Chromatographic fingerprinting as a strategy to identify regulated plants in illegal herbal supplements.

Erectile dysfunction (ED) is a sexual disorder characterized by the inability to achieve or maintain a sufficiently rigid erection. Despite the availability of non-invasive oral treatment options, many patients turn to herbal alternatives. Furthermore, herbal supplements are increasingly gaining popularity in industrialized countries and, as a consequence, quality control is a highly important issue. Unfortunately, this is not a simple task since plants are often crushed and mixed with other plants, which complicates their identification by usage of classical approaches such as microscopy. The aim of this study was to explore the potential use of chromatographic fingerprinting to identify plants present in herbal preparations intended for the treatment of ED. To achieve this goal, a HPLC-PDA and a HPLC-MS method were developed, using a full factorial experimental design in order to acquire characteristic fingerprints of three plants which are potentially beneficial for treating ED: Epimedium spp., Pausinystalia yohimbe and Tribulus terrestris. The full factorial design demonstrated that for all three plant references a C8 column (250mm×4.6mm; 5µm particle size) is best suited; methanol and an ammonium formate buffer (pH 3) were found to be the best constituents for the mobile phase. The suitability of this strategy was demonstrated by analysing several self-made triturations in three different botanical matrices, which mimic the influential effects that could be expected when analysing herbal supplements. To conclude, this study demonstrates that chromatographic fingerprinting could provide a useful means to identify plants in a complex herbal mixture.

Clustering and diagnostic modelling of slimming aids based on chromatographic and mass spectrometric fingerprints.

Herbal medicines and food supplements intended as slimming aids are increasingly gaining popularity worldwide, especially for treating obesity. In this study, an ultra-performance liquid chromatography coupled to photodiode array detection (UPLC-PDA) and an ultra-performance liquid chromatography mass spectrometry (UPLC-MS) method were developed to analyze 92 slimming aids (confiscated by customs), aimed at acquiring highly informative fingerprints. Three types of fingerprints were acquired (PDA, Total Ion Chromatograms (TIC), and MS fingerprints) which were used in the chemometric data analysis. Both unsupervised (i.e., Hierarchical Cluster Analysis (HCA)) and supervised techniques (i.e., Classification and Regression Tree (CART) and Partial Least Squares - Discriminant Analysis (PLS-DA)) were applied. The aim was to perform an in-depth study of the samples, thereby exploring potential patterns present in the data. HCA was able to generate a clustering which was mainly defined by chemical compounds detected in the samples, i.e., sibutramine, phenolphthalein and amfepramone. PLS-DA generated the best diagnostic models for both PDA and TIC fingerprints, characterized by correct classification rates of external validation of 85% and 80%, respectively. For the MS fingerprints, the best model was obtained by CART (65% correct classification rate of external validation). Despite a lower correct classification rate, exploration of the concerned misclassifications revealed that the MS fingerprints proved to be superior since even very low concentrations of sibutramine could be detected. This study shows that reliable chemometric models can be obtained, based on the presence of prohibited chemical substances, which allow high-throughput data analysis of such samples. Moreover, they generate a prime notion of potential threat to a patient's health posed by these kinds of slimming aids. Copyright © 2016 John Wiley & Sons, Ltd.

Chemometrics and the identification of counterfeit medicines-A review.

This review article provides readers with a number of actual case studies dealing with verifying the authenticity of selected medicines supported by different chemometric approaches. In particular, a general data processing workflow is discussed with the major emphasis on the most frequently selected instrumental techniques to characterize drug samples and the chemometric methods being used to explore and/or model the analytical data. However, further discussion is limited to a situation in which the collected data describes two groups of drug samples - authentic ones and counterfeits.

The Monte Carlo validation framework for the discriminant partial least squares model extended with variable selection methods applied to authenticity studies of Viagra® based on chromatographic impurity profiles.

