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See detailA BAYESIAN PROBABILITY CRITERION TO ASSESS ANALYTICAL RESULTS RELIABILITY
Rozet, Eric ULg; Lebrun, Pierre ULg; Boulanger, B et al

Conference (2013, May 21)

In pharmaceutical and biomedical industries, quantitative analytical methods such as HPLC play a key role. Indeed, the analytical results obtained from them are used to make crucial decisions such as the ... [more ▼]

In pharmaceutical and biomedical industries, quantitative analytical methods such as HPLC play a key role. Indeed, the analytical results obtained from them are used to make crucial decisions such as the release of batches of drugs, the evaluation of safety and efficacy of new drug candidates or the monitoring of patients health. Prior to their routine use, analytical methods are submitted to a stringent validation study [1] where they have to demonstrate that they are fit for their final purpose, i.e. providing accurate results: where is the analytical result, is the theoretical unknown true concentration of analyte in the sample analyzed and a regulatory acceptance limit. Typically this demonstration is made by either providing point estimates of systematic error (bias) and random error (variance) or sometimes by providing interval estimates of these statistical parameters at several well defined concentration levels of the target analyte [2]. They are then compared to maximum acceptable levels. More recently, tolerance intervals approaches have been proposed that are evaluated in a similar way at these key concentration levels [3]. However none of these decision approaches allow knowing the probability to obtain accurate results over the whole concentration range of interest: is a vector of parameters and Pmin is a minimum reliability probability. Frequentist approximations have been proposed to estimate this probability but only at the concentration levels experimentally tested [4,5]. In this work, a linear hierarchical Bayesian approach is proposed. It takes into account the potential random characteristic of the slope and intercept observed from one analytical run to the other, but it also integrates the possible covariance between the parameters. Additionally, heteroscedasticity of the residual variance over the concentration range investigated is taken into account. A situation regularly observed in practice. Finally a reliability profile for the whole concentration range studied is obtained using MCMC sampling. This profile provides the probability (Prel) to obtain accurate results over the full concentration range investigated. This profile is then compared to a minimum reliability probability (Pmin) that will define the valid concentration range of the analytical method. The usefulness of this approach is illustrated through the validation of a bioanalytical method and also compared with one concentration level at a time frequentist approaches [4,5]. [1] International Conference on Harmonization (ICH) of Technical Requirements for registration of Pharmaceuticals for Human Use Topic Q2 (R1): Validation of Analytical Procedures: Text and Methodology, Geneva, 2005. [2] A. Bouabidi and al., J. Chromatogr. A, 1217 (2010) 3180. [3] Ph. Hubert and al., J. Pharm. Biomed. Anal., 36 (2004) 579. [4] W. Dewé and al., Chemometr. Intell. Lab. Syst. 85 (2007) 262. [5] B. Govaerts and al., Qual. Reliab. Engng. Int. 24 (2008) 667. [less ▲]

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See detailDesign Spaces for Analytical Methods
Rozet, Eric ULg; Lebrun, Pierre ULg; Debrus, Benjamin ULg et al

in Trends in Analytical Chemistry [=TRAC] (2013), 42

Since the adoption of the ICH Q8 document concerning the development of pharmaceutical processes following a Quality by Design (QbD) approach, there have been many discussions on the opportunity for ... [more ▼]

Since the adoption of the ICH Q8 document concerning the development of pharmaceutical processes following a Quality by Design (QbD) approach, there have been many discussions on the opportunity for analytical method developments to follow a similar approach. A key component of the QbD paradigm is the definition of the Design Space of analytical methods where assurance of quality is provided. Several Design Spaces for analytical methods have been published, stressing the importance of this concept. This paper aims at explaining what is an analytical method Design Space, why it is useful for the robust development and optimization of analytical methods and how to build such a Design Space. A strong emphasis is made by distinguishing the usual mean response surface approach, overlapping mean response surfaces and the desirability function one to other probabilistic approaches as only these last ones correctly define a Design Space. In addition, recent publications assessing the Design Space of analytical methods are reviewed and discussed. [less ▲]

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See detailRELIABILITY OF ANALYTICAL METHODS’ RESULTS: A BAYESIAN APPROACH TO ANALYTICAL METHOD VALIDATION
Rozet, Eric ULg; Govaerts, B.; Lebrun, Pierre ULg et al

Conference (2012, March)

Methods validation is mandatory in order to assess the fitness of purpose of the developed analytical method. Of core importance at the end of the validation is the evaluation of the reliability of the ... [more ▼]

