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

Rapid screening and multicomponent quantifications of active components of oral syrup over-the-counter medications by Raman and UV-visible spectroscopy and multivariate regression analysis

Over-the-counter medications (OTCMs) are frequently recommended as a first-line treatment for common ailments, diseases, and illnesses. Oral liquid dosage forms are advantageous for rapid absorption with no dissolution time and are easier for pediatric and geriatric consumers to swallow. The production of these medicines by pharmaceutical industry makes them readily available to the public. Although the US Food and Drug Administration (FDA) provides strict guidelines to drug manufacturers of these products; the risk of counterfeiting is a global issue. This can lead to several adverse effects and health issues. Here, we report a fast screening and quality assurance method using Raman and UV-visible spectroscopy combined with Principal Component Analysis (PCA) and Partial-Least-Square (PLS) regression of commonly used OTCM oral syrups. PLS regressions of UV-visible absorption spectra were used for multicomponent quantifications of the active component (acetaminophen, guaifenesin, dextromethorphan HBr, and phenylephrine HCl) concentrations of OTMCs in flavored (sugar or sugar-free) oral syrups. Raman and UV-visible spectral responses varied based on the type and concentration of the active component analyzed. PCA of the spectral data provided pattern recognition of the oral syrup OTCM. The developed PLS method demonstrated good linearity with an R2 > 0.9784 and high sensitivity with a low detection limit of 0.02 mg/mL for acetaminophen and guaifenesin. Moreover, the simultaneous quantification of concentrations of all active components by the described method yielded good accuracies ranging from 88 to 94%. This study provides an example of the benefits of the combined use of Raman and UV-vis spectral profiling, PCA, and PLS regression for the quality analysis of oral syrups OTCM providing multicomponent quantification of active components with no need for sample extraction. The reported method can be easily adapted and scaled for online detection analysis used in the drug manufacturing industry, both in-situ and field analysis, and for the quality control of syrups OTCM by regulatory agencies and quality control officers.

Determination of anthraquinones in Rhamnus purshiana using HPLC coupled to diode array detector and simple ultraviolet spectroscopic analysis

A new method based on Ultraviolet spectrophotometry was developed and compared with that based on HPLC for the determination and quantification of anthraquinones in the extracts of Rhamnus purshiana bark. A validated quantitative analysis of cascaroside A, cascaroside B, emodin, and aloe-emodin in these herbal products has been previously performed using HPLC coupled with diode array detector. In the HPLC analysis, all the anthraquinones showed satisfactory regression (r² > 0.98) within the test ranges, and the recovery was in the range of 94 to 117%. The limits of detection and quantification ranged from 0.008-0.010 (μg mL^-1 ) and 0.029-0.035 (μg mL^-1 ), respectively. Hierarchical cluster analysis and principal component analysis showed differences in the anthraquinones determined from herbal samples. Subsequently, a simple and low-cost ultraviolet spectrophotometric methodology for the quantitative analysis of the same compounds in the extracts was applied, and all the contents were determined. A paired t-test confirmed that there were no significant differences between the two methods. Our results revealed that the developed method is simple and provides the ability to discriminate and control the quality of anthraquinones in herbal products. This article is protected by copyright. All rights reserved.

Chemometric-Guided Approaches for Profiling and Authenticating Botanical Materials

Botanical supplements with broad traditional and medicinal uses represent an area of growing importance for American health management; 25% of U.S. adults use dietary supplements daily and collectively spent over $9. 5 billion in 2019 in herbal and botanical supplements alone. To understand how natural products benefit human health and determine potential safety concerns, careful in vitro, in vivo, and clinical studies are required. However, botanicals are innately complex systems, with complicated compositions that defy many standard analytical approaches and fluctuate based upon a plethora of factors, including genetics, growth conditions, and harvesting/processing procedures. Robust studies rely upon accurate identification of the plant material, and botanicals' increasing economic and health importance demand reproducible sourcing, as well as assessment of contamination or adulteration. These quality control needs for botanical products remain a significant problem plaguing researchers in academia as well as the supplement industry, thus posing a risk to consumers and possibly rendering clinical data irreproducible and/or irrelevant. Chemometric approaches that analyze the small molecule composition of materials provide a reliable and high-throughput avenue for botanical authentication. This review emphasizes the need for consistent material and provides insight into the roles of various modern chemometric analyses in evaluating and authenticating botanicals, focusing on advanced methodologies, including targeted and untargeted metabolite analysis, as well as the role of multivariate statistical modeling and machine learning in phytochemical characterization. Furthermore, we will discuss how chemometric approaches can be integrated with orthogonal techniques to provide a more robust approach to authentication, and provide directions for future research.

