Eric ZIEMONS | Université de Liège (original) (raw)

Papers by Eric ZIEMONS

PLOS ONE, Aug 11, 2023

The negative consequences of Substandard and falsified (SF) medicines are widely documented nowad... more The negative consequences of Substandard and falsified (SF) medicines are widely documented nowadays and there is still an urgent need to find them in more efficient ways. Several screening tools have been developed for this purpose recently. In this study, three screening tools were used on 292 samples of ciprofloxacin and metronidazole collected in Cameroon. Each sample was then analyzed by HPLC and disintegration tests. Seven additional samples from the nitro-imidazole (secnidazole, ornidazole, tinidazole) and the fluoroquinolone (levofloxacin, ofloxacin, norfloxacin, moxifloxacin) families were analyzed to mimic falsified medicines. Placebo samples that contained only inert excipients were also tested to mimic falsified samples without active pharmaceutical ingredient (API). The three screening tools implemented were: a simplified visual inspection checklist, a low-cost handheld near infrared (NIR) spectrophotometer and paper analytical devices (PADs). Overall, 61.1% of the samples that failed disintegration and assay tests also failed the visual inspection checklist test. For the handheld NIR, one-class classifier models were built to detect the presence of ciprofloxacin and metronidazole, respectively. The APIs were correctly identified in all the samples with sensitivities and specificities of 100%. However, the importance of a representative and up-to-date spectral database was underlined by comparing models built with different calibration set spanning different variability spaces. The PADs were used only on ciprofloxacin samples and detected the API in all samples in which the presence of ciprofloxacin was confirmed by HPLC. However, these PADs were not specific to ciprofloxacin since they reacted like ciprofloxacin to other fluoroquinolone compounds. The advantages and drawbacks of each screening tool were highlighted. They are promising means in the frame of early detection of SF medicines and they can increase the speed

In the pharmaceutical field, analysis of tablets by Raman hyperspectral imaging is widely used fo... more In the pharmaceutical field, analysis of tablets by Raman hyperspectral imaging is widely used for quality control purpose and has been now included in the general chapters of the European Pharmacopeia. However, data obtained can be consequent to analyze and implies to use appropriated chemometric tools. Most of the time, factorial decomposition methods (i.e Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS)) can be applied, excepted for the analysis of big data matrices, as well as in the presence of many constituents. Moreover, even when the composition is known, the MCR resolution can be challenging because some low variances sources can be diluted in the process of unmixing and can hardly be resolved unless information on the expected sample composition. Moreover, it can exist minor compounds presents in a few pixels which can be missed in the MCR process. To bypass these limitations, one possibility can be to step back to the analysis of individual pixels, which somehow would be the most efficient method for database matching. The objective of the present study is to develop a pixel-based identification (PBI) approach to elucidate chemical composition of Raman hyperspectral images of complex pharmaceutical formulations. The proposed approach relies on the identification of Essential Spectral pixels (ESP). The proposed study was evaluated on both known and unknown tablets composition. The known formulations were made of polymorphic forms of carbamazepine (case 1) and piroxicam (case 2) to mimic minor compounds (from 0.1%w/w to 5%w/w). The seven unknown samples were falsified chloroquine (case 3) which were seized during the COVID-19 pandemic [3]. Raman hyperspectral imaging analyses of samples were performed with a LabRAM HR Evolution (Horiba scientific) equipped with an EMCCD detector (1600 × 200-pixels sensor) (Andor Technology Ltd.), a Leica 50x Fluotar LWD objective and a 785 nm laser with a power of 45mW at sample (XTRA II single frequency diode laser, Toptica Photonics AG). For both case 1 and 3, the whole tablet surface was analyzed with a 150 x 150 mapping and a step size of 87µm (total map size of ~13 x 13 mm²). For case 2, the middle of the tablet surface was mapped with a step size of 5.5 μm over a 5.5 x 5.5 mm², providing a 1000 x 1000 mapping. Three different approaches were then evaluated on each map to select pixels: (i) Kennard-Stone randomized, (ii) randomized selection and (iii) ESP selection, which were subsequently matched with the in-house database by using correlation coefficient (CC). For case 1 and case 2, the ESP approach compared to the other pixel-selection algorithms has shown the best results in terms of correlation coefficient but also with the smallest analysis time, with 50 seconds for the classical data size and 2 minutes for the big map size. The ESP approach was thus applied on falsified medicines and enabled to get the entire sample composition even for complex formulation (from 4 to 9 chemical compounds) with correlation coefficient superior to 0.80. After gathering the ESP, a classical least squares was applied and allowed to show that even chemical information localised in a unique pixel had been elucidated, as it can be seen in Figure 1. The proposed study highlighted the potential of the PBI approach for chemical identification purposes. It has been shown that, for known samples, both tiny and huge amount of data can be analyzed without the need of the entire map, by selecting only a few percentages of pixels (~8% of the initial data). The proposed methodology allowed to keep even the chemical information which was not in the in-house database which is very interesting in case of falsified medicines purposes. The global conclusion of this study is about the potential applicability of the methodology to other hyperspectral imaging techniques or matrices. Indeed, thanks to the inherent properties of the essential spectral pixel algorithm, the only requirement for PBI is to have at least one pure pixel by component. In case of mixed spectra, the use of the ESP could be a pre-processing step like, to reduce data dimensionality, which has been successfully demonstrated