The aim of this work was to develop a general framework for the validation of discriminant models based on the Monte Carlo approach that is used in the context of authenticity studies based on chromatographic impurity profiles. The performance of the validation approach was applied to evaluate the usefulness of the diagnostic logic rule obtained from the partial least squares discriminant model (PLS-DA) that was built to discriminate authentic Viagra® samples from counterfeits (a two-class problem). The major advantage of the proposed validation framework stems from the possibility of obtaining distributions for different figures of merit that describe the PLS-DA model such as, e.g., sensitivity, specificity, correct classification rate and area under the curve in a function of model complexity. Therefore, one can quickly evaluate their uncertainty estimates. Moreover, the Monte Carlo model validation allows balanced sets of training samples to be designed, which is required at the stage of the construction of PLS-DA and is recommended in order to obtain fair estimates that are based on an independent set of samples. In this study, as an illustrative example, 46 authentic Viagra® samples and 97 counterfeit samples were analyzed and described by their impurity profiles that were determined using high performance liquid chromatography with photodiode array detection and further discriminated using the PLS-DA approach. In addition, we demonstrated how to extend the Monte Carlo validation framework with four different variable selection schemes: the elimination of uninformative variables, the importance of a variable in projections, selectivity ratio and significance multivariate correlation. The best PLS-DA model was based on a subset of variables that were selected using the variable importance in the projection approach. For an independent test set, average estimates with the corresponding standard deviation (based on 1000 Monte Carlo runs) of the correct classification rate, sensitivity, specificity and area under the curve were equal to 96.42% ± 2.04, 98.69% ± 1.38, 94.16% ± 3.52 and 0.982 ± 0.017, respectively.

Chromatographic impurity fingerprinting of genuine and counterfeit Cialis® as a means to compare the discriminating ability of PDA and MS detection.

Public health is threatened worldwide by counterfeit medicines. Their quality, safety and efficacy cannot be guaranteed since no quality control is performed during and/or after the manufacturing process. Characterization of these products is a very important topic. During this study a High Performance Liquid Chromatography-Photodiode Array (HPLC-PDA) and a High Performance Liquid Chromatography - Mass Spectrometry (HPLC-MS) method were developed to analyse both genuine and counterfeit samples of Cialis®. The obtained PDA and MS fingerprints were explored and modelled using unsupervised Principal Component Analysis (PCA) and supervised Partial Least Squares and its discriminant variant (PLS, PLS-DA) as well the classification methods including Soft Independent Modelling of Class Analogy (SIMCA) and the k Nearest Neighbour classifier (kNN). Both MS1 and MS2 data and data measured at 254 nm and 270 nm were used with the aim to test the potential complementarity of PDA and MS detection. First, it was checked if both groups of fingerprints can support differentiation between genuine and counterfeit medicines. Then, it was verified if the obtained multivariate models could be improved by combining information present in MS and PDA fingerprints. Survey of the models obtained for the 254 nm data, 270 nm data and 254_270 nm data combination showed that a tendency of discrimination could be observed with PLS. For the 270 nm data and 254_270 nm data combination a perfect discrimination between genuine and counterfeit medicines is obtained with PLS-DA and SIMCA. This shows that 270 nm alone performs equally well compared to 254_270 nm. For the MS1 and MS1_MS2 data perfect models were obtained using PLS-DA and kNN, indicating that the MS2 data do not provide any extra useful information to acquire the aimed distinction. When combining MS1 and 270 nm perfect models were gained by PLS-DA and SIMCA, which is very similar to the results obtained for PDA alone. These results show that both detectors have a potential to reveal chemical differences between genuine and counterfeit medicines and thus enable the construction of diagnostic models with excellent recognition. However, if a larger sample set, including more possible sources of variation, is analysed more sophisticated techniques such as MS might be necessary.

ATR-FTIR spectroscopy and chemometrics: An interesting tool to discriminate and characterize counterfeit medicines.

Counterfeit medicines pose a huge threat to public health worldwide. High amounts of counterfeit pharmaceuticals enter the European market and therefore detection of these products is essential. Attenuated Total Reflection Fourier-Transform infrared spectroscopy (ATR-FTIR) might be useful for the screening of counterfeit medicines since it is easy to use and little sample preparation is required. Furthermore, this approach might be helpful to customs to obtain a first evaluation of suspected samples. This study proposes a combination of ATR-FTIR and chemometrics to discriminate and classify counterfeit medicines. A sample set, containing 209 samples in total, was analyzed using ATR-FTIR and the obtained spectra were used as fingerprints in the chemometric data-analysis which included Principal Component Analysis (PCA), k-Nearest Neighbours (k-NN), Classification and Regression Trees (CART) and Soft Independent Modelling of Class Analogy (SIMCA). First it was verified whether the mentioned techniques are capable to distinguish samples containing different active pharmaceutical ingredients (APIs). PCA showed a clear tendency of discrimination based on the API present; k-NN, CART and SIMCA were capable to create suitable prediction models based on the presence of different APIs. However k-NN performs the least while SIMCA performs the best. Secondly, it was tested whether these three models could be expanded to discriminate between genuine and counterfeit samples as well. k-NN was not able to make the desired discrimination and therefore it was not useful. CART performed better but also this model was less suited. SIMCA, on the other hand, resulted in a model with a 100% correct discrimination between genuine and counterfeit drugs. This study shows that chemometric analysis of ATR-FTIR fingerprints is a valuable tool to discriminate genuine from counterfeit samples and to classify counterfeit medicines.

