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Content uniformity is a critical attribute for potent and low-dosage formulations of active pharmaceutical ingredient (API) that, in addition to the formulation parameters, plays pivotal role during pharmaceutical development and production. However, when API content is low, implementing a vibrational spectroscopic analytical tool to monitor the content and blend uniformity remains a challenging task. The aim of this study was to showcase the potentials of mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy for quantitative analysis of alprazolam (ALZ) in a low-content powder blends with lactose, which is used as a common diluent for tablets produced by direct compression. The offered approach might be further scaled up and exploited for potential application in the process analytical technology (PAT). Partial least square and orthogonal PLS (OPLS) methodologies were employed to build the calibration models from raw and processed spectral data (standard normal variate, first and second derivatives). The models were further compared regarding their main statistical indicators: correlation coefficients, predictivity, root mean square error of estimation (RMSEE), and root mean square error of cross-validation (RMSEEcv). All statistical models presented high regression and predictivity coefficients. The RMSEEcv for the optimal models was 1.118, 0.08, and 0.059% for MIR, NIR, and Raman spectroscopy, respectively. The scarce information content extracted from the ALZ NIR spectra and the major band overlapping with those from lactose monohydrate was the main culprit of poor accuracy in the NIR model, whereas the subsampling instrumental setup (resulting in a non-representative spectral acquisition of the sample) was regarded as a main limitation for the MIR-based calibration model. The OPLS models of the Raman spectra of the powder blends manifested favorable statistical indicators for the accuracy of the calibration model, probably due to the distinctive ALZ Raman pattern resulting in the largest number of predictive spectral points that were used for the mathematical modeling. Furthermore, the Raman scattering calibration model was optimized in narrower scanning range (1700–700 cm−1) and its prediction power was evaluated (root mean square error of prediction, RMSEP = 0.03%). Thus, the Raman spectroscopy presented the most favorable statistical indicators in this comparative study and therefore should be further considered as a PAT for the quantitative determination of ALZ in low-content powder blends.

The purpose of this study was to apply factorial design in order to determine the influence of the formulation factors and their interactions on several responses such as particle size, dissolution behaviour at pH 1.2 and pH 7.4 as well as production yield, during the development of budesonide loaded, chitosan coated Ca-alginate microparticles (MPs) intended for treatment of inflammatory diseases in the gastrointestinal tract. Produced drug-loaded MPs were spherical in shape, had smooth surfaces with low porosity and size range between 5 and 11 µm. Production yield for the formulations from the design varied from 19% to 50%. Optimisation was performed using central composite design setting the targets: particle size at 5.5 µm, maximised yield, suppressed dissolution at pH 1.2 and sustained release at pH 7.4. The optimised batches were identified with a combined desirability value of 0.967.

A low-dose tablet formulation, containing a potent Benzodiazepine derivative Alprazolam was developed, considering the achievement of appropriate content uniformity of the active substance in powder blends and tablets as a major challenge. Two different types of lactose monohydrate (Tablettose 80 and Granulac 200) and two different types of dry mixing processes (high-shear mixing and "in bulk" mixing) were employed. To evaluate the influence of the variables (mixing speed, mixing time, filling level of the high-shear and cube mixer, lactose monohydrate type) and their interactions upon the response (content uniformity of Alprazolam in the powder blends), a Factorial 2 4 design (with 4 factors at 2 levels in 1 block) was generated for each type of mixer. For high-shear dry mixing the Response Surface, D-optimal Factorial 2 4 design (with 2 replications and 31 experiments) was used, while for the "in bulk" dry mixing the Response Surface, Central Composite Factorial 2 4 design (with 34 experiments) was used. The process parameters for the high-shear mixer were varied within the following ranges: filling level of 70-100%, impeller mixing speed of 50-300 rpm and mixing time of 2-10 minutes. For the cube mixer the following process parameter ranges were employed: filling level of 30-60%, mixing speed of 20-390 rpm and mixing time of 2-10 minutes. Raman spectroscopy in conjunction with a validated Partial Least Square (PLS) regression model was used as a Process Analytical Technology (PAT) tool for Alprazolam content determination and content uniformity monitoring. The DoE model was further employed to optimize the powder blending process in regard to the achievement of appropriate Alprazolam content uniformity using high-shear mixing and Tabletosse 80 as filler. The desirability function revealed that the following process parameters: a mixing time of 2 minutes, a mixing speed of 300 rpm and a 70% filling level of the mixer would produce powder blends with the lowest variability in Alprazolam content. The three independent lab batches of low-dose Alprazolam tablets, produced with high-shear mixing using these process parameters, conformed to the requirements of the European Pharmacopoeia for content (assay) of Alprazolam and uniformity of the dosage units.

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