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In 2019, a global viral pandemic, due to the SARS-CoV-2 virus, broke out. Soon after, the search for a vaccine and/or antiviral medicine began. One of the candidate antiviral medicines tested was ivermectin. Although several health authorities warned the public against the use of this medicine outside clinical trials, the drug was widely used at the end of 2020 and in 2021. Simultaneously, several reports started to emerge demonstrating serious adverse effects after self-medicating with ivermectin. It stands to reason that the self-administration of substandard or falsified (SF) medicines bearing harmful quality deficiencies have contributed to this phenomenon. In order to have a better view on the nature of these harmful quality deficiencies, SF ivermectin samples, intercepted in large quantities by the Belgian regulatory agencies during the period 2021-2022, were analyzed in our official medicines control laboratory. None of the samples (n = 19) were compliant to the quality criteria applicable to medicinal products. These SF products either suffered from a systematic underdosing of the active pharmaceutical ingredient or were severely contaminated with bacteria, two of which were contaminated with known pathogens that cause gastrointestinal illness upon oral intake. In addition to the direct risks of self-medicating with such a product, the improper usage and dosage of ivermectin medication might also facilitate ivermectin tolerance or resistance in parasites. This may have detrimental consequences on a global scale, certainly as the number of newly developed active pharmaceutical ingredients that can safely be used to combat parasites is rather scarce., (© 2023 John Wiley & Sons, Ltd.)

Like drug products, Active Pharmaceutical Ingredients (APIs) are subject to substandard and falsification issues, which represent a threat to patient health. In order to monitor the quality of drug substances and prevent the use of non-compliant APIs, Official Medicine Control Laboratories work together in a European network developing coordinated strategies and programmes. The API working group proposed a market surveillance study on omeprazole and omeprazole magnesium with the objectives of controlling the pharmaceutical quality of samples, checking compliance with the monographs of the European Pharmacopoeia, and collecting analytical fingerprints that could be further used to differentiate manufacturing sources for future authenticity investigations. The study described in this article reports the analysis carried out by 7 European laboratories on 28 samples from 11 manufacturers with 5 analytical techniques (related substances with HPLC, residual solvents with GC-MS, near infrared spectroscopy, proton nuclear magnetic resonance spectroscopy and X-ray powder diffractometry). The large amount of resulting analytical data were centralized and treated with two chemometric methods: Principal Component Analysis and Hierarchical Clustering Analysis. Data were analyzed separately and in combination (data fusion), allowing us to conclude that NMR and XRPD were suitable to differentiate samples originating from 9 out of 11 manufacturers. Analytical fingerprints associated with chemometrics were demonstrated to be a valuable methodology to discriminate manufacturers of omeprazole and omeprazole magnesium APIs and detect future substandard and falsified APIs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The threats of substandard and falsified (SF) antimicrobials, posed to public health, include serious adverse drug effects, treatment failures and even development of antimicrobial resistance. Next to these issues, it has no doubt that efficient methods for on-site screening are required to avoid that SF antimicrobials reach the patient or even infiltrate the legal supply chain. This study aims to develop a fast on-site screening method for SF antimicrobials using spectroscopic techniques (mid infrared, benchtop near infrared, portable near infrared and Raman spectroscopy) combined with chemometrics. 58 real-life illegal antimicrobials (claiming 18 different antimicrobials and one beta-lactamase inhibitor) confiscated by the Belgian Federal Agency for Medicines and Health Products (FAMHP) and 14 genuine antimicrobials were analyzed and used to build and validate models. Two types of models were developed and validated using supervised chemometric tools. One was used for the identification of the active pharmaceutical ingredients (APIs) by applying partial least squares-discriminant analysis (PLS-DA) and another one was used for the detection of non-compliant (overdosed or underdosed) samples by applying PLS-DA, k-nearest neighbors (k-NN) and soft independent modelling by class analogy (SIMCA). The best model capable of identifying amoxicillin and clavulanic acid (co-amoxiclav), azithromycin, co-trimoxazole and amoxicillin was based on the mid-infrared spectra with a correct classification rate (ccr) of 100%. The optimal model capable of detecting non-compliant samples within the combined group of amoxicillin and co-amoxiclav via SIMCA showed a ccr for the test set of 88% (7/8) using mid infrared or benchtop near infrared spectroscopy. The best model for detecting non-compliant samples within the group of amoxicillin via SIMCA was obtained using mid-infrared or Raman spectra, resulting in a ccr of 80% for the test set (4/5) and a ccr for calibration of 100%. For the group of co-amoxiclav, the optimal models showed a ccr of 100% for the detection of non-compliant samples by applying mid-infrared, benchtop near infrared or portable near infrared spectroscopy. Taken together, the obtained models, hyphenating spectroscopic techniques and chemometrics, enable to easily identify suspected SF antimicrobials and to differentiate non-compliant samples from compliant ones., Competing Interests: Declaration of competing interest The authors state that there is no conflict of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Substandard and falsified (SF) antimicrobials are gaining popularity in both developing and developed countries, posing a growing threat to public health. In general, the evaluation of SF antimicrobial drugs mainly focuses on the identification and quantification of the pharmaceutical active ingredients, ignoring other parameters of drug quality control. This study performed an in-depth characterization and hazard identification of suspected SF antimicrobial medicinal products encountered in Belgium. In this comprehensive evaluation, impurity tests and dissolution studies were carried out. The dissolution profiles of illegal SF antimicrobials were mathematically compared to their genuine counterparts using the f 1 and f 2 -factor. The results indicated that 17 out of 57 illegal samples contained higher than permitted amounts of impurities and clearly demonstrated low equivalences of dissolution profiles between SF antimicrobials and genuine products. The variations between tablets at the different time points of the dissolution curves were also higher for the SF medicines. Moreover, 11 out of 19 illegal samples failed to meet the dissolution criteria prescribed by the United States Pharmacopeia. As impurities may induce adverse reactions and improper dissolution patterns may be the cause of insufficient drug efficacy, aggravation of illness and even promotion of antimicrobial resistance can be expected., (© 2019 John Wiley & Sons, Ltd.)