Chemical Authentication of Botanical Ingredients: A Review of Commercial Herbal Products

Chemical methods are the most important and widely used traditional plant identification techniques recommended by national and international pharmacopoeias. We have reviewed the successful use of different chemical methods for the botanical authentication of 2,386 commercial herbal products, sold in 37 countries spread over six continents. The majority of the analyzed products were reported to be authentic (73%) but more than a quarter proved to be adulterated (27%). At a national level, the number of products and the adulteration proportions varied very widely. Yet, the adulteration reported for the four countries, from which more than 100 commercial products were purchased and their botanical ingredients chemically authenticated, was 37% (United Kingdom), 31% (Italy), 27% (United States), and 21% (China). Simple or hyphenated chemical analytical techniques have identified the total absence of labeled botanical ingredients, substitution with closely related or unrelated species, the use of biological filler material, and the hidden presence of regulated, forbidden or allergenic species. Additionally, affecting the safety and efficacy of the commercial herbal products, other low quality aspects were reported: considerable variability of the labeled metabolic profile and/or phytochemical content, significant product-to-product variation of botanical ingredients or even between batches by the same manufacturer, and misleading quality and quantity label claims. Choosing an appropriate chemical technique can be the only possibility for assessing the botanical authenticity of samples which have lost their diagnostic microscopic characteristics or were processed so that DNA cannot be adequately recovered.

Chemometrics and chromatographic fingerprints to classify plant food supplements according to the content of regulated plants

Plant food supplements are gaining popularity, resulting in a broader spectrum of available products and an increased consumption. Next to the problem of adulteration of these products with synthetic drugs the presence of regulated or toxic plants is an important issue, especially when the products are purchased from irregular sources. This paper focusses on this problem by using specific chromatographic fingerprints for five targeted plants and chemometric classification techniques in order to extract the important information from the fingerprints and determine the presence of the targeted plants in plant food supplements in an objective way. Two approaches were followed: (1) a multiclass model, (2) 2-class model for each of the targeted plants separately. For both approaches good classification models were obtained, especially when using SIMCA and PLS-DA. For each model, misclassification rates for the external test set of maximum one sample could be obtained. The models were applied to five real samples resulting in the identification of the correct plants, confirmed by mass spectrometry. Therefore chromatographic fingerprinting combined with chemometric modelling can be considered interesting to make a more objective decision on whether a regulated plant is present in a plant food supplement or not, especially when no mass spectrometry equipment is available. The results suggest also that the use of a battery of 2-class models to screen for several plants is the approach to be preferred.

Testing of complementarity of PDA and MS detectors using chromatographic fingerprinting of genuine and counterfeit samples containing sildenafil citrate

Counterfeit medicines are a global threat to public health. High amounts enter the European market, which is why characterization of these products is a very important issue. In this study, a high-performance liquid chromatography-photodiode array (HPLC-PDA) and high-performance liquid chromatography-mass spectrometry (HPLC-MS) method were developed for the analysis of genuine Viagra®, generic products of Viagra®, and counterfeit samples in order to obtain different types of fingerprints. These data were included in the chemometric data analysis, aiming to test whether PDA and MS are complementary detection techniques. The MS data comprise both MS1 and MS2 fingerprints; the PDA data consist of fingerprints measured at three different wavelengths, i.e., 254, 270, and 290 nm, and all possible combinations of these wavelengths. First, it was verified if both groups of fingerprints can discriminate between genuine, generic, and counterfeit medicines separately; next, it was studied if the obtained results could be ameliorated by combining both fingerprint types. This data analysis showed that MS1 does not provide suitable classification models since several genuines and generics are classified as counterfeits and vice versa. However, when analyzing the MS1_MS2 data in combination with partial least squares-discriminant analysis (PLS-DA), a perfect discrimination was obtained. When only using data measured at 254 nm, good classification models can be obtained by k nearest neighbors (kNN) and soft independent modelling of class analogy (SIMCA), which might be interesting for the characterization of counterfeit drugs in developing countries. However, in general, the combination of PDA and MS data (254 nm_MS1) is preferred due to less classification errors between the genuines/generics and counterfeits compared to PDA and MS data separately.

Identification of (antioxidative) plants in herbal pharmaceutical preparations and dietary supplements

The standard procedures for the identification, authentication, and quality control of medicinal plants and herbs are nowadays limited to pure herbal products. No guidelines or procedures, describing the detection or identification of a targeted plant or herb in pharmaceutical preparations or dietary supplements, can be found. In these products the targeted plant is often present together with other components of herbal or synthetic origin. This chapter describes a strategy for the fast development of a chromatographic fingerprint approach that allows the identification of a targeted plant in herbal preparations and dietary supplements. The strategy consists of a standard chromatographic gradient that is tested for the targeted plant with different extraction solvents and different mobile phases. From the results obtained, the optimal fingerprint is selected. Subsequently the samples are analyzed according to the selected methodological parameters, and the obtained fingerprints can be compared with the one obtained for the pure herbal product or a standard preparation. Calculation of the dissimilarity between these fingerprints will result in a probability of presence of the targeted plant. Optionally mass spectrometry can be used to improve specificity, to confirm identification, or to identify molecules with a potential medicinal or antioxidant activity.

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.