References of "Hubert, Philippe"
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See detailChimie Analytique - Manuel de Travaux Pratiques
Dispas, Amandine ULg; Ziemons, Eric ULg; Hubert, Philippe ULg

Learning material (2013)

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See detailAnalyzing spectral data for the selection of a calibration model
Ziemons, Eric ULg; Moonen, François; Hubert, Philippe ULg

Patent (2013)

The invention relates to method of analyzing spectral data for the selection of a calibration model, relating spectra of a substance to physical or chemical parameter of the substance, over a ... [more ▼]

The invention relates to method of analyzing spectral data for the selection of a calibration model, relating spectra of a substance to physical or chemical parameter of the substance, over a predetermined range of the physical or chemical parameter. [less ▲]

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See detailChemical imaging of small molecules from simple to complex matrices: Quantitative approaches based on Surface Enhanced Raman scattering
De Bleye, Charlotte ULg; Sacre, Pierre-Yves ULg; Chavez, Pierre-François ULg et al

Conference (2013, July)

Surface Enhanced Raman scattering (SERS) allows to dramatically exalt the Raman diffusion of molecules absorbed or very closed to rough metallic surfaces while keeping their structural information. SERS ... [more ▼]

Surface Enhanced Raman scattering (SERS) allows to dramatically exalt the Raman diffusion of molecules absorbed or very closed to rough metallic surfaces while keeping their structural information. SERS chemical imaging, presenting a high specificity and sensibility, allows acquiring a visual representation of samples combining spectral and spatial measurements. This technique could become a powerful tool in pharmaceutical and biological analysis enabling to identify and quantify molecules thanks to chemometric evaluation while looking at their distribution or their interactions. In this context, SERS chemical imaging is investigated in detection or quantitative determination of molecules in pharmaceutical and biological matrices. The feasibility of making quantitative measurements using SERS is evaluated on small target molecules models such as 4-aminophenol and lactate. Firstly, a SERS method to quantify 4-aminophenol which is the primary impurity of acetaminophen coming from its degradation during the storage or from its synthesis was developed on a real pharmaceutical formulation. The standard addition method was selected as calibration method in order to take into account the matrix effect coming from the different components of the latter. Despite the well-known stability and repeatability problems of SERS, the method was thoroughly validated by means of accuracy profiles as decision tool. Moreover, this validation methodology allowed to define a first estimation of the real analytical performance of the technique. Secondly, the detection of lactate, which is a critical metabolite implicated in several metabolic disorders, was successfully tested in the physiological concentration in a simple matrix. Preliminary results for the determination of this metabolic biomarker were also very promising allowing to consider more complex matrices. Based on these results, SERS chemical imaging was implemented to detect 4-aminophenol in a pharmaceutical tablet formerly pulverised by a SERS substrate. Through this imaging technique, it was not only possible to detect the presence of the impurity at the limit of specification of 0.1% (w/w) but it was also possible to differentiate tablets comprising different concentrations of the latter. These promising results represent the first step towards quantitative measurements using SERS chemical imaging. [less ▲]

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See detailA New Method for Quality by Design Robust Optimization in Liquid Chromatography
Debrus, Benjamin ULg; Lebrun, Pierre ULg; Rozet, Eric ULg et al

in LC-GC Europe (2013), -(-), -

A new method to optimize liquid chromatography (LC) methods using a Quality by Design (QbD) approach is presented. This method is based on the use of design of experiments (DOE) and independent component ... [more ▼]

A new method to optimize liquid chromatography (LC) methods using a Quality by Design (QbD) approach is presented. This method is based on the use of design of experiments (DOE) and independent component analysis (ICA) to accurately estimate the modelled responses (that is, the retention times at the beginning, the apex, and the end) of each peak, even for coeluted peaks. The modelling of these responses usesmultiple linear regressions, while the propagation of the error affecting the responses and coming from the models is carried out by Monte Carlo simulation. The design space is determined as the region of assay factors where the probability to reach baseline-resolved peaks is higher than the desired level of quality. This method was applied to the optimization of the separation of nine compounds in a mixture, yielding the design space and the demonstration of robustness of the method. Finally, the method was validated. [less ▲]

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See detailInnovative Methodology for the Definition of Design Spaces of Chromatographic Methods
Rozet, Eric ULg; Debrus, B; Lebrun, Pierre ULg et al

Conference (2013, June 06)

As defined by ICH [1] and FDA, Quality by Design (QbD) stands for “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process ... [more ▼]

