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See detailBayesian hierarchical linear regression for the validation of analytical methods
Lebrun, Pierre ULg; Boulanger, Bruno ULg; Rozet, Eric ULg et al

Conference (2010, May)

Analytical quantitative methods are widely used to quantify analytes of interest, for instance in pharmaceutical formulations, linking an observed response to the concentration of one compound of the ... [more ▼]

Analytical quantitative methods are widely used to quantify analytes of interest, for instance in pharmaceutical formulations, linking an observed response to the concentration of one compound of the formulation. Current methodologies to validate these analytical methods are based on one-way ANOVA random effect model in order to estimate repeatability and intermediate precision variances. This model is then applied several times at different concentration levels over a range of concentrations where the method is intended to be used, assuming independency between the levels. In this way, the capacity of the method to be able to quantify accurately is assessed at various concentration levels, and the method is said to be fitted for purpose (or valid) at the concentration level(s) where it shows trueness and precision that are fully acceptable, i.e. within predefined acceptance limits. Problem of such approach is the amount of data required and the time needed to collect them, while small sample sizes (small number of series and of replicates per series) are often preferred and practiced by laboratories. A better use of the data could then be envisaged. In this presentation, we take into account the response-concentration relationship that exists by the use of a hierarchical linear regression model. Instead of fitting a model at each concentration level that is assessed, only one model is studied. We show how the Bayesian framework is well adapted to this task. Also, as a predictive tool, we naturally derive beta-expectation and beta-gamma content tolerance intervals by means of MCMC simulations. The Bayesian modeling can also include informative prior information whenever justified, leading to reliable decisions given the domain knowledge. [less ▲]

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See detailCritical analysis of several analytical method validation strategies in the framework of the fit for purpose concept.
Rozet, Eric ULg; Bouabidi, A.; Fillet, Marianne ULg et al

in Journal of Chromatography. A (2010), 1217

Analytical method validation is a mandatory step at the end of the development in all analytical laboratories. It is a highly regulated step of the life cycle of a quantitative analytical method. However ... [more ▼]

Analytical method validation is a mandatory step at the end of the development in all analytical laboratories. It is a highly regulated step of the life cycle of a quantitative analytical method. However, even if some documents have been published there is a lack of clear guidance for the methodology to follow to adequately decide when a method can be considered as valid. This situation has led to the availability of several methodological approaches and it is therefore the responsibility of the analyst to choose the best one. The classical decision processes encountered during method validation evaluation are compared, namely the descriptive, difference and equivalence approaches. Furthermore a validation approach using accuracy profile computed by means of beta-expectation tolerance interval and total measurement error is also available. In the present paper all of these different validation approaches were applied to the validation of two analytical methods. The evaluation of the producer and consumer risks by Monte Carlo simulations were also made in order to compare the appropriateness of these various approaches. The classical methodologies give rise to inadequate and contradictory conclusions which do not allow them to answer adequately the objective of method validation, i.e. to give enough guarantees that each of the future results that will be generated by the method during routine use will be close enough to the true value. It is found that the validation methodology which gives the most guarantees with regards to the reliability or adequacy of the decision to consider a method as valid is the one based on the use of the accuracy profile. [less ▲]

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See detailExpected Design Space: a Bayesian perspective based on modelling, prediction and multi-criteria decision method
Lebrun, Pierre ULg; Boulanger, Bruno ULg

Conference (2009, October)

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

The Design Space (DS) is defined as the set of factors settings (input conditions) that provides 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. First, in a Bayesian framework, DS is derived from the joint predictive posterior distribution of the responses in a multivariate multiple regression problem using non-informative as well as informative prior distribution of the parameters. The case of DS in presence of highly correlated responses will be covered. Second, a multi-criteria decision is taken with respect to the pre-defined acceptance limits, aiming to identify the DS of any analytical methods or other processes. A strong link is made between acceptance limits and objective functions to optimize, using desirability functions and index. Examples based on high-performance liquid chromatography (HPLC) methods will be given, illustrating the applicability of the methodology with highly correlated and constrained responses. [less ▲]

