Pierre-Yves Sacre

Comparison and combination of spectroscopic techniques for the detection of counterfeit medicines

During this study, Fourier transform infrared spectroscopy (FT-IR), near infrared spectroscopy (NIR) and Raman spectroscopy were applied to 55 samples of counterfeit and imitations of Viagra and 39 samples of counterfeit and imitations of Cialis. The aim of the study was to investigate which of these techniques and associations of them were the best for discriminating genuine from counterfeit and imitation samples. Only the regions between 1800-400 cm(-1) and 7000-4000 cm(-1) were used for FT-IR and NIR spectroscopy respectively. Partial least square analysis has been used to allow the detection of counterfeit and imitation tablets. It is shown that for the Viagra samples, the best results were provided by a combination of FT-IR and NIR spectroscopy. On the other hand, the best results for the Cialis samples were provided by the combination of NIR and Raman spectroscopy (1400-1190 cm(-1)). These techniques not only permitted a clear discrimination between genuine and counterfeit or imitation samples but also the distinction of clusters among illegal samples. This might be interesting for forensic investigations by authorities.

Chromatography in the detection and characterization of illegal pharmaceutical preparations.

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.

Data processing of vibrational chemical imaging for pharmaceutical applications.

Vibrational spectroscopy (MIR, NIR and Raman) based hyperspectral imaging is one of the most powerful tools to analyze pharmaceutical preparation. Indeed, it combines the advantages of vibrational spectroscopy to imaging techniques and allows therefore the visualization of distribution of compounds or crystallization processes. However, these techniques provide a huge amount of data that must be processed to extract the relevant information. This review presents fundamental concepts of hyperspectral imaging, the basic theory of the most used chemometric tools used to pre-process, process and post-process the generated data. The last part of the present paper focuses on pharmaceutical applications of hyperspectral imaging and highlights the data processing approaches to enable the reader making the best choice among the different tools available.

Detection of counterfeit Viagra® by Raman microspectroscopy imaging and multivariate analysis.

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.

Development and validation of a ultra-high-performance liquid chromatography-UV method for the detection and quantification of erectile dysfunction drugs and some of their analogues found in counterfeit medicines

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.

Classification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines.

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.

A fast Ultra High Pressure Liquid chromatographic method for qualification and quantification of pharmaceutical combination preparations containing paracetamol, acetyl salicylic acid and/or antihistaminics.

A fully validated UHPLC method for the identification and quantification of pharmaceutical preparations, containing paracetamol and/or acetyl salicylic acid, combined with anti-histaminics (phenylephrine, pheniramine maleate, diphenhydramine, promethazine) and/or other additives as quinine sulphate, caffeine or codeine phosphate, was developed. The proposed method uses a Waters Acquity BEH C18 column (2 mm × 100 mm, 1.7 μm) with a gradient using an ammonium acetate buffer pH 4.0 as aqueous phase and methanol as organic modifier. The obtained method was fully validated based on its measurement uncertainty (accuracy profile) and robustness tests. Calibration lines for all components were linear within the studied ranges. The relative bias and the relative standard deviations for all components were respectively smaller than 1.5% and 2%, the β-expectation tolerance limits did not exceed the acceptance limits of 10% and the relative expanded uncertainties were smaller than 5% for all of the considered components. A UHPLC method was obtained for the identification and quantification of these kind of pharmaceutical preparations, which will significantly reduce analysis times and workload for the laboratories charged with the quality control of these preparations.

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

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.

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 new criterion to assess distributional homogeneity in hyperspectral images of solid pharmaceutical dosage forms

During galenic formulation development, homogeneity of distribution is a critical parameter to check since it may influence activity and safety of the drug. Raman hyperspectral imaging is a technique of choice for assessing the distributional homogeneity of compounds of interest. Indeed, the combination of both spectroscopic and spatial information provides a detailed knowledge of chemical composition and component distribution. Actually, most authors assess homogeneity using parameters of the histogram of intensities (e.g. mean, skewness and kurtosis). However, this approach does not take into account spatial information and loses the main advantage of imaging. To overcome this limitation, we propose a new criterion: Distributional Homogeneity Index (DHI). DHI has been tested on simulated maps and formulation development samples. The distribution maps of the samples were obtained without validated calibration model since different formulations were under investigation. The results obtained showed a linear relationship between content uniformity values and DHI values of distribution maps. Therefore, DHI methodology appears to be a suitable tool for the analysis of homogeneity of distribution maps even without calibration during formulation development.