As defined by ICH [1] and FDA, Quality by Design (QbD) stands for “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management”. A risk–based QbD–compliant approach is proposed for the robust development of analytical methods. This methodology based on Design of Experiments (DoE) to study the experimental domain models the retention times at the beginning, the apex and the end of each peak corresponding to the compounds of a mixture and uses the separation criterion (S) rather than the resolution (RS) as a Critical Quality Attribute. Stepwise multiple linear regressions are used to create the models. The estimated error is propagated from the modelled responses to the separation criterion (S) using Monte Carlo simulations in order to estimate the predictive distribution of the separation criterion (S) over the whole experimental domain. This allows finding ranges of operating conditions that will guarantee a satisfactory quality of the method in its future use. These ranges define the Design Space (DS) of the method. In chromatographic terms, the chromatograms processed at operating conditions within the DS will assuredly show high quality, with well separated peaks and short run time, for instance. This Design Space can thus be defined as the subspace, necessarily encompassed in the experimental domain (i.e. the knowledge space), within which the probability for the criterion to be higher than an advisedly selected threshold is higher than a minimum quality level. Precisely, the DS is defined as “the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” [1]. Therefore, this DS defines a region of operating conditions that provide prediction of assurance of quality rather than only quality as obtained with traditional mean response surface optimisation strategies. For instance, in the liquid chromatography there is a great difference in e.g. predicting a resolution (RS) higher than 1.5 vs. predicting that the probability for RS to be higher than 1.5 (i.e. P(RS> 1.5)) is high. The presentation of this global methodology will be illustrated for the robust optimisation and DS definition of several liquid chromatographic methods dedicated to the separation of different mixtures: pharmaceutical formulations, API and impurities/degradation products, plant extracts, separation of enantiomers, … [less ▲]

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See detailA BAYESIAN PROBABILITY CRITERION TO ASSESS ANALYTICAL RESULTS RELIABILITY
Rozet, Eric ULg; Lebrun, Pierre ULg; Boulanger, B et al

Conference (2013, May 21)

In pharmaceutical and biomedical industries, quantitative analytical methods such as HPLC play a key role. Indeed, the analytical results obtained from them are used to make crucial decisions such as the ... [more ▼]

In pharmaceutical and biomedical industries, quantitative analytical methods such as HPLC play a key role. Indeed, the analytical results obtained from them are used to make crucial decisions such as the release of batches of drugs, the evaluation of safety and efficacy of new drug candidates or the monitoring of patients health. Prior to their routine use, analytical methods are submitted to a stringent validation study [1] where they have to demonstrate that they are fit for their final purpose, i.e. providing accurate results: where is the analytical result, is the theoretical unknown true concentration of analyte in the sample analyzed and a regulatory acceptance limit. Typically this demonstration is made by either providing point estimates of systematic error (bias) and random error (variance) or sometimes by providing interval estimates of these statistical parameters at several well defined concentration levels of the target analyte [2]. They are then compared to maximum acceptable levels. More recently, tolerance intervals approaches have been proposed that are evaluated in a similar way at these key concentration levels [3]. However none of these decision approaches allow knowing the probability to obtain accurate results over the whole concentration range of interest: is a vector of parameters and Pmin is a minimum reliability probability. Frequentist approximations have been proposed to estimate this probability but only at the concentration levels experimentally tested [4,5]. In this work, a linear hierarchical Bayesian approach is proposed. It takes into account the potential random characteristic of the slope and intercept observed from one analytical run to the other, but it also integrates the possible covariance between the parameters. Additionally, heteroscedasticity of the residual variance over the concentration range investigated is taken into account. A situation regularly observed in practice. Finally a reliability profile for the whole concentration range studied is obtained using MCMC sampling. This profile provides the probability (Prel) to obtain accurate results over the full concentration range investigated. This profile is then compared to a minimum reliability probability (Pmin) that will define the valid concentration range of the analytical method. The usefulness of this approach is illustrated through the validation of a bioanalytical method and also compared with one concentration level at a time frequentist approaches [4,5]. [1] International Conference on Harmonization (ICH) of Technical Requirements for registration of Pharmaceuticals for Human Use Topic Q2 (R1): Validation of Analytical Procedures: Text and Methodology, Geneva, 2005. [2] A. Bouabidi and al., J. Chromatogr. A, 1217 (2010) 3180. [3] Ph. Hubert and al., J. Pharm. Biomed. Anal., 36 (2004) 579. [4] W. Dewé and al., Chemometr. Intell. Lab. Syst. 85 (2007) 262. [5] B. Govaerts and al., Qual. Reliab. Engng. Int. 24 (2008) 667. [less ▲]