Detection of sibutramine in adulterated dietary supplements using attenuated total reflectance-infrared spectroscopy.

Sibutramine is one of the most occurring adulterants encountered in dietary supplements with slimming as indication. These adulterated dietary supplements often contain a herbal matrix. When customs intercept these kind of supplements it is almost impossible to discriminate between the legal products and the adulterated ones, due to misleading packaging. Therefore in most cases these products are confiscated and send to laboratories for analysis. This results inherently in the confiscation of legal, non-adulterated products. Therefore there is a need for easy to use equipment and techniques to perform an initial screening of samples. Attenuated total reflectance-infrared (ATR-IR) spectroscopy was evaluated for the detection of sibutramine in adulterated dietary supplements. Data interpretation was performed using different basic chemometric techniques. It was found that the use of ATR-IR combined with the k-Nearest Neighbours (k-NN) was able to detect all adulterated dietary supplements in an external test set and this with a minimum of false positive results. This means that a small amount of legal products will still be confiscated and analyzed in a laboratory to be found negative, but no adulterated samples will pass the initial ATR-IR screening.

Headspace-gas chromatographic fingerprints to discriminate and classify counterfeit medicines.

Counterfeit medicines are a global threat to public health. These pharmaceuticals are not subjected to quality control and therefore their safety, quality and efficacy cannot be guaranteed. Today, the safety evaluation of counterfeit medicines is mainly based on the identification and quantification of the active substances present. However, the analysis of potential toxic secondary components, like residual solvents, becomes more important. Assessment of residual solvent content and chemometric analysis of fingerprints might be useful in the discrimination between genuine and counterfeit pharmaceuticals. Moreover, the fingerprint approach might also contribute in the evaluation of the health risks different types of counterfeit medicines pose. In this study a number of genuine and counterfeit Viagra(®) and Cialis(®) samples were analyzed for residual solvent content using headspace-GC-MS. The obtained chromatograms were used as fingerprints and analyzed using different chemometric techniques: Principal Component Analysis, Projection Pursuit, Classification and Regression Trees and Soft Independent Modelling of Class Analogy. It was tested whether these techniques can distinguish genuine pharmaceuticals from counterfeit ones and if distinct types of counterfeits could be differentiated based on health risks. This chemometric analysis showed that for both data sets PCA clearly discriminated between genuine and counterfeit drugs, and SIMCA generated the best predictive models. This technique not only resulted in a 100% correct classification rate for the discrimination between genuine and counterfeit medicines, the classification of the counterfeit samples was also superior compared to CART. This study shows that chemometric analysis of headspace-GC impurity fingerprints allows to distinguish between genuine and counterfeit medicines and to differentiate between groups of counterfeit products based on the public health risks they pose.

A strategy for the identification of plants in illegal pharmaceutical preparations and food supplements using chromatographic fingerprints.

The detection of regulated and forbidden herbs in pharmaceutical preparations and nutritional supplements is a growing problem for laboratories charged with the analysis of illegal pharmaceutical preparations and counterfeit medicines. This article presents a feasibility study of the use of chromatographic fingerprints for the detection of plants in pharmaceutical preparations. Fingerprints were developed for three non-regulated common herbal products--Rhamnus purshiana, Passiflora incarnata L. and Crataegus monogyna--and this was done by combining three different types of detection: diode-array detection, evaporative light scattering detection and mass spectrometry. It is shown that these plants could be detected in respective triturations of the dry extracts with lactose and three different herbal matrices as well as in commercial preparations purchased on the open market.