Falsified medical products have become a global threat since they were first mentioned to the general public at the conference of experts on the rational use of drugs organized by the world health organization (WHO) in 1985. Today, official estimates of the annual death toll due to falsified medical products range between two hundred thousand and one million. Although the extent of this global problem is the most significant in the developing world, an increasing number of reports have demonstrated the presence of a substantial (black) market for falsified medical products in the developed world. In recent years, also biotechnology drugs (synthetic peptide drugs and protein drugs) have been reported to be prone for falsifications. Next to the traditional doping related substances and image-enhancing polypeptides (e.g., human growth hormone, melanotan II) also essential medicines such as insulin, oxytocin and monoclonal antibodies have been falsified. The danger regarding the use of these falsified polypeptide drugs lies in the fact that end-users have no guarantee of the safety and efficacy of these preparations. Multiple reports have namely described the presence of the wrong active pharmaceutical ingredient (API), the wrong dosage or the absence of the API. Additionally, adverse health effects have been reported in the past due to toxic contaminations and product or process related impurities. Moreover, also unauthorized polypeptides or polypeptides which failed clinical trials or are still subject of clinical or pre-clinical assessments have been found in seizures of regulatory agencies. It stands to reason that regulatory agencies and analytical laboratories handling falsified biotechnology drugs have stepped up efforts to counter these grievous practices. The analysis of these falsified polypeptides and putative impurities is however not always straightforward. Often (bio)analytical laboratories have to resort to a combination of electrophoretic techniques, immunological assays and mass spectrometry based approaches to merely identify the content of seized samples. In addition, the difference in size (peptide vs proteins vs monoclonal antibodies), complexity (e.g., isoforms, glycosylations) and different synthesis techniques (chemical synthesis, recombinant expression, native protein isolation) result in a wide range of putative health risks. This review therefore aims to provide a brief overview of the genuine biotherapeutics present on the market and their quality prerequisites. Next, we describe the identification strategy utilised by our lab to identify the API in falsified biotherapeutics, followed by a discussion of the putative hazards due to impurities and contaminations that were found or could be encountered in falsified biotherapeutics. Finally, we terminate with an educational prediction of what may happen in the future and possible ways to counteract putative future disasters., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Counterfeit medicines are a global threat to public health. High amounts enter the European market, enforcing the need for simple techniques to help customs detect these pharmaceuticals. This study focused on physical profiling and IR spectroscopy to obtain a prime discrimination between genuine and illegal Viagra® and Cialis® medicines. Five post-tableting characteristics were explored: colour, mass, long length, short length, and thickness. Hypothesis testing showed that most illegal samples (between 60 and 100%) significantly differ from the genuine medicines, in particular for mass and long length. Classification and Regression Trees (CART) analysis resulted in a good discrimination between genuine and illegal medicines (98.93% correct classification rate for Viagra®, 99.42% for Cialis®). Moreover, CART confirmed the observation that mass and long length are the key physical characteristics which determine the observed discrimination. IR analysis was performed on tablets without blister and on tablets in intact blister. These data were analyzed using Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares - Discriminant Analysis (PLS-DA). Supervised techniques needed to be applied since Principal Component Analysis (PCA) was not able to generate the desired discrimination. Our study shows that a perfect discrimination between genuine and illegal medicines can be made by both SIMCA and PLS-DA without removing the tablets from the blister. This approach has the advantage of keeping the blister intact. Our study demonstrates that these user friendly techniques are reliable methods to aid customs to obtain a prime distinction between genuine and illegal samples on the spot. Copyright © 2015 John Wiley & Sons, Ltd., (Copyright © 2015 John Wiley & Sons, Ltd.)

