HPLC; chromatography; design of experiments; multi-criteria optimization; statistical models; error propagation; design space
Abstract :
[en] 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.
Research center :
CIRM - Centre Interdisciplinaire de Recherche sur le Médicament - ULiège
Disciplines :
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others
Author, co-author :
Lebrun, Pierre ; Université de Liège - ULiège > Département de pharmacie > Chimie analytique
Govaerts, Bernadette
Debrus, Benjamin ; Université de Liège - ULiège > Département de pharmacie > Chimie analytique
Ceccato, Attilio ; Université de Liège - ULiège > Département de pharmacie > Analyse des médicaments
Caliaro, Gabriel
Hubert, Philippe ; Université de Liège - ULiège > Département de pharmacie > Chimie analytique
Boulanger, Bruno ; Université de Liège - ULiège > Département de pharmacie > Analyse des médicaments
Language :
English
Title :
Development of a new predictive modelling technique to find with confidence equivalence zone and design space of chromatographic analytical methods
Publication date :
2008
Journal title :
Chemometrics and Intelligent Laboratory Systems
ISSN :
0169-7439
eISSN :
1873-3239
Publisher :
Elsevier Science, Amsterdam, Netherlands
Volume :
91
Pages :
4-16
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
Automated development of analytical methods (ADAM)
Schoenmakers P. Optimization of chromatographic selectivity: a guide to method development. Anal. Chim. Acta 208 (1988) 357-358
Snyder L., Kirkland J., and Glajch J. Practical HPLC Method Development. second edition (1997), Wiley-Interscience
Massart D., et al. Chemometrics: A Textbook (Data Handling in Science and Technology) (1990), Elsevier Science
Vanbel P. Development of flexible and efficient strategies for optimizing chromatographic conditions. J. Pharm. Biomed. Anal. 21 (1998) 603-610
Dewé W., Marini R., Chiap P., Hubert P., Crommen J., and Boulanger B. Development of response models for optimizing HPLC methods. Chemometr. Intell. Lab. Syst. 74 (2004) 263-268
Cox G., and Cochran W. Experimental Designs. 2nd edition (1957), Wiley
Winer B. Statistical Principles in Experimental Design. 2nd edition (1962), McGraw-Hill
ICH. Q8 Draft Guidance on Pharmaceutical Development (2004) Ver 4.3
Khuri A., and Cornell J. Response Surfaces: Designs and Analyses (1987), Marcel Dekker
G. Vivó-Truyols, New Strategies for Optimisation and Data Treatment in HPLC. PhD thesis, University of Valencia, 2005.
Boulanger B. Utilisation de l'Analyse en Composantes Independantes (ICA) pour la separation numerique des pics et la quantification automatique en CLHP-UV. Conference Chimiometrie 2006, Paris, December (2006)
Martens H., and Martens M. Experimental Designs. 2nd edition (2001), Wiley
Harrington E. The desirability function. Ind. Qual. Control 21 (1965) 494-498
Derringer G., and Suich R. Simultaneous optimization of several response variables. J. Qual. Technol. 12 4 (1980) 214-219
Le Bailly de Tilleghem C., and Govaerts B. Uncertainty propagation in multiresponse optimization using a desirability index. Tech. Rep. vol. 0532 (2005), Universite catholique de Louvain, Louvain-la-Neuve
Le Bailly de Tilleghem C., and Govaerts B. Distribution of desirability index in multicriteria optimization using desirability functions based on the cumulative distribution function of the standard normal. Tech. Rep. vol. 0531 (2005), Universite catholique de Louvain, Louvain-la-Neuve
Trautmann H., and Weihs C. Uncertainty of optimum influence factor levels in multicriteria optimization using the concept of desirability. Tech. Rep. SFB 475 vol. 23/04 (2004), Dortmund University