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.

Exploratory analysis of chromatographic fingerprints to distinguish rhizoma Chuanxiong and rhizoma Ligustici.

Identification and quality control of products of natural origin, used for preventive and therapeutical goals, is required by regulating authorities, as the World Health Organization. This study focuses on the identification and distinction of the rhizomes from two Chinese herbs, rhizoma Chuanxiong (from Ligusticum chuanxiong Hort.) and rhizoma Ligustici (from Ligusticum jeholense Nakai et Kitag), by chromatographic fingerprints. A second goal is using the fingerprints to assay ferulic acid, as its concentration provides an additional differentiation feature. Several extraction methods were tested, to obtain the highest number of peaks in the fingerprints. The best results were found using 76:19:5 (v/v/v) methanol/water/formic acid as solvent and extracting the pulverized material on a shaking bath for 15 min. Then fingerprint optimization was done. Most information about the herbs, i.e. the highest number of peaks, was observed on a Hypersil ODS column (250 mm × 4.6 mm ID, 5 µm), 1.0% acetic acid in the mobile phase and employing within 50 min linear gradient elution from 5:95 (v/v) to 95:5 (v/v) acetonitrile/water. The final fingerprints were able to distinguish rhizoma Chuanxiong and Ligustici, based on correlation coefficients combined with exploratory data analysis. The distinction was visualized using Principal Component Analysis, Projection Pursuit and Hierarchical Clustering Analysis techniques. Quantification of ferulic acid was possible in the fingerprints of both rhizomes. The time-different intermediate precisions of the fingerprints and of the ferulic acid quantification were shown to be acceptable.

Prediction and interpretation of the antioxidant capacity of green tea from dissimilar chromatographic fingerprints.

Previously, multivariate calibration techniques have been successfully applied to model and predict the antioxidant activity of green tea from its chromatographic fingerprint. Since the selectivity differences between dissimilar chromatographic systems have already been valuably used in several applications, in this paper it is studied whether combining the complementary information contained in two dissimilar fingerprints can improve the predictive capacity of the multivariate calibration model. The simplest way of combining the data is concatenating both fingerprints for each sample. The resulting matrix can then be subjected to Orthogonal Projections to Latent Structures (O-PLS). Unfortunately, this approach resulted in a more complex model with a prediction error of about the average of the errors obtained with the individual fingerprints. Secondly, only the peaks with high loading and low orthogonal loading from both chromatograms were included in the O-PLS model. This resulted in a reduced complexity, but not in better predictions, probably due to a lack of complementarity of the information concerning the antioxidant capacity. Finally, the concatenated fingerprints were subjected to stepwise multiple linear regression (MLR) in order to build a model based on the variables most correlated with the antioxidant capacity. The obtained prediction error was lower than those of both previous approaches, but still higher than the error of the model based on a single analysis. This is probably again caused by a lack of complementarity in the variables. Nevertheless, it was advantageous to develop fingerprints on dissimilar system, because it enables to choose the most suited chromatographic profile to build a multivariate calibration model for the considered purpose. In contrast to what was expected, the study showed that the most simple (so the worst separated) fingerprints resulted in the best predictions. On the other hand, a more complex fingerprint in which more compounds are separated is still important to improve the interpretability of the model.

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.

Drug impurity profiling: Method optimization on dissimilar chromatographic systems: Part I: pH optimization of the aqueous phase.

The use of dissimilar chromatographic systems in drug impurity profiling can be very advantageous. Screening a new-drug impurity mixture on those systems not only enhances the chance that all impurities are revealed, but also allows choosing a suited system for further method development. In this paper several strategies were evaluated to predict the optimal pH (of the buffer used in the mobile phase) from the screening results. Four or five dissimilar stationary phases were screened at four pH values (between 2.5 and 9.4), in order to obtain maximal information about the composition of the sample and to select one column for the subsequent optimization. Different linear models (straight lines, 2nd and 3rd degree polynomials) based on these experiments were tested for their ability to predict the retention times (t(R)) of the impurities at intermediate pH values. The predicted t(R) values were then used to calculate minimal resolutions and eventually to select an optimal pH at which the highest minimal resolution is predicted. None of the applied models is accurate enough to predict correctly which peaks are worst separated at the indicated optimal pH. However, the best strategy (applying a second degree polynomial describing the t(R) measured at 3 consecutive screening pH values) did succeed in indicating an optimal pH at which a good separation of the impurities is obtained. Unfortunately, the resulting separation quality is not or only slightly better than the best separation obtained during screening. Therefore, it can be concluded that the most (time-) efficient approach to develop an impurity profile of a new drug is to screen it on four or five dissimilar columns at four different pH values and to retain the best screening conditions (without making predictions for intermediate conditions) for further optimization of the organic modifier composition of the mobile phase, and occasionally the temperature and the gradient. This is at least the case when the profiles have a complexity similar to those studied.