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See detailA new statistical method for the automated detection of peaks in UV-DAD chromatograms of a sample mixture
Debrus, Benjamin ULg; Lebrun, Pierre ULg; Ceccato, Attilio ULg et al

in Talanta (2009), 79

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 ... [more ▼]

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–UVDAD 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, itwas 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 could be shortened when using this type of methodologies. [less ▲]

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See detailA risk-based analysis of the AAPS conference report on quantitative bioanalytical methods validation and implementation.
Boulanger, Bruno ULg; Rozet, Eric ULg; Moonen, Francois et al

in Journal of Chromatography. B : Analytical Technologies in the Biomedical & Life Sciences (2009), 877(23), 2235-43

The 3rd American Association of Pharmaceutical Scientists (AAPS)/Food and Drug Administration (FDA) Bioanalytical workshop in 2006 concluded with several new recommendations regarding the validation of ... [more ▼]

The 3rd American Association of Pharmaceutical Scientists (AAPS)/Food and Drug Administration (FDA) Bioanalytical workshop in 2006 concluded with several new recommendations regarding the validation of bioanalytical methods in a report published in 2007. It was aimed to conciliate or adapt validation principles for small and large molecules and an opportunity to revisit some of the major decision rules related to acceptance criteria given the experience accumulated since 1990. The purpose here is to provide a "risk-based" reading of the recommendations of 3rd AAPS/FDA Bioanalytical Workshop. Five decision rules were compared using simulations: the proposed pre-study FDA and Total Error Rules, the rules based on the beta-Expectation Tolerance and beta-gamma-Content Tolerance Interval and, finally, the 4-6-20 rule for in-study acceptance of runs. The simulation results demonstrated that the beta-Expectation Tolerance Rule controls appropriately the risk. The beta-gamma-Content Tolerance Interval was found to be too conservative, depending on the objective, and to lead to a high rate of rejection of procedures that could be considered as acceptable. On the other side, the FDA and the AAPS/FDA workshop Total Error Rule, combined or not, did not achieve their intended objective. With these rules, the risk is high to deliver results in study that would not meet the targeted acceptance criteria. This can be explained because, first, there is confusion between the quality of a procedure and the fitness of purpose of the results it could produce and, second, between the initial performances of a procedure, for example evaluated during pre-study validation and the quality of the future results. [less ▲]

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See detailUniversal applicability of Total Error for the validation of bioanalytical methods
Rozet, Eric ULg; Boulanger, Bruno ULg; Rudaz, S. et al

Conference (2009)

An innovative universal strategy using Total Error is thus proposed to decide about the method’s validity that controls the risk of accepting an unsuitable assay together with the ability to predict the ... [more ▼]

An innovative universal strategy using Total Error is thus proposed to decide about the method’s validity that controls the risk of accepting an unsuitable assay together with the ability to predict the reliability of future results. Several examples of applications of this validation methodology to various types of assays [LC-MS, ELISA, Bio-Assays] will be presented. [less ▲]

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See detailTotal Error for the validation of bioanalytical methods
Rozet, Eric ULg; Boulanger, Bruno ULg; Rudaz, S. et al

Conference (2009)

Detailed reference viewed: 49 (14 ULg)
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See detailValidation for analytical methods : reducing the risk of decisions. Tutorial 04
Boulanger, Bruno ULg; Moonen, François; Hubert, Philippe ULg

Conference (2009)

Detailed reference viewed: 38 (6 ULg)
See detailEvaluation of decision methodologies for analytical method validation
Rozet, Eric ULg; Rudaz, S.; Bouabidi, A. et al

Poster (2009)

Detailed reference viewed: 29 (2 ULg)
See detailTotal Error-Based Criterion for Analytical Method Transfer
Rozet, Eric ULg; Dewe, W.; Boulanger, Bruno ULg et al

Poster (2009)

Detailed reference viewed: 22 (1 ULg)