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One of the major issues within the context of the fully automated development of chromatographic methods consists of the automated detection and identification of peaks coming from complex samples such as multi-component pharmaceutical formulations or stability studies of these formulations. The same problem can also occur with plant materials or biological matrices. This step is thus critical and time-consuming, especially when a Design of Experiments (DOE) approach is used to generate chromatograms. The use of DOE will often maximize the changes of the analytical conditions in order to explore an experimental domain. Unfortunately, this generally provides very different and "unpredictable" chromatograms which can be difficult to interpret, thus complicating peak detection and peak tracking (i.e. matching peaks among all the chromatograms). In this context, Independent Components Analysis (ICA), a new statistically based signal processing methods was investigated to solve this problem. The ICA principle assumes that the observed signal is the resultant of several phenomena (known as sources) and that all these sources are statistically independent. Under those assumptions, ICA is able to recover the sources which will have a high probability of representing the constitutive components of a chromatogram. In the present study, ICA was successfully applied for the first time to HPLC-UV-DAD chromatograms and it was shown that ICA allows differentiation of noise and artifact components from those of interest by applying clustering methods based on high-order statistics computed on these components. Furthermore, on the basis of the described numerical strategy, it was also possible to reconstruct a cleaned chromatogram with minimum influence of noise and baseline artifacts. This can present a significant advance towards the objective of providing helpful tools for the automated development of liquid chromatography (LC) methods. It seems that analytical investigations cou

One of the major difficulties within the context of the fully automated development of chromatographic methods consists in the automated detection of the peaks coming from complex matrices such as multicomponent pharmaceutical formulations or stability studies of these formulations. The same problem can also occur with plant materials or biological matrices. This step is thus critical and time-consuming, especially when Designs of Experiments (DOE) are used to generate chromatograms. The use of DOE leads to maximize the changes of the analytical conditions in order to cleverly explore an experimental domain. Unfortunately, this generally provides very different and “uncontrolled” chromatograms which can be hardly interpretable, complicating picking and peak tracking. In this context, numerical signal processing methods such as Independent Components Analysis (ICA) was investigated to solve this problem. The ICA principle assumes that the observed signal is the resultant of several phenomena (known as sources) and that all these sources are statistically independent. ICA is able to estimate sources which most often seem judicious to represent the constitutive components of a chromatogram. In the present study, ICA was applied to HPLC-UV-DAD chromatograms and we showed that ICA allows differentiating noises and artifacts components from those of interest, by applying clustering methods based on high-order statistics computed on these components. Furthermore, on the basis of the described numerical strategy, it was also possible to rebuild a cleaned chromatogram easily legible. This represents a very significant advance towards our final objective, the fully automated development of liquid chromatography (LC) method.

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. © 2007 Elsevier B.V. All rights reserved.

All analysts face the same situations as method validation is the process of proving that an analytical method is acceptable for its intended purpose. In order to resolve this problem, the analyst refers to regulatory or guidance documents, and therefore the validity of the analytical methods is dependent on the guidance, terminology and methodology, proposed in these documents. It is therefore of prime importance to have clear definitions of the different validation criteria used to assess this validity. It is also necessary to have methodologies in accordance with these definitions and consequently to use statistical methods which are relevant with these definitions, the objective of the validation and the objective of the analytical method. The main purpose of this paper is to outline the inconsistencies between some definitions of the criteria and the experimental procedures proposed to evaluate those criteria in recent documents dedicated to the validation of analytical methods in the pharmaceutical field, together with the risks and problems when trying to cope with contradictory, and sometimes scientifically irrelevant, requirements and definitions. (c) 2007 Elsevier B.V. All rights reserved.

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.

In some chemical processes, extractive workup is used to remove the solvent of reaction in an aqueous layer and to isolate the product in the organic layer. Most of the time some reaction solvent is co-extracted in the organic layer, along with some water. This has a non-negligible impact on subsequent operations (washing, crystallization), and we have thus determined those levels for ten reaction solvents and five extraction solvents in order to help the process chemist in the development of efficient isolation procedures.