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.

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., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Recent advances in genomics, recombinant expression technologies and peptide synthesis have led to an increased development of protein and peptide therapeutics. Unfortunately this goes hand in hand with a growing market of counterfeit and illegal biopharmaceuticals, including substances that are still under pre-clinical and clinical development. These counterfeit and illegal protein and peptide substances could imply severe health threats as has been demonstrated by numerous case reports. The Belgian Federal Agency for Medicines and Health Products (FAMHP) and customs are striving, together with their global counterparts, to curtail the trafficking and distributions of these substances. At their request, suspected protein and peptide preparations are analysed in our Official Medicines Control Laboratory (OMCL). It stands to reason that a general screening method would be beneficiary in the battle against counterfeit and illegal peptide drugs. In this paper we present such general screening method employing liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the identification of counterfeit and illegal injectable peptide preparations, extended with a subsequent quantification method using ultra-high performance liquid chromatography with diode array detection (UHPLC-DAD). The screening method, taking only 30 min, is able to selectively detect 25 different peptides and incorporates the proposed minimum of five identification points (IP) as has been recommended for sports drug testing applications. The group of peptides represent substances which have already been detected in illegal and counterfeit products seized by different European countries as well as some biopharmaceutical peptides which have not been confiscated yet by the controlling agencies, but are already being used according to the many internet users forums. Additionally, we also show that when applying the same LC gradient, it is also possible to quantify these peptides without the need for derivatization or the use of expensive labelled peptides. This quantification method was successfully validated for a representative subset of 10 different peptides by using the "total error" approach in accordance with the validation requirements of ISO-17025., (Copyright © 2015 Elsevier B.V. All rights reserved.)

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., (Copyright © 2014 Elsevier B.V. All rights reserved.)

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., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Counterfeit and illegal pharmaceutical products are an increasing worldwide problem and constitute a major challenge for analytical laboratories to detect and characterize them. Spectroscopic techniques such as infrared spectroscopy and Raman spectroscopy have always been the first methods of choice to detect counterfeits and illegal preparations, but due to the evolution in the seized products and the necessity of risk assessment, chromatographic methods are becoming more important in this domain. This review intends to give a general overview of the techniques described in literature to characterize counterfeit and illegal pharmaceutical preparations, focusing on the role of chromatographic techniques with different detection tools.

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., (Copyright © 2013 Elsevier B.V. All rights reserved.)

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.

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 10 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 × 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 min. 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 β-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., (Copyright © 2012 Elsevier B.V. All rights reserved.)

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)., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug samples and to classify counterfeit samples in different classes following the RIVM classification system. Models were built for two data sets consisting of the Fourier Transformed Infrared spectra, the near infrared spectra and the Raman spectra for genuine and counterfeit samples of respectively Viagra(®) and Cialis(®). Easy interpretable models were obtained for both models. The models were validated for their descriptive and predictive properties. The predictive properties were evaluated using both cross validation as an external validation set. The obtained models for both data sets showed a 100% correct classification for the discrimination between genuine and counterfeit samples and 83.3% and 100% correct classification for the counterfeit samples for the Viagra(®) and the Cialis(®) data set respectively., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Pharmaceutical counterfeiting is a permanently growing problem. Control laboratories are constantly analysing counterfeit medicines. In industrialised countries, one of the main counterfeited class of medicines are erectile dysfunction drugs. This paper describes the development and validation of a fast method to detect and quantify the three authorised phosphodiesterase type 5 inhibitors and five analogues. The method is based on the use of a sub-2 microns polar-embedded column with a gradient using acetonitrile as organic modifier and 10mM ammonium formate buffer (pH 3.5) as aqueous component of the mobile phase. The separation was achieved in less than 4.5 min. The method has also been compared to the registered HPLC method for the assay of Viagra(®) which was considered as the reference method. The method is also compatible with on-line coupling mass spectrometry and will significantly reduce analysis times and solvent consumption., (Copyright © 2011 Elsevier B.V. All rights reserved.)

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., (Copyright © 2011 Elsevier B.V. All rights reserved.)

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., (Copyright © 2011 Elsevier B.V. All rights reserved.)