A.M. van Nederkassel

Fast separations on monolithic silica columns: method transfer, robustness and column ageing for some case studies.

Six separation methods, developed on conventional silica high performance liquid chromatography (HPLC) columns were transferred to monolithic silica columns of 5 and 10 cm length. The transferred methods include the separation of an alkylbenzene mixture, the separations of drugs from their impurities (nimesulide, tetracycline, phenoxymethylpenicillin and erythromycin) and the separation of a green tea extract. The transfer of the first three methods was successful while for the latter three it was not. Increasing the flow rate up to 9 ml/min (where possible) inversely decreased the analysis time of the successfully transferred methods to 48 s (alkylbenzene mixture) 1.8 min (nimesulide mixture) and 3 min (tetracycline mixture) while still reasonable well separated peaks were obtained. The robustness and repeatability of the transferred and accelerated separations was found to be acceptable. Despite the use of flow rates up to 9 ml/min and frequent mobile phase changes with pH values varying from 3.5 to 7, the column performance was found to be rather constant and the column ageing to be minimal.

Similarity analyses of chromatographic fingerprints as tools for identification and quality control of green tea.

Similarity assessment of complex chromatographic profiles of herbal medicinal products is important as a potential tool for their identification. Mathematical similarity parameters have the advantage to be more reliable than visual similarity evaluations of often subtle differences between the fingerprint profiles. In this paper, different similarity analysis (SA) parameters are applied on green-tea chromatographic fingerprint profiles in order to test their ability to identify (dis)similar tea samples. These parameters are either based on correlation or distance measurements. They are visualised in colour maps and evaluation plots. Correlation (r) and congruence (c) coefficients are shown to provide the same information about the similarity of samples. The standardised Euclidean distance (ds) reveals less information than the Euclidean distance (de), while Mahalanobis distances (dm) are unsuitable for the similarity assessment of chromatographic fingerprints. The adapted similarity score (ss*) combines the advantages of r (or c) and de. Similarity analysis based on correlation is useful if concentration differences between samples are not important, whereas SA based on distances also detects concentration differences well. The evaluation plots including statistical confidence limits for the plotted parameter are found suitable for the evaluation of new suspected samples during quality assurance. The ss* colour maps and evaluation plots are found to be the best tools (in comparison to the other studied parameters) for the distinction between deviating and genuine fingerprints. For all studied data sets it is confirmed that adequate data pre-treatment, such as aligning the chromatograms, prior to the similarity assessment, is essential. Furthermore, green-tea samples chromatographed on two dissimilar High-Performance Liquid Chromatography (HPLC) columns provided the same similarity assessment. Combining these complementary fingerprints did not improve the similarity analysis of the studied data set.

Isotopic ratios to detect infringements of patents or proprietary processes of pharmaceuticals : Two case studies

Because of the increasing problem of drug counterfeiting and the potential danger related as well as the economic losses involved, the pharmaceutical industry and the regulatory instances are interested in the development of anti-counterfeiting and patent protection methodologies. In this paper, the evaluation of measured isotopic ratios by means of explorative chemometric techniques was performed to distinguish groups in two data sets containing samples of acetyl salicylic acid and ibuprofen, respectively. The samples in the data sets originated from different countries and manufacturers. For both compounds a clear distinction of groups of samples could be obtained. These groups could be explained based on the origin of the samples, both geographically as well as based on the manufacturer. Hypotheses were formulated concerning the synthetic pathways of the molecules and they were linked to the groups obtained with the chemometric tools.

Recognizing paracetamol formulations with the same synthesis pathway based on their trace-enriched chromatographic impurity profiles.

The development of a new drug substance is an expensive and time-consuming process. Therefore, the developers want to maximize the profit from the drug by patenting the concerned molecule as well as its synthesis pathway. In a later stage a faster or cheaper manufacturing process can be developed and patented. The aim of this study is to recognize paracetamol-containing drug formulations in relation to their synthesis pathways, in order to demonstrate the possibility to reveal fraudulently synthesized paracetamol. Since different synthesis pathways require different starting materials, solvents, reagents and catalysts and since they can produce different intermediates, it is expected that drug products originating from a different synthesis pathway will exhibit a different impurity profile. Therefore, in this study several paracetamol samples, synthesized in four different ways, are analyzed using trace-enrichment high-performance liquid chromatography (HPLC). The resulting chromatographic data were chemometrically treated in order to reveal clustering tendencies in the hope of distinguishing the different pathways. When performing principal component analysis (PCA) only 3 vaguely outlined clusters appeared. Projection pursuit (PP) was able to reveal 4 clusters and the samples with known synthesis pathway, except one, were classified in the different clusters. When hierarchical clustering and auto-associative multivariate regression trees (AAMRT) were applied, the samples of the four synthesis pathways could also be distinguished. AAMRT has an added value, since it can indicate the variables (peaks and thus also the impurities) that are responsible for the differences between the samples synthesized differently.