Stratégies analytiques pour la détection de contrefaçons de médicaments de la dysfonction érectile

Since the late eighties, when it was first mentioned, the worldwide phenomenon of pharmaceutical counterfeiting is growing. Belgian customs often encounter presumed counterfeited medical products in Belgian airports and ports because of their central position in Europe and their importance in the transit of goods. Further and deeper analyses are required to assess the counterfeit character of these goods and to provide a scientific basis for the eventual legal procedure. As reference laboratory for the federal agency for medicines and health products (FAMHP), the Scientific Institute of Public Health (IPH) frequently analyses illegal and counterfeit pharmaceutical preparations. The present research project was started with the objective of evaluating several existing methods and developing new analytical methods to detect counterfeit erectile dysfunction drugs. This thesis is focused on the analysis of illegal samples of phosphodiesterase type 5 inhibitors (PDE5-i) containing drugs because these are the most counterfeited pharmaceutical specialities in Belgium. The research was divided into a spectroscopic and a chromatographic part: Infrared based spectroscopies have already demonstrated their ability to detect counterfeit drugs. The first part of the study evaluates the capacity of each technique (mid-infrared (mid-IR), near-infrared (NIR) and Raman spectroscopy) separately and their combinations to discriminate genuine from illegal tablets. Then, the Classification And Regression Trees (CART) algorithm has been used to classify the different samples following the classification system of the Dutch National Institute for Public Health and the Environment (RIVM). The second spectroscopic approach used Raman microspectroscopy mapping to detect counterfeited Viagra®. This technique allows the detection of different compounds according to their Raman spectrum but also the study of the distribution of a selected ingredient among the core of a tablet. The chromatographic part consists of the development and validation of a new Ultra High Pressure Liquid Chromatography method coupled with a UV diode array detector (UHPLC-DAD) and compatible with mass spectrometry (MS) to detect and quantify the three authorised phosphodiesterase type 5 inhibitors (sildenafil, tadalafil and vardenafil) and five of their analogues in illegal pharmaceutical preparations. This method has been validated between +/- 5% acceptance limits using the total error approach and has been compared to the official Viagra® assay method. The ability of HPLC-UV impurity fingerprints to detect illegal samples and to predict whether a new unknown sample is genuine has also been evaluated. The developed analytical methods may be included in a general approach to detect counterfeit drugs containing PDE5-i. This generic approach may also be used to detect other types of counterfeited drugs but should therefore be adapted for each type of medicine

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

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 Transform 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

Verification of the active pharmaceutical ingredient in tablets using a low-cost near-infrared spectrometer

The present study investigated the possibilities and limitations of using a low-cost NIR spectrometer for the verification of the presence of the declared active pharmaceutical ingredients (APIs) in tablet formulations, especially for medicine screening studies in low-resource settings. Spectra from 950 to 1650 nm were recorded for 170 pharmaceutical products representing 41 different APIs, API combinations or placebos. Most of the products, including 20 falsified medicines, had been collected in medicine quality studies in African countries. After exploratory principal component analysis, models were built using data-driven soft independent modelling of class analogy (DD-SIMCA), a one-class classifier algorithm, for tablet products of penicillin V, sulfamethoxazole/trimethoprim, ciprofloxacin, furosemide, metronidazole, metformin, hydrochlorothiazide, and doxycycline. Spectra of amoxicillin and amoxicillin/clavulanic acid tablets were combined into a single model. Models were tested using Procrustes cross-validation and by projection of spectra of tablets containing the same or different APIs. Tablets containing no or different APIs could be identified with 100 % specificity in all models. A separation of the spectra of amoxicillin and amoxicillin/clavulanic acid tablets was achieved by partial least squares discriminant analysis. 15 out of 19 external validation products (79 %) representing different brands of the same APIs were correctly identified as members of the target class; three of the four rejected samples showed an API mass percentage of the total tablet weight that was out of the range covered in the respective calibration set. Therefore, in future investigations larger and more representative spectral libraries are required for model building. Falsified medicines containing no API, incorrect APIs, or grossly incorrect amounts of the declared APIs could be readily identified. Variation between different NIR-S-G1 spectroscopic devices led to a loss of accuracy if spectra recorded with different devices were pooled. Therefore, piecewise direct standardization was applied for calibration transfer. The investigated method is a promising tool for medicine screening studies in low-resource settings

Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy

Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules

Application of surface-enhanced Raman chemical imaging (SER-CI) to quantification in pharmaceutical tablets

Since its discovery, the application of Surface-Enhanced Raman Spectroscopy (SERS) has extended to various areas, including the pharmaceutical field, facing up challenges in the SERS substrate and sample preparation. This paper will present how beneficially SERS can be applied to the quantification of low-dose compounds in pharmaceutical tablets, focusing on the determination of 4-aminophenol, a toxic impurity, in acetaminophen tablets

User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules

In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure–retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user’s decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps

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

Raman chemical imaging, a new tool in kidney stone structure analysis: Case-study and comparison to Fourier Transform Infrared spectroscopy

<div><p>Background and objectives</p><p>The kidney stone’s structure might provide clinical information in addition to the stone composition. The Raman chemical imaging is a technology used for the production of two-dimension maps of the constituents' distribution in samples. We aimed at determining the use of Raman chemical imaging in urinary stone analysis.</p><p>Material and methods</p><p>Fourteen calculi were analyzed by Raman chemical imaging using a confocal Raman microspectrophotometer. They were selected according to their heterogeneous composition and morphology. Raman chemical imaging was performed on the whole section of stones. Once acquired, the data were baseline corrected and analyzed by MCR-ALS. Results were then compared to the spectra obtained by Fourier Transform Infrared spectroscopy.</p><p>Results</p><p>Raman chemical imaging succeeded in identifying almost all the chemical components of each sample, including monohydrate and dihydrate calcium oxalate, anhydrous and dihydrate uric acid, apatite, struvite, brushite, and rare chemicals like whitlockite, ammonium urate and drugs. However, proteins couldn't be detected because of the huge autofluorescence background and the small concentration of these poor Raman scatterers. Carbapatite and calcium oxalate were correctly detected even when they represented less than 5 percent of the whole stones. Moreover, Raman chemical imaging provided the distribution of components within the stones: nuclei were accurately identified, as well as thin layers of other components. Conversion of dihydrate to monohydrate calcium oxalate was correctly observed in the centre of one sample. The calcium oxalate monohydrate had different Raman spectra according to its localization.</p><p>Conclusion</p><p>Raman chemical imaging showed a good accuracy in comparison with infrared spectroscopy in identifying components of kidney stones. This analysis was also useful in determining the organization of components within stones, which help locating constituents in low quantity, such as nuclei. However, this analysis is time-consuming, making it more suitable for research studies rather than routine analysis.</p></div