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See detailMEASURING VARIABILITY SOURCES IN NMR METABOLOMIC STUDIES
Rozet, Eric ULg; De Tullio, Pascal ULg; Hubert, Philippe ULg et al

Conference (2013, May 13)

Due to the huge amount of information available in NMR spectra obtained from the analysis of metabolomic experiments, multivariate analysis such as Principal Component Analysis (PCA) are required to ... [more ▼]

Due to the huge amount of information available in NMR spectra obtained from the analysis of metabolomic experiments, multivariate analysis such as Principal Component Analysis (PCA) are required to understand the influence of treatments over the metabolites [1]. However, many experiments in metabolomics studies have more complexes variability structures than simply comparing several treatments: they may include time effects, biological effects such as diet or hormonal status, and other blocking factors or variability sources: samples stability, age of the individuals, pH of a buffer, days of acquisition, and so on. Metabolomic data analysis needs to extract from the spectral data matrix the variations linked to a change indicated in the factor of interest. However other sources of variability may impair this objective. This stresses the importance to discover the sources of variability of the spectral metabolomic data using appropriate methodology. Classically, to analyze such data analysis of variance (ANOVA) or multivariate ANOVA (MANOVA) [2] is used. However direct application of these methodologies to NMR spectra obtained from structured metabolomics studies is inappropriate or impossible. More complex data analyses methodologies are required to understand the importance of the various factors implied in the experiments and to provide a measure of their variance components. Three related methodologies have been proposed to achieve this: ASCA [3], ANOVA-PCA [4] and AComDim [5]. The ASCA and ANOVA-PCA methodologies combine first an analysis of variance step (ANOVA) and then a PCA step. The AComDim one adds to the output of the ANOVA-PCA step a multi-block analysis. In this presentation, the usefulness and applicability of these advanced techniques to data analysis of NMR metabolomic spectra are provided to highlight the increase of knowledge gained and the estimation of main sources of variability arising in an experimental setup. Two NMR databases will be used [6]. The first one concerns human serum analyzed by 1H-NMR where three random factors are present: day of measurement (3 days), sample (2 samples per individual) and replication of analyses as well as two fixed controlled factors, time of measurement after thawing (2 times) and two protein suppression methods for the spectral pre-treatment. The second database is about the 1H-NMR analyses of rats’ urine where two different concentrations of citrate and of hippurate were deliberately added and three other sources of variability are present: urine pool diluted or not diluted, repetitions of analyses, days of analyses (three days), as well as two different spectral pre-treatment procedures. [less ▲]

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See detailNON-ANTHOCYANIN POLYPHENOLS QUANTIFICATION IN EUTERPE OLERACEA FRUITS BY A UHPLC−LTQ-ORBITRAP MS METHOD
Dias, Aecio; Rozet, Eric ULg; Chataigné, G et al

Poster (2013, May)

High antioxidant and anti-inflammatory activities have been observed from non-anthocyanin polyphenols of E. oleracea fruits [1-2]. The aim of this work was to quantify major non-anthocyanin polyphenols by ... [more ▼]

High antioxidant and anti-inflammatory activities have been observed from non-anthocyanin polyphenols of E. oleracea fruits [1-2]. The aim of this work was to quantify major non-anthocyanin polyphenols by an accurate UHPLC−LTQ-Orbitrap MS method. Fruits were harvested in Pará state (Brazil), processed to pulp and lyophilised. 0.5g of dry pulp powder was defatted by sonication with petroleum ether. The residue was then extracted five times with 5mL MeOH each time for 30 min (optimized conditions giving recovery rates > 90%). The extract was evaporated to dryness with a RapidVap® evaporator at 35°C. Solubilization of the dried extract was realised using 40% MeOH. For the UHPLC quantification, a HSS C18 column (1.8µm) was used with a gradient elution of MeOH and H2O both with 0.1% HCOOH and the ionisation source (ESI) was operated in NI mode. 26 compounds were identified, among them 7 identified for the first time in this fruit. Total error and accuracy profiles were used as validation criteria. Calibration in the matrix was found to be more accurate than calibration without matrix. Trueness, repeatability, intermediate precision, selectivity, response function, linearity and LOD/LOQ for 12 non-anthocyanin phenolic compounds were evaluated and the quantification method validated. [1] J. Kang et al., Food Chem. 122 (2010) 610–617. [2] J. Kang et al., Food Chem. 128 (2011) 152–157. [less ▲]

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See detailFalsification des médicaments: mythe ou réalité ?
Marini Djang'Eing'A, Roland ULg; Fillet, Marianne ULg; Vancauwenberghe, Roy et al

Conference (2013, April 24)