Methods validation is mandatory in order to assess the fitness of purpose of the developed analytical method. Of core importance at the end of the validation is the evaluation of the reliability of the individual results that will be generated during the routine application of the method. Regulatory guidelines provide a general framework to assess the validity of a method, but none address the issue of results reliability. In this study, a Bayesian approach is proposed to address this concern. Results reliability is defined here as “the probability ()π of an analytical method to provide analytical results within predefined acceptance limits ()X()λ± around their reference or conventional true concentration values ()Tμ over a defined concentration range and under given environmental and operating conditions.” By providing the minimum reliability probability (minπ needed for the subsequent routine application of the method, as well as specifications or acceptance limits ()λ±, the proposed Bayesian approach provides the effective probability of obtaining reliable future analytical results over the whole concentration range investigated. This is summarized in a single graph: the reliability profile. This Bayesian reliability profile is also compared to two frequentist approaches, the first one derived from the work of Dewé et al. [1] and the second proposed by Govaerts et al. [2]. Furthermore, the applicability of the Bayesian reliability profile is shown using as example the validation of a bioanalytical method dedicated to the determination of ketoglutaric acid (KG) and hydroxymethylfurfural (HMF) in human plasma by SPE-HPLC-UV. [1] Dewé W., Govaerts B., Boulanger B., Rozet E., Chiap P., Hubert Ph., Chemometr. Intell. Lab. Syst. 85 (2007) 262-268. [2] B. Govaerts, W. Dewé, M. Maumy, B. Boulanger, Qual. Reliab. Engng. Int. 24 (2008) 667-680. [less ▲]

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See detailSMALL SAMPLE SIZE CAPABILITY INDEX FOR ASSESSING VALIDITY OF ANALYTICAL METHODS
Rozet, Eric ULg; Boulanger, B.; Ziemons, Eric ULg et al

Poster (2012, March)

Analytical method’s capability evaluation can be a useful methodology to assess the fitness of purpose of these methods for their future routine application. However, care on how to compute the capability ... [more ▼]

Analytical method’s capability evaluation can be a useful methodology to assess the fitness of purpose of these methods for their future routine application. However, care on how to compute the capability indices has to be made. Indeed, the commonly used formulas to compute capability indices such as Cpk, will highly overestimate the true capability of the methods. Especially during methods validation or transfer, there are only few experiments performed and, using in these situations the commonly applied capability indices to declare a method as valid or as transferable to a receiving laboratory will conduct to inadequate decisions. In this work, an improved capability index, namely Cpk-tol and the corresponding estimator of proportion of non conforming results (tolCpk−π) is proposed. Through Monte-Carlo simulations, they have been shown to greatly increase the estimation of analytical methods capability in particular in low sample size situations as encountered during methods validation or transfer. Additionally, the usefulness of this capability index is illustrated through several case studies. [less ▲]

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See detailARE CAPABILITY INDICES USEFULL TO ASSESS ANALYTICAL METHODS VALIDITY ?
Rozet, Eric ULg; Bouabidi, Abderrahim ULg; Talbi, M. et al

Poster (2012, February)

Analytical methods capability evaluation can be a useful methodology to assess the fitness of purpose of these methods for their future routine application. However, care on how to compute the capability ... [more ▼]

Analytical methods capability evaluation can be a useful methodology to assess the fitness of purpose of these methods for their future routine application. However, care on how to compute the capability indices has to be made. Indeed, the commonly used formulas to compute capability indices such as Cpk, will highly overestimate the true capability of the methods. Especially during methods validation or transfer, there are only few experiments performed and, using in these situations the commonly applied capability indices to declare a method as valid or as transferable to a receiving laboratory will conduct to inadequate decisions. In this work, an improved capability index, namely Cpk-tol and the corresponding estimator of proportion of non conforming results ( ) is proposed. Through Monte-Carlo simulations, they have been shown to greatly increase the estimation of analytical methods capability in particular in low sample size situations as encountered during methods validation or transfer. Additionally, the usefulness of this capability index is illustrated through several case studies covering applications commonly encountered in the pharmaceutical industry. Finally a methodology to determine the optimal sample size required to validate analytical methods is also given using the proposed capability metric. [less ▲]

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See detailCOMBINATION OF INDEPENDENT COMPONENT ANALYSIS, DESIGN OF EXPERIMENTS AND DESIGN SPACE FOR A NOVEL METHODOLOGY TO DEVELOP CHROMATOGRAPHIC METHODS
Rozet, Eric ULg; Debrus, Benjamin ULg; Lebrun, Pierre ULg et al

