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The micronization process of new compounds is usually performed based on an empirical basis with a limited understanding of input material properties and potential challenges. This study focuses on the understanding of the fracture behavior of small organic molecular crystals, by using particle shape and surface energy analysis techniques as well as molecular modeling tools. These methodologies enable us to generate new data and new ways of working that can provide crucial information for future pharmaceutical development. The shape, surface energy, and mechanical properties for four different drug substances were studied. This study showed that the crystal shape and the intermolecular interactions influence the dominant fracture mechanism. An in-depth knowledge of these together with the mechanical properties gives an insight of the fracture mechanism of small organic molecular crystals. In a second part, Partial Least Squares regression was applied to the data sets to model the size reduction ratio and d 90 of the micronized materials. Models were derived using multiple parameters.

The use of transmission Raman spectroscopy for quantitative assessment of pharmaceutical tablets using different multivariate approaches was investigated. Although Raman spectroscopy is most often used in backscatter geometry, in this paper a transmission approach was utilized, where the Raman scattered light is detected at the back side of the tablets. Raman spectra were recorded using a dispersive spectrometer with a 785 nm excitation laser and a typical exposure time of 10 s. The tablets were loaded to a 32-position sample rack and measured by an automated procedure. Tablets with variation in content of paracetamol were manufactured. The data were evaluated with respect to the content of paracetamol, using partial least squares (PLS) and multivariate curve resolution (MCR). In addition, classical least squares (CLS), curve fitting and peak ratios were included for comparison. MCR, CLS and PLS gave comparable results with relative prediction errors for an independent test set in the range of 2.4-3.4%. Curve fitting and peak ratios gave higher prediction errors, typically around 4 and 6%, respectively. Interestingly, quantitative models based on only two samples in the calibration sets resulted in almost as good results as if half of the available tablets were included in the calibration. Due to the simple calibration models and the selective Raman spectra, the loadings and spectra were easy to interpret for all the multivariate methods used in this paper. The implications for content uniformity analysis by using transmission Raman in this simplified approach are discussed.

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