Evaluation of orthogonal/dissimilar RP-HPLC systems sets for their suitability as method-development starting points for drug impurity profiles.

Orthogonal or dissimilar separation systems provide different selectivities and their application can facilitate the development of methods to identify and quantify impurities in a drug substance. Two sets of chromatographic systems potentially applicable for method development were evaluated using four drug/impurity profiles. The sets consist of orthogonal or dissimilar systems and systems with good overall separation properties, selected in earlier studies. The aim of this study is to evaluate these systems for selectivity differences in the impurity profiles. These differences should allow determining the number of compounds occurring in an impurity profile. Then, one or a very limited number of systems is to be proposed for further method development. To examine the selectivity changes and separation quality for each impurity profile, both the normalized retention times tau and the resolutions between pairs of consecutively eluting peaks were plotted on parallel axes, representing the systems. For each profile, several systems of the studied sets can serve as potential starting points for further method development. All impurities could be separated from the active substance and from each other on at least one system. However, for the different profiles, different systems were selected as best, which makes that each system in a given set has its importance, depending on the properties of the profile.

Exploration of linear multivariate calibration techniques to predict the total antioxidant capacity of green tea from chromatographic fingerprints.

Nowadays fingerprinting is a generally applied technique for the identification and quality assessment of herbal products. In this study it was aimed to predict a quantitative property, the antioxidant capacity of green tea, from chromatographic fingerprints. Different linear multivariate calibration techniques, commonly applied on spectral data, were explored and compared. When the chromatograms were appropriately pretreated, all tested techniques were able to predict the total antioxidant capacity with a precision comparable to that of the reference method (Trolox equivalent antioxidant capacity assay). Stepwise multiple linear regression (MLR) however is less recommended because of inadequate variable selection. Principal components regression (PCR) also seems less preferable, because large variations not correlated with the total antioxidant capacity were also included in the model. This problem does not occur with partial least squares (PLS) models. Of all tested PLS methods, orthogonal projections to latent structures (O-PLS) was preferred because of its simplicity, reproducibility, good interpretability of the compounds' contribution to the antioxidant capacity and its good predictive and describing abilities.

Dissimilar or orthogonal reversed-phase chromatographic systems: A comparison of selection techniques.

Developing an analytical separation procedure for an unknown mixture is a challenging issue. An important example is the separation and quantification of a new drug and its impurities. One approach to start method development is the screening of the mixture on dissimilar chromatographic systems, i.e. systems with large selectivity differences. After screening, the most suited system is retained for further method development. In a step prior to such strategy dissimilar chromatographic systems need to be selected. In this paper the performance of different chemometric selection approaches, described in the literature, was visually evaluated and compared. Additionally, orthogonal projection approach (OPA) was tested as another potential selection method. All techniques, including the OPA method, were able to select (a set of) dissimilar chromatographic systems and many similarities between the selections were observed. However, the Kennard and Stone algorithm performed best in selecting the most dissimilar systems in the earliest steps of the selection procedure. The generalized pairwise correlation method (GPCM) and the auto-associative multivariate regression trees (AAMRT) were also performing well. OPA and weighted pair group method using arithmetic averages (WPGMA) are less preferable.

Comparison of Plackett-Burman and supersaturated designs in robustness testing.

An optimized FIA assay of L-N-monomethylarginine (LNMMA) was validated. The linearity, precision, accuracy and range of the analytical method were evaluated and robustness testing was performed. Several experimental designs for robustness testing containing different numbers of experiments (N) were compared. Both Plackett-Burman (N=8 or 12) and supersaturated designs (N=6) were examined. The latter design results were analyzed with the Fixing Effects and Adding Rows (FEAR) method, based on the initial addition of zero effect rows to the model matrix, which then are iteratively replaced by fixed effects. It was evaluated whether by reducing the number of experiments from 12 to 8 or 6, similar effects are estimated and considered (non-)significant. The FIA method was found linear in a range of 70-130% of the LNMMA concentration in the samples, and precise and accurate in a range of 80-120%. The estimated factor effects and the critical effects were found comparable for all examined designs, though there also are some indications that some from the supersaturated designs tend to be overestimated. The method was considered robust, since no significant effects occurred for the response describing the quantitative aspect of the method. For other responses, such as peak height and residence time, significant effects occur. However, only the most important effects are found with all designs. The effects reported from a supersaturated design based on the FEAR method still can be subject of further research.