Poster (2012, February)

As defined by ICH [1] and FDA, Quality by Design (QbD) stands for “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process ... [more ▼]

As defined by ICH [1] and FDA, Quality by Design (QbD) stands for “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management”. A risk–based QbD–compliant approach is proposed for the robust development of analytical methods. This methodology based on Design of Experiments (DoE) to study the experimental domain models the retention times at the beginning, the apex and the end of each peak corresponding to the compounds of a mixture and uses the separation criterion (S) rather than the resolution (RS) as a Critical Quality Attribute. Stepwise multiple linear regressions are used to create the models. The estimated error is propagated from the modelled responses to the separation criterion (S) using Monte Carlo simulations in order to estimate the predictive distribution of the separation criterion (S) over the whole experimental domain. This allows finding ranges of operating conditions that will guarantee a satisfactory quality of the method in its future use. These ranges define the Design Space (DS) of the method. In chromatographic terms, the chromatograms processed at operating conditions within the DS will assuredly show high quality, with well separated peaks and short run time, for instance. This Design Space can thus be defined as the subspace, necessarily encompassed in the experimental domain (i.e. the knowledge space), within which the probability for the criterion to be higher than an advisedly selected threshold is higher than a minimum quality level. Precisely, the DS is defined as “the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” [1]. Therefore, this DS defines a region of operating conditions that provide prediction of assurance of quality rather than only quality as obtained with traditional mean response surface optimisation strategies. For instance, in the liquid chromatography there is a great difference in e.g. predicting a resolution (RS) higher than 1.5 vs. predicting that the probability for RS to be higher than 1.5 (i.e. P(RS> 1.5)) is high. The presentation of this global methodology will be illustrated for the robust optimisation and DS definition of several liquid chromatographic methods dedicated to the separation of different mixtures: pharmaceutical formulations, API and impurities/degradation products, plant extracts, separation of enantiomers, … References [1] International Conference on Harmonisation (ICH) of Technical Requirements for Registration of Pharmaceuticals for Human Use, Topic Q8(R2): Pharmaceutical development, Geneva, 2009. [less ▲]

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See detailQUALITY BY DESIGN COMPLIANT METHOD VALIDATION
Rozet, Eric ULg; Boulanger, B.; Hubert, Philippe ULg

Poster (2012, February)

Analytical method validation is a mandatory step to evaluate the ability of developed methods to provide accurate results for their routine application in order to trust the critical decisions that will ... [more ▼]

Analytical method validation is a mandatory step to evaluate the ability of developed methods to provide accurate results for their routine application in order to trust the critical decisions that will be made with them. Even if several guidelines exist to help perform analytical method validations (ICH Q2R1 [1], USP <1225> [2], …) there is still the need to clarify the meaning and interpretation of analytical method validation criteria and methodology. Indeed, actually method validation is mostly realised as the traditional check list implementation of e.g. the ICH Q2R1 or USP <1225> method validation requirements. However, within the trend of Quality by Design [3], there is the need to switch from this traditional vision to an analytical method validation really adding value and providing a high level of assurance of analytical methods results reliability. Yet, different interpretations can be made of the validation guidelines as well as for the definitions of the validation criteria. This will lead to diverse experimental designs implemented to try fulfilling these criteria. Finally, different decision methodologies can also be interpreted from these guidelines. Therefore, the risk that a validated analytical method may be unfit for its future purpose will depend on a personal interpretation of these guidelines. The objective of this presentation is thus to show that analytical method validation should be planned and performed by first starting with the end in mind: what is the objective of the analytical methods under study? In such a way analytical method validation is coherent with the actual Quality by Design regulatory expectations. The risk of having validated an analytical method unfit for its purpose is strongly reduced as well as the risk of generating Out of Specification (OOS) results due to an unfit method. References [1] International Conference on Harmonisation (ICH) of Technical Requirements for registration of Pharmaceuticals for Human Use, Topic Q2 (R1): Validation of Analytical Procedures: Text and Methodology, Geneva, 2005. [2] USP 33 NF 28 S1, U.S. Pharmacopeia, 2007. USP–NF General Chapter <1225>. [3] International Conference on Harmonisation (ICH) of Technical Requirements for Registration of Pharmaceuticals for Human Use, Topic Q8(R2): Pharmaceutical development, Geneva, 2009. [less ▲]