La santé publique est de nos jours minée par la problématique des médicaments falsifiés ou de qualité inférieure, avec plusieurs conséquences sanitaires, économiques voire professionnelles. On estime à 7 ... [more ▼]

La santé publique est de nos jours minée par la problématique des médicaments falsifiés ou de qualité inférieure, avec plusieurs conséquences sanitaires, économiques voire professionnelles. On estime à 7% la part du marché pharmaceutique mondial que représenterait ce fléau; l’Afrique, l’Asie et de nombreux pays d'Amérique latine étant les régions les plus touchées avec plus de 30% de médicaments falsifiés. D’après l'OMS, plus de 50% des médicaments achetés à partir des sites internet illégaux sont contrefaits, annihilant très fortement les chances de succès thérapeutique. Ces médicaments viennent dans la plupart des cas des pays asiatiques et de l’Eurasie. Le trafic de faux médicaments est un crime contre l'humanité qui représente environ 50 milliards de dollars par an (10-15 % de plus que le marché de la drogue). Au travers de deux leçons, la situation de la falsification des médicaments sera présentée au grand public dans le but de le sensibiliser à ce fléau. La première leçon présentera la situation en Europe avec un accent sur la Belgique. La problématique du droit à la propriété intellectuelle et de l’encadrement législatif sera abordée, ainsi que la falsification des médicaments modernes et des phytomédicaments, ces derniers étant utilisés par plus de 40% de la population en Europe et aux Etats-Unis. Dans la seconde leçon sera abordée la situation vécue en Afrique. L’approvisionnement en médicaments de qualité par le partage de l’information sera présenté ainsi que les moyens analytiques à la disposition de ce continent pour combattre ce fléau. Des membres du Département de Pharmacie de l’Université de Liège, de l’Agence Fédérale des Médicaments et des Produits de Santé ainsi que du programme QUAMED (Quality Medicines for All) feront partager leur expérience sur cette question d’une brûlante actualité.  [less ▲]

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See detailValidation methodologies of near infrared spectroscopy methods in pharmaceutical applications
Chavez, Pierre-François ULg; De Bleye, Charlotte ULg; Sacre, Pierre-Yves ULg et al

in European Pharmaceutical Review (2013), 18(1), 3-6

As any analytical methods, a mandatory step at the end of the development of a near infrared spectroscopy (NIRS) method is the validation. This step enables to give enough guarantees that each future ... [more ▼]

As any analytical methods, a mandatory step at the end of the development of a near infrared spectroscopy (NIRS) method is the validation. This step enables to give enough guarantees that each future results coming from the application of the method in routine will be closed enough to the true value. However, from the literature, a minority of NIRS methods are thoroughly validated despite of the guidelines published by different group and regulatory authorities to help analyst to adequately decide if his method can be considered as valid. In this context, the aim of this review is to offer a critical overview of the different validation methodologies applied to assess the validity of quantitative methods using near infrared spectroscopy used in the field of pharmacy. [less ▲]

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See detailA Bayesian Design Space for analytical methods based on multivariate models and predictions
Lebrun, Pierre ULg; Boulanger, Bruno ULg; Debrus, Benjamin ULg et al

in Journal of Biopharmaceutical Statistics (2013), 23

The International Conference for Harmonization (ICH) has released regulatory guidelines for Pharmaceutical Development. In the document ICH Q8, The Design Space of a process is presented as the set of ... [more ▼]

The International Conference for Harmonization (ICH) has released regulatory guidelines for Pharmaceutical Development. In the document ICH Q8, The Design Space of a process is presented as the set of factor settings providing satisfactory results. However, ICH Q8 does not propose any practical methodology to define, derive and compute Design Space. In parallel, in the last decades, it has been observed that the diversity and the quality of analytical methods have evolved exponentially allowing substantial gains in selectivity and sensitivity. However, there is still a lack for a rationale towards the development of robust separation methods in a systematic way. Applying ICH Q8 to analytical methods provides a methodology for predicting a region of the space of factors in which results will be reliable. Combining design of experiments and Bayesian standard multivariate regression, an identified form of the predictive distribution of a new response vector has been identified and used, under non-informative as well as informative prior distributions of the parameters. From the responses and their predictive distribution, various critical quality attributes can be easily derived. This Bayesian framework was then extended to the multi-criteria setting to estimate the predictive probability that several critical quality attributes will be jointly achieved in the future use of an analytical method. An example based on a high-performance liquid chromatography (HPLC) method is given. For this example, a constrained sampling scheme was applied to ensure the modeled responses have desirable properties. [less ▲]

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