Analytica Chimica Acta, Apr 1, 2021

Hyperspectral imaging has been widely used for different kinds of applications and many chemometr... more Hyperspectral imaging has been widely used for different kinds of applications and many chemometric tools have been developed to help identifying chemical compounds. However, most of those tools rely on factorial decomposition techniques that can be challenging for large data sets and/or in the presence of minor compounds. The present study proposes a pixel-based identification (PBI) approach that allows readily identifying spectral signatures in Raman hyperspectral imaging data. This strategy is based on the identification of essential spectral pixels (ESP), which can be found by convex hull calculation. As the corresponding set of spectra is largely reduced and encompasses the purest spectral signatures, direct database matching and identification can be reliably and rapidly performed. The efficiency of PBI was evaluated on both known and unknown samples, considering genuine and falsified pharmaceutical tablets. We showed that it is possible to analyze a wide variety of pharmaceutical formulations of increasing complexity (from 5 to 0.1% (w/w) of polymorphic impurity detection) for medium (150 x 150 pixels) and big (1000 x 1000 pixels) map sizes in less than 2 min. Moreover, in the case of falsified medicines, it is demonstrated that the proposed approach allows the identification of all compounds, found in very different proportions and, sometimes, in trace amounts. Furthermore, the relevant spectral signatures for which no match is found in the reference database can be identified at a later stage and the nature of the corresponding compounds further investigated. Overall, the provided results show that Raman hyperspectral imaging combined with PBI enables rapid and reliable spectral identification of complex pharmaceutical formulations.

Analytica Chimica Acta, Mar 1, 2022

Multivariate curve resolution unmixing of hyperspectral imaging data can be challenging when low ... more Multivariate curve resolution unmixing of hyperspectral imaging data can be challenging when low sources of variance are present in complex samples, as for minor (low-concentrated) chemical compounds in pharmaceutical formulations. In this work, it was shown how the reduction of hyperspectral imaging data matrices through the selection of essential spectra can be crucial for the analysis of complex unknown pharmaceutical formulation applying Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). Results were obtained on simulated datasets and on real FT-IR and Raman hyperspectral images of both genuine and falsified tablets. When simulating the presence of minor compounds, different situations were investigated considering the presence of single pixels of pure composition as well as binary and ternary mixtures. The comparison of the results obtained applying MCR-ALS on the reduced data matrices with those obtained on the full matrices revealed unequivocal: more accurate decomposition could be achieved when only essential spectra were analyzed. Indeed, when analyzing the full dataset, MCR-ALS failed resolving minor compounds even though pure spectra were provided as initial estimation, as shown for Raman hyperspectral imaging data obtained on a medicine sample containing 7 chemical compounds. In contrast, when considering the reduced dataset, all minor contributions (down to 1 pixel over 17,956) were successfully unmixed. The same conclusion could be drawn from the results obtained analysing FT-IR hyperspectral imaging data of a falsified medicine.

Talanta, Jul 1, 2020

Evaluation of the analytical performances of two Raman handheld spectrophotometers for pharmaceut... more Evaluation of the analytical performances of two Raman handheld spectrophotometers for pharmaceutical solid dosage form quantitation, Talanta (2020), doi:

Journal of Pharmaceutical and Biomedical Analysis, Feb 1, 2021

reference to the experimental data, under the so-called maximum entropy principle. Recent practic... more reference to the experimental data, under the so-called maximum entropy principle. Recent practical formulations of this approach involve simulations carried out over multiple replicas or iterative ensemble-correction procedures based on the determination of several (Lagrange) parameters. Here, we present an alternative, self-learning approach to sample molecular ensembles compatible with experimental data with the minimal possible bias on the simulation trajectories. The method does not require multiple replicas and is based on adding an adaptive bias potential during the simulation that discourages the sampling of conformations that are not consistent with the experimental measurements. To illustrate this approach, we applied this novel simulation technique to spin-labeled T4-lysozyme, targeting a set of spin-spin distance distributions measured by DEER/EPR spectroscopy. We show how the proposed method is able to efficiently sample the experimental distance distributions without altering uncorrelated degrees of freedom. We anticipate that this new simulation approach will be widely useful to obtain conformational ensembles compatible with diverse types of experimental measurements of biomolecular dynamics.

