Chemometrics and chromatographic fingerprints to discriminate and classify counterfeit medicines containing PDE-5 inhibitors.

Chromatographic fingerprints recorded for a set of genuine and counterfeit samples of Viagra® and Cialis® were evaluated for their use in the detection and classification of counterfeit samples of these groups of medicines. Therefore several exploratory chemometric techniques were applied to reveal structures in the data sets as well as differences among the samples. The focus was on the differentiation between genuine and counterfeit samples and on the differences between the samples of the different classes of counterfeits as defined by the Dutch National Institute for Public Health and the Environment (RIVM). In a second part the revealed differences between the samples were modelled to obtain a predictive model for both the differentiation between genuine and counterfeit samples as well as the classification of the counterfeit samples. The exploratory analysis clearly revealed differences in the data for the genuine and the counterfeit samples and with projection pursuit and hierarchical clustering differences among the different groups of counterfeits could be revealed, especially for the Viagra® data set. For both data sets predictive models were obtained with 100% correct classification rates for the differentiation between genuine and counterfeit medicines and high correct classification rates for the classification in the different classes of counterfeit medicines. For both data sets the best performing models were obtained with Least Square-Support Vector Machines (LS-SVM) and Soft Independent Modelling by Class Analogy (SIMCA)

A validated GC-MS method for the determination and quantification of residual solvents in counterfeit tablets and capsules

A fast headspace GC-MS method was developed and validated for the detection and quantification of residual solvents of all three ICH-classes in counterfeit tablets and capsules. The method was validated for ten solvents, selected based on an initial screening of counterfeit medicinal products. The considered solvents were ethanol, 2-propanol, acetone, ethylacetate, chloroform, carbon tetrachloride, benzene, toluene, dichloromethane and ethylbenzene. The proposed method uses a Phenomenex 624 capillary column (60 m x 0.32 mm; 1.8 µm film thickness) (Phenomenex, Torrance, USA) with an oven temperature program from 60°C (held for 5 min) to 270°C at 25 °C/min. 270°C is held for 10 min. The total run time is 23.4 minutes. The obtained method was fully validated by applying the “total error” profile. Calibration lines for all components were linear within the studied ranges. The relative bias and the relative standard deviations for all components were smaller than 5%, the -expectation tolerance limits did not exceed the acceptance limits of 10% and the relative expanded uncertainties were acceptable for all of the considered components. A method was obtained for the screening and quantification of residual solvents in counterfeit tablets and capsules, which will allow a fast screening of these products for the presence of residual solvents

Chemometrics and infrared spectroscopy – A winning team for the analysis of illicit drug products

Abstract Spectroscopic techniques such as infrared spectroscopy and Raman spectroscopy are used for a long time in the context of the analysis of illicit drugs, and their use is increasing due to the development of more performant portable devices and easy application in the context of harm reduction through drug checking or onsite forensic analysis. Although these instruments are routinely used with a spectral library, the importance of chemometric techniques to extract relevant information and give a full characterisation of samples, especially in the context of adulteration, is increasing. This review gives an overview of the applications described in the context of the analysis of illicit drug products exploiting the advantages of the combination of spectroscopy with chemometrics. Next to an overview of the literature, the review also tries to emphasize the shortcomings of the presented research papers and to give an incentive to what is needed to include chemometrics as a part of the daily routine of drug checking services and mobile forensic applications.info:eu-repo/semantics/publishe

Evaluation of the residual solvent content of counterfeit tablets and capsules

A group of counterfeit samples of Viagra® and Cialis® were screened for their residual solvent content and compared to the content of the genuine products. It was observed that all counterfeit samples had higher residual solvent contents compared to the genuine products. A more diverse range of residual solvents was found as well as higher concentrations. In general these concentrations did not exceed the international imposed maximum limits. Only in a few samples the limits were exceeded. A Projection Pursuit analysis revealed clusters of samples with similar residual solvent content, possibly enabling some future perspectives in forensic research

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

&lt;p&gt;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.&lt;/p&gt;</p

Detection of counterfeit Viagra by Raman microspectroscopy imaging and multivariate analysis

&lt;p&gt;During the past years, pharmaceutical counterfeiting was mainly a problem of developing countries with weak enforcement and inspection programs. However, Europe and North America are more and more confronted with the counterfeiting problem. During this study, 26 counterfeits and imitations of Viagra® tablets and 8 genuine tablets of Viagra® were analysed by Raman microspectroscopy imaging. After unfolding the data, three maps are combined per sample and a first PCA is realised on these data. Then, the first principal components of each sample are assembled. The exploratory and classification analysis are performed on that matrix. PCA was applied as exploratory analysis tool on different spectral ranges to detect counterfeit medicines based on the full spectra (200-1800 cm⁻¹), the presence of lactose (830-880 cm⁻¹) and the spatial distribution of sildenafil (1200-1290 cm⁻¹) inside the tablet. After the exploratory analysis, three different classification algorithms were applied on the full spectra dataset: linear discriminant analysis, k-nearest neighbour and soft independent modelling of class analogy. PCA analysis of the 830-880 cm⁻¹ spectral region discriminated genuine samples while the multivariate analysis of the spectral region between 1200 cm⁻¹ and 1290 cm⁻¹ returns no satisfactory results. A good discrimination of genuine samples was obtained with multivariate analysis of the full spectra region (200-1800 cm⁻¹). Application of the k-NN and SIMCA algorithm returned 100% correct classification during both internal and external validation.&lt;/p&gt;</p

Impurity fingerprints for the identification of counterfeit medicines - a feasibility study

&lt;p&gt;Most of the counterfeit medicines are manufactured in non good manufacturing practices (GMP) conditions by uncontrolled or street laboratories. Their chemical composition and purity of raw materials may, therefore, change in the course of time. The public health problem of counterfeit drugs is mostly due to this qualitative and quantitative variability in their formulation and impurity profiles. In this study, impurity profiles were treated like fingerprints representing the quality of the samples. A total of 73 samples of counterfeit and imitations of Viagra(®) and 44 samples of counterfeit and imitations of Cialis(®) were analysed on a HPLC-UV system. A clear distinction has been obtained between genuine and illegal tablets by the mean of a discriminant partial least squares analysis of the log transformed chromatograms. Following exploratory analysis of the data, two classification algorithms were applied and compared. In our study, the k-nearest neighbour classifier offered the best performance in terms of correct classification rate obtained with cross-validation and during external validation. For Viagra(®), both cross-validation and external validation sets returned a 100% correct classification rate. For Cialis(®) 92.3% and 100% correct classification rates were obtained from cross-validation and external validation, respectively.&lt;/p&gt;</p