The purpose of this research study was evaluation of the utility of two common multivariate techniques, agglomerative cluster analysis (CA) and principal component analysis (PCA), as supplementary means of detecting incompatibilities, which can occur between active pharmaceutical ingredients and excipients at the preformulation stage of a solid dosage form. For the detection of incompatibilities between atenolol (beta blocker) and selected excipients (mannitol, lactose, starch, methylcellulose, b-cyclodextrin, meglumine, chitosan, polyvinylpyrrolidone and magnesium stearate), the thermogravimetry (TG), differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR) were chosen. The results have shown that compatibility between atenolol and an excipient can be identified in a CA dendrogram by two large clusters, from which one groups an excipient and physical mixtures with a high concentration of the excipient. Another cluster encompasses atenolol and mixtures with a high content of the drug. In the PCA plot, all samples are located along the first principal component axis (PC1), beginning from a single component located with the most negative PC1 value, through mixtures with gradually varying concentration of both ingredients, till the second component located close to the most positive PC1 values. The results have shown that CA and PCA fulfil their role as supporting techniques in the interpretation of the data acquired from the TG curves, and the obtained data are compatible with the results of DSC and FTIR analyses.

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A chemometrically optimized DSC interpretation was developed for the identification of compatibility/incompatibility between active pharmaceutical ingredients (APIs) and excipients in pharmaceutical preparations. The chemometric approach based on factor analysis (FA) can be used as a supplementary tool for incompatibility detection in theophylline mixtures with excipients. The FA results expose the formation of two distinctly separate clusters on the FA score scatter plots-in the case of mixtures with compatible ingredients, one cluster was formed by theophylline and mixtures with high API content, the second by excipient and mixtures with high excipient content. In the event of incompatibility, the DSC curves of binary mixtures differ from those of ingredients, and the FA score scatter plot displays one cluster consisting of some mixtures at different ratios and the other of remaining mixtures and both ingredients. In brief, FA proved the incompatibility of theophylline mixtures with arabic gum, glucose, sorbitol and sucrose. The application of FA can help to circumvent the misinterpretation of DSC data.

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Application of thermogravimetry (TG) alone to study compatibility/incompatibility of active pharmaceutical ingredients (APIs) with excipients yields to misleading results due to overlapping of the thermal stages in the course of decomposition of both ingredients and their pharmaceutical mixtures. Hence, the purpose of this study was to assess the usefulness of multivariate statistical analysis as a supporting tool for interpretation of the TG traces during assessing compatibility of hydrocortisone as an API with selected excipients (mannitol, starch, lactose, methylcellulose, b-cyclodextrin, meglumine, chitosan, magnesium stearate and polyvinylpyrrolidone). The results show that two multivariate techniques, principal component analysis (PCA) and cluster analysis (CA), can be successfully used for interpretation of TG traces, while the TG is used alone as a screening technique to assess compatibility. The results obtained by using TG analysis, supported by PCA and CA, were approved by those of differential scanning calorimetry, infrared spectroscopy and X-ray powder diffraction. Incompatibilities were only found in mixtures of hydrocortisone with magnesium stearate and b-cyclodextrin.

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