Nowadays, data management and data analysis are more and more present in the scientist’s life. In... more Nowadays, data management and data analysis are more and more present in the scientist’s life. Indeed, there is an increase of cutting-edge technologies in the analytical field, which provide high quality data but in high amount. In some cases, it is possible increase the data analysis power of a basic computer by parallelizing functions. However, central processing unit (CPU) computing is quickly limited. Another option is to perform analyses with the Graphics Processing Unit (GPU). However, it requires a high knowledge of coding and most usual toolboxes do not support GPU computing. That is why some chemometric strategies have to be developed to be able to analyse such amount of data with an accessible software, such as the MATLAB® environment, on an affordable workstation. In this study, several chemometrics algorithms will be evaluated for the data analysis of an infrared chemical image of a pharmaceutical tablet. The image (3.5 million of spectra of 767 wavenumbers) has been acquired on a FT-IR Cary 670/620 Agilent series microscope (Agilent Technologies). The file size of the raw image data is ~40GB and is impossible to analyse even on a workstation due to “Out of Memory” error. The goal of this work is to show that specific chemometrics strategies coupled to a little bit of coding enables the analysis keeping only the relevant information by reducing the size of the matrix in a smart way

Inside a research unit, there are several kind of information which are gathered in a continuous ... more Inside a research unit, there are several kind of information which are gathered in a continuous flow from operators. It is important to centralize all of the information not to lose any interesting result. Because of the cost and/or the non-applicability of commercial database software, it is interesting to build its own one. Indeed, in the case of spectroscopic data, the use of a market database software is not user-friendly because, stored data cannot be directly manipulated in multivariate analyses applications. The database under development actually contains the data of various pharmaceutical samples (genuine and falsified) obtained on several spectroscopic devices. For this application, a Matlab® graphical user interface (GUI) is being developed. It provides the access to spectroscopic data for any operator and allows the automatic implementation of new instruments and/or new formulation groups. Moreover, in case of falsification suspicion, another GUI has been developed to provide qualitative information regarding the composition of the medicines. It is an interesting tool because it provides an instantaneously visual comparison between reference database and the unknown spectrum. Moreover, statistical tools, as Hit Quality Index or correlation coefficient, can provide a numerical result to quantify the match between spectra. In the future, results from other kind of techniques (e.g. HPLC, dissolution curves, photos) will be added to centralize samples information

Journal of Pharmaceutical and Biomedical Analysis

Chemometrics applied to spectroscopic measurements such as near-infrared are gaining more and mor... more Chemometrics applied to spectroscopic measurements such as near-infrared are gaining more and more importance for quality control of pharmaceutical products. Handheld near-infrared devices show great promise as a medicines quality screening technique for post-marketing surveillance. These devices are able to detect substandard and falsified medicines in pharmaceutical supply chains and enable rapid action before these medicines reach patients. The instrumental and environmental changes, expected or not, can adversely affect the analytical performances of prediction models developed for routine applications. Based on a previous study, PLS prediction models were developed and validated on three similar handheld NIR transmission spectrophotometers of the same model and from same company. These models have shown to be effective in analyzing metformin tablet samples, but significant spectral differences between handheld systems complicated their deployment for routine analysis. In this study, different strategies have been applied and compared to correct the instrumental variations, including global modelling (GM) and calibration transfer methods (Direct Standardization, DS; Spectral Space Transformation, SST and Slope/Bias correction, SBC), considering the RMSEP and the accuracy profile as assessment criteria. The transfer methods showed good capabilities to maintain the predictive performances comparable to that of the global modelling approach, except for a remaining slight bias. This approach is interesting since very few standardization samples are required to develop an adequate transfer model. GM, SST and SBC were able to correct/handle drifts in the spectral responses of different handheld instruments and thus may help to avoid the need for a long, laborious, and costly full recalibration process due to inter-instrument variations.

Talanta Open, 2021

Abstract In recent decades, more than 15% of the antimalarials marketed in low- and middle-income... more Abstract In recent decades, more than 15% of the antimalarials marketed in low- and middle-income countries have been of poor quality, in which quinoline derivatives and quinine-based formulations account for 21%. Near infrared spectroscopy (NIR) was chosen for its fast and inexpensive test properties as well as the ability of using handheld devices to monitor drugs directly on the field. Data driven - soft independent modelling of class analogy (DD-SIMCA) and partial least squares (PLS) regression models were developed for qualitative and quantitative purpose, respectively. The specificity and selectivity tests were performed using the DD-SIMCA models on the placebo, the quinidine and cinchonine standard samples. Then, PLS regression methods have been developed and validated for the quality control of quinine dosage forms manufactured by a major local manufacturer in the Democratic Republic of Congo (DRC). Calibration and validation samples were prepared by dissolving quinine sulphate /quinine hydrochloride in the presence of excipients in HCl 1M. The opportunity to work with dissolved quinine with a cheap and readily available medium in low and middle income countries allowed analysis of different pharmaceutical forms (oral drops, solutions for injection and tablets) with the same regression model. DD-SIMCA models have demonstrated for both equipment perfect authentication of quinine and good discrimination of the two alkaloids close to quinine namely cinchonine and quinidine. The NIR PLS regression models were successfully validated using the total error approach with acceptance limits set at ± 10% with a risk level of 5%. The predictive performance of the methods developed was tested in terms of robustness.

