References of "Mbinze Kindenge, Jérémie"
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See detailANALYTICAL DESIGN SPACE STRATEGY FOR THE DEVELOPMENT OF LC METHODS TO COMBAT POTENTIALLY COUNTERFEIT ANTIBIOTIC DRUGS
Dispas, Amandine ULg; Mbinze Kindenge, Jérémie ULg; Lebrun, Pierre ULg et al

Conference (2013, July)

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See detailAPPLICATION OF AN INNOVATIVE DESIGN SPACE OPTIMIZATION STRATEGY TO THE DEVELOPMENT OF LC METHODS TO COMBAT POTENTIALLY COUNTERFEIT NONSTEROIDAL ANTIINFLAMMATORY DRUGS
Mbinze Kindenge, Jérémie ULg; Lebrun, Pierre ULg; Debrus, Benjamin ULg et al

in Journal of Chromatography. A (2012), 1263

In the context of the battle against counterfeit medicines, an innovative methodology has been used to develop rapid and specific high performance liquid chromatographic methods to detect and determine 18 ... [more ▼]

In the context of the battle against counterfeit medicines, an innovative methodology has been used to develop rapid and specific high performance liquid chromatographic methods to detect and determine 18 non-steroidal anti-inflammatory drugs, 5 pharmaceutical conservatives, paracetamol, chlorzoxazone, caffeine and salicylic acid. These molecules are commonly encountered alone or in combination on the market. Regrettably, a significant proportion of these consumed medicines are counterfeit or substandard, with a strong negative impact in countries of Central Africa. In this context, an innovative design space optimization strategy was successfully applied to the development of LC screening methods allowing the detection of substandard or counterfeit medicines. Using the results of a unique experimental design, the design spaces of 5 potentially relevant HPLC methods have been developed, and transferred to an ultra high performance liquid chromatographic system to evaluate the robustness of the predicted DS while providing rapid methods of analysis. Moreover, one of the methods has been fully validated using the accuracy profile as decision tool, and was then used for the quantitative determination of three active ingredients and one impurity in a common and widely used pharmaceutical formulation. The method was applied to 5 pharmaceuticals sold in the Democratic Republic of Congo. None of these pharmaceuticals was found compliant to the European Medicines Agency specifications [less ▲]

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See detailAPPLICATION OF AN INNOVATIVE DESIGN SPACE OPTIMIZATION STRATEGY TO THE DEVELOPMENT OF LC METHODS TO COMBAT POTENTIALLY COUNTERFEIT NSAIDS
Mbinze Kindenge, Jérémie ULg; Lebrun, Pierre ULg; Debrus, Benjamin ULg et al

Poster (2012, June)

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See detailTHE DEVELOPMENT OF LC GENERIC ANALYTICAL METHODS TO FIGHT COUNTERFEIT NSAIDS USING DESIGN SPACE OPTIMISATION STRATEGY
Mbinze Kindenge, Jérémie ULg; Lebrun, Pierre ULg; Debrus, Benjamin ULg et al

Conference (2012, May)

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See detailApplication d'une nouvelle méthode de développement de méthodes chromatographiques pour lutter contre la contrefaçon des médicaments anti-inflammatoires
Dispas, Amandine ULg; Lebrun, Pierre ULg; Mbinze Kindenge, Jérémie ULg et al

Conference (2011, December)

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See detailIMPLEMENTATION OF GENERIC ANALYTICAL METHODS TO FIGHT AGAINST COUNTERFEIT MEDICINES
Marini Djang'Eing'A, Roland ULg; Mbinze Kindenge, Jérémie ULg; Debrus, Benjamin ULg et al

Conference (2011, September)

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See detailDesign Space and desirability index. A Bayesian predictive risk-based approach to flexibly achieve multi-criteria decision methods.
Lebrun, Pierre ULg; Boulanger, Bruno; Hubert, Philippe ULg et al

Conference (2011, March 02)

The Design Space (DS) is defined as the set of factors settings (input conditions) that will provide results at least better than pre-defined acceptance limits. The proposed methodology aims at ... [more ▼]

The Design Space (DS) is defined as the set of factors settings (input conditions) that will provide results at least better than pre-defined acceptance limits. The proposed methodology aims at identifying a region in the space of factors that will likely provide satisfactory results during the future use of an analytical method or process in routine, through an optimization process. In a Bayesian framework, the responses are modelled using a multivariate multiple regression model allowing deriving their joint predictive posterior distribution. On the basis of this consequent distribution, a multi-criteria risk-based decision is taken with respect to the pre-defined acceptance limits. This aims to identify the DS. In this context, desirability methodologies are also applied to take the risk-based decision in a more flexible way. An example based on high-performance liquid chromatography illustrates the applicability of the methodology with highly correlated and constrained responses. [less ▲]

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