Caract risation du milieu accumul dans une chambre de r g n ration apr(s section du nerf sciatique chez le rat

SIGLEBSE B226399J / UCL - Université Catholique de LouvainBEBelgiu

New methodology for the automated development of analytical method in liquid chromatography for the analysis of unknown compounds mixtures

peer reviewedDe nos jours, de nombreuses stratégies d’optimisation de méthodes chromatographiques sont disponibles. Néanmoins, le développement de méthodes chromatographiques reste l’étape la plus limitante dans les processus de synthèse ou d’identification de nouvelles molécules pouvant conduire à des agents thérapeutiques ou à de nouveau biomarqueurs et cela malgré la disponibilité de nouvelles technologies tant en chimie (chimie combinatoire, High throughput screening…) qu’en biochimie analytique (protéomique, métabolomique, herbal fingerprinting…). L’objectif de l’étude présentée dans ces pages est d’éprouver une nouvelle méthodologie de développement automatisé de méthodes chromatographiques combinant la planification expérimentale, l’analyse en composantes indépendantes, l’analyse de la propagation de l’erreur prédictive et la modélisation par régression linéaire multiple. Finalement, cette méthodologie automatisée a permis de séparer avec succès les composés d’un mélange inconnu.Nowadays, many strategies to optimize chromatographic methods are available. However, the development of chromatographic methods remains the most limiting step in the process of synthesis or identification of new molecules that could lead to therapeutic agents or new biomarkers despite the availability of new technologies both in chemistry (chemical combinatorial, high throughput screening...) and in analytical biochemistr y (proteomics, metabolomics, herbal ... fingerprinting). Therefore, the aim of this study is to test a new methodology for developing automated chromatographic methods combining experimental planning, independent component analysis, analysis of predictive error propagation and multiple linear regression modeling. Finally, this automated methodology has enabled us to successfully separate the components of an unknown mixture.Automated Development of Analytical Method, ADA

A systematic approach for the development of liquid chromatographic methods

Developing chromatographic methods can be laborious, time-consuming, and expensive. The definition of a strategy to find rapid separation conditions has therefore become of prime importance since the analytical development has to follow the rhythm of the generation of new compounds. To meet this demand, chemists clearly need the utilization of automation and standardized strategies of work. This paper reports a standardized and fully automated approach dedicated to the development of liquid chromatographic (LC) methods, which are widely used to provide information about chemical reactions, such as impurity profiles and structure or potency determinations. The present approach consists of automatically screening samples with predefined chromatographic conditions. Chromatography is performed under the gradient mode, and the whole dataset is generated in less than 1 day. Even if the screening does not directly provide the final conditions for the separation of the compounds of interest, it generally gives sufficient information to allow a rapid optimization of the chromatographic separation (typically less than 24 h). Different examples of development of LC methods using this strategy are presented

Development of a new predictive modelling technique to find with confidence equivalence zone and design space of chromatographic analytical methods

A new method for modelling chromatographic responses is presented as a critical piece for the achievement of automated development of analytical methods. This methodology is based on four parts. First, we propose to use a very little set of statistical equations to create predictive models for retention time based responses as the apex, the width and the asymmetry of peaks. Second, an experimental design is set up to realize experiments. Third, using grid search over the domain, multi criteria decision is taken with respect to different local or global optimization criteria, used as desirability functions. This allows finding an optimal chromatogram. Fourth, we advice to investigate how the predictive error of the models propagates around optimal solution. This allows to give confidence in the optimal solution, in finding a set of zones that presumably will give an acceptable solution. Design spaces can be derived with a similar technique. The approach is exemplified with a real case and predictions of models at optimal analytical conditions are validated through new experiments. Flexibility is left over all the presented methodology.Automated development of analytical methods (ADAM