Since last decades, the world has known significant changes in the pharmaceutical products sale. ... more Since last decades, the world has known significant changes in the pharmaceutical products sale. The emergence of the internet trade is an important issue because it is easy to sell medicines without passing any control. Moreover, in low- and middle-income countries (LMIC), the number of local pharmacies has grown, increasing the risk to have substandard or falsified medicines. Indeed, according to the World Health Organization (WHO) it is difficult to ensure quality medicines due to areas conflict, corrupted governments and poor health system [1]. For that reason, several analytical techniques have been developed since last decade. One of the most interesting tool is the Minilab, developed by the Global Pharma Health Fund (GPHF), which is a mobile mini-laboratory for fast drug quality control. Moreover, Raman spectroscopy has gained a great interest because it can be used at any step of analytical chain or on the fieldwork with handheld devices. However, spectroscopic data implies development of chemometrics models to gather relevant information. Several unsupervised techniques have already been used to authenticate drug products [2-3]. Due to the intrinsic properties of classification methods, class modeling is more appropriated to this kind of analysis. Indeed, falsified medicines can be quite different from the calibration set, so that, it does not have sense to attribute a class meanwhile it is dissimilar to the calibration set. In this study, the performances of two class-modeling techniques will be evaluated on handheld Raman spectra, to separate falsified medicines from authentic drugs. Three different generics of paracetamol, ibuprofen and artemether-lumefantrine with different dosage, dosage form and formulations were analyzed. Most of them were gathered in local Belgium pharmacies and other were gathered in Africa (artemether-lumefantrine formulations). Samples were analyzed with a handheld Raman spectrophotometer Pharma 21CFR part 11 qualified (Truscan RM, Thermo Scientific, USA) directly through the blister. In order to have a good representativeness of intra-batch variability, 10 tablets were analyzed per sample. The acquisition parameters were set to default. Two models were tested: one-class PLS (OC-PLS) [4] and the data driven-soft independent modeling class analogy (DD-SIMCA) [5]. All the computations were done in Matlab® (R2017b). The calibration and validation set was the same for each model and composed of 60 spectra for each, with different batch number. Because of the nature of each algorithm, there is a significant difference in terms of separation. Looking at Figure 1, the separation of dosage form for ibuprofen is much different between the two models. For the DD-SIMCA, it is more difficult to separate the long acting release from the soft capsule/coated tablet compared to the OC-PLS model. For the other API, similar results are obtained for the dosage and for the brand. It seems that the OC-PLS is more sensitive to the small spectral variabilities. In the case of artemether-lumefantrine formulations, the separation between samples is much more difficult. Indeed, the lumefantrine is a high Raman scatterer. This can explain that the signal of artemether and excipients is difficult to access. Elsewhere, in terms of development, the DD-SIMCA is much harder to optimize because there are more tunable parameters than for OC-PLS. Furthermore, both models are really influenced by spectral pretreatment. An optimization has to be done for each. The authentication of pharmaceutical products by handheld Raman spectroscopy has been possible thanks to class modeling. Both tested algorithms shown interesting results regarding the separation of samples depending on their characteristics. The optimization of data processing and pre-processing is the key-step to improve as sensitivity as specificity of both class modeling methods

Cannabis sativa L. has one of the most controversial histories in our society. After many decades... more Cannabis sativa L. has one of the most controversial histories in our society. After many decades of prohibition, due to the presence in its inflorescences of the psychotropic Δ9-tetrahydrocannabinol (THC), nowadays its possible therapeutic role is getting attention from the scientific community. It follows that many countries are legalizing cannabis products containing THC for recreational and medical use. In parallel, another cannabinoid, naturally synthetized by the plant, is currently under the spotlight: cannabidiol (CBD). According to the legislation of most countries in Europe, cannabis with a THC content higher than 0.2-1% (depending on the country) is classified as illicit drug. On the other hand, CBD has not a psychotropic effect and cannabis with various contents of CBD can be easily found in dedicated shops. This type of cannabis is named “CBD like” to be distinguished from the illicit one, “THC like”. It is clear that the coming of these products in the legal market pos...

Journal of Pharmaceutical and Biomedical Analysis, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.