Development of a novel vertical high shear kneader and its performance in wet kneading of pharmaceutical powders

A novel multi-functional vertical high shear kneader has been developed. Wet kneading of pharmaceutical powders was conducted under various blade components and operating conditions. Compression properties of wet kneaded mass was analyzed and dispersion of hinder liquid (water) among the mass was investigated by assaying tracer aqueous pigment. Pellets were produced through a dome type extrusion granulator with continuous extrusion pressure measurement device and a fluidized bed drier, and then the physical properties were measured. Quantitative relationship between the pellet's physical properties and the binder dispersion condition as well as the compression properties could be obtained. It was found that the newly developed kneader was very effective to uniformly disperse binder as well as impart high shear stress to the wet mass without generating obvious adhesion onto the vessel wall. It was also pointed out that the extrusion pressure could determine the physical strength of pellet. This method proposes a new methodology for continuous monitoring of kneading condition as well as predicting pellet's physical properties.     

Comparison of different chemometric and analytical methods for the prediction of particle size distribution in pharmaceutical powders

This work compares the estimation of the particle size distribution of a pharmaceutical powder using near-infrared spectroscopy (NIRS), powder flowability properties, and components concentration. The estimations were made by considering the former data blocks separately and together using a multi-block approach. The powders were based on a formulation of paracetamol as the pharmaceutical active ingredient. The reference method used to determine particle size distribution was sieving. Partial least squares methods were used to estimate the multivariate regression models, and the results were compared in terms of figures of merit. It was shown that the partial least squares methods gave similar prediction errors. Regarding the data blocks used, the NIRS block was proven the most advantageous to estimate the particle size distribution. The prediction error of the NIRS block was similar to the other data blocks with additional advantages such as less generalization problems and the possibility of its use to predict additional physical and chemical properties with an improvement to analysis time. The multi-block approach produced the worst results but nevertheless allowed a deeper understanding of the individual contributions of the data blocks in the prediction of the particle size distribution.     

Chromatographic analysis of compounds of pharmaceutical interest.

A set of 17,600 samples belonging to our compound collection is selectively examined by liquid chromatography with UV, evaporative light scattering, and mass spectrometric detection methods. At least 70% of this set consists of pure samples with the expected structures. Subsequent studies by flow injection mass spectrometry show that this value is a conservative estimate and that the actual percentage of pure and correct compounds is close to 80%. Because this is the first time that sample quality information becomes available on such a large scale for the compound collection, it offers an opportunity to perform chemi-informatic studies for which structural integrity is essential. Results of these studies can be used to improve the selection of compounds for screening and evaluate the quality of compounds from particular sources.