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The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of Q versus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations' parameters. Two inputs were found important for the drug dissolution: d and t. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of Q versus d and t resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs' black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.

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Hyperspectral coherent anti-Stokes Raman scattering (CARS) microscopy is quickly becoming a prominent imaging modality because of its many advantages over the traditional paradigm of multispectral CARS. In particular, recording a significant portion of the vibrational spectrum at each spatial pixel allows image-wide spectral analysis at much higher rates than can be achieved with spontaneous Raman. We recently developed a hyperspectral CARS method, the driving principle behind which is the fast acquisition and display of a hyperspectral datacube as a set of intuitive images wherein each material in a sample appears with a unique trio of colors. Here we use this system to image and analyze two types of polymorphic samples: the pseudopolymorphic hydration of theophylline, and the packing polymorphs of the sugar alcohol mannitol. In addition to these solid-state form modifications we have observed spectral variations of crystalline mannitol and diprophylline as functions of their orientations relative to the optical fields. We use that information to visualize the distributions of these compounds in a pharmaceutical solid oral dosage form.

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