clarified that T1 and D mostly represented the hydration and water-compartmentalization effects of the hydrogel, respectively. In conclusion, the state of water seems to play an important role in the properties of the PAA-HEMA hydrogel.

Latent structure modeling underlying theophylline tablet formulations using a Bayesian network based on a self-organizing map clustering

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and design space. In this article, we integrate thin-plate spline (TPS) interpolation, Kohonen's self-organizing map (SOM) and a Bayesian network (BN) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared using a standard formulation. We measured the tensile strength and disintegration time as response variables and the compressibility, cohesion and dispersibility of the pretableting blend as latent variables. We predicted these variables quantitatively using nonlinear TPS, generated a large amount of data on pretableting blends and tablets and clustered these data into several clusters using a SOM. Our results show that we are able to predict the experimental values of the latent and response variables with a high degree of accuracy and are able to classify the tablet data into several distinct clusters. In addition, to visualize the latent structure between the causal and latent factors and the response variables, we applied a BN method to the SOM clustering results. We found that despite having inserted latent variables between the causal factors and response variables, their relation is equivalent to the results for the SOM clustering, and thus we are able to explain the underlying latent structure. Consequently, this technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulation.