Multivariate Texture Analysis for Solid Dosage Intermediate and Final Product Characterization and Downstream Performance Prediction

Comprehensive Quality by Design in Pharmaceutical Development and Manufacture
2009 AIChE Annual Meeting
AIChE Annual Meeting
November 8, 2009 - 7:00pm
A framework using wavelet decomposition coupled with multivariate latent variable methods is proposed to quantitatively characterize the texture appearance of pharmaceutical materials and products. This framework allows having a non-biased measure of the visual characteristic of solid materials. The proposed technique has shown successful application in the steel sector , and is applied in a straight forward manner to grayscale images of the pharmaceutical products (or intermediate materials).

In summary, the technique uses wavelet decomposition on the grayscale images to reduce them to a vector of energies for multiple “frequencies” (called details in wavelet theory), such vectors are then compressed using Principal Components Analysis (for characterization) or used as regressors against a property of interest (e.g. in vitro dissolution, compression profile)

This method is illustrated with two case studies:

i) The analysis of an intermediate pharmaceutical product (wet granules from a high shear wet granulation step) to provide a quantitative envelope for the material, and also to predict its manufacturability at the compression step (after drying and milling).

ii) The characterization of a modified release film coated tablets, where the textural characteristics of the tablet are linked to the in vitro dissolution of the product.

Using a latent variable regression model, it was found that the appearance of the wet granules is able to predict 80% of the hardness compression profile for the material. In the case of the modified release tablets, the visual characteristics of the tablet alone can predict dissolution at 9hr with a 91% R2 accuracy. A model that considers the processing conditions in the film coater and the visual appearance is able to predict dissolution to a 97% R2.&'
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