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See detailReply to the responses on the comments on “Uncertainty profiles for the validation of analytical methods” by Saffaj and Ihssane
Rozet, Eric ULg; Ziemons, Eric ULg; Marini Djang'Eing'A, Roland ULg et al

in Talanta (2012), 100

Saffaj et al., recently proposed an uncertainty profile for evaluating the validity of analytical methods using the statistical methodology of γ-confidence β-content tolerance intervals. This profile ... [more ▼]

Saffaj et al., recently proposed an uncertainty profile for evaluating the validity of analytical methods using the statistical methodology of γ-confidence β-content tolerance intervals. This profile assesses the validity of the method by comparing the method measurement uncertainty to a pre defined acceptance limit stating the maximum uncertainty suitable for the method under study. In this letter we comment on the response (T. Saffaj, B. Ihssane, Talanta 94 (2012) 361-362) these authors have made to our previous letter (E. Rozet, E. Ziemons, R.D. Marini, B. Boulanger, Ph. Hubert, Talanta 88 (2012) 769–771). In particular, we demonstrate that β-expectation tolerance intervals are prediction intervals, we show that β-expectation tolerance intervals are highly usefull for assessing analytical methods validation and for estimating measurement uncertainty and finally we show what are the differences and implications for these two topics (validation and uncertainty) when using either the methodology of β-expectation tolerance intervals or the γ-confidence β-content tolerance tolerance interval one. [less ▲]

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See detailDesign Space ou Espace de Conception
Boulanger, B.; Lebrun, Pierre ULg; Rozet, Eric ULg et al

Scientific conference (2011, November 29)

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See detailNew Methodology for the Development of Chromatographic Methods
Rozet, Eric ULg; Debrus, Benjamin ULg; Lebrun, Pierre ULg et al

Conference (2011, September 08)

As defined by ICH [1] and FDA, Quality by Design (QbD) stands for “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process ... [more ▼]

As defined by ICH [1] and FDA, Quality by Design (QbD) stands for “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management”. A risk–based QbD–compliant approach is proposed for the robust development of analytical methods. This methodology based on Design of Experiments (DoE) to study the experimental domain models the retention times at the beginning, the apex and the end of each peak corresponding to the compounds of a mixture and uses the separation criterion (S) rather than the resolution (RS) as a Critical Quality Attribute. Stepwise multiple linear regressions are used to create the models. The estimated error is propagated from the modelled responses to the separation criterion (S) using Monte Carlo simulations in order to estimate the predictive distribution of the separation criterion (S) over the whole experimental domain. This allows finding ranges of operating conditions that will guarantee a satisfactory quality of the method in its future use. These ranges define the Design Space (DS) of the method. In chromatographic terms, the chromatograms processed at operating conditions within the DS will assuredly show high quality, with well separated peaks and short run time, for instance. This Design Space can thus be defined as the subspace, necessarily encompassed in the experimental domain (i.e. the knowledge space), within which the probability for the criterion to be higher than an advisedly selected threshold is higher than a minimum quality level. Precisely, the DS is defined as “the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” [1]. Therefore, this DS defines a region of operating conditions that provide prediction of assurance of quality rather than only quality as obtained with traditional mean response surface optimisation strategies. For instance, in the liquid chromatography there is a great difference in e.g. predicting a resolution (RS) higher than 1.5 vs. predicting that the probability for RS to be higher than 1.5 (i.e. P(RS> 1.5)) is high. The presentation of this global methodology will be illustrated for the robust optimisation and DS definition of several liquid chromatographic methods dedicated to the separation of different mixtures: pharmaceutical formulations, API and impurities/degradation products, plant extracts, separation of enantiomers, … References [1] International Conference on Harmonisation (ICH) of Technical Requirements for Registration of Pharmaceuticals for Human Use, Topic Q8(R2): Pharmaceutical development, Geneva, 2009. Acknowledgements A research grant from the Belgium National Fund for Scientific Research (F.R.S-FNRS) to E. Rozet is gratefully acknowledged. [less ▲]

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See detailEvaluating the reliability of analytical results using a probability criterion: a Bayesian perspective
Rozet, Eric ULg; Govaerts, B.; Lebrun, Pierre ULg et al

in Analytica Chimica Acta (2011), 705

Methods validation is mandatory in order to assess the fitness of purpose of the developed analytical method. Of core importance at the end of the validation is the evaluation of the reliability of the ... [more ▼]

Methods validation is mandatory in order to assess the fitness of purpose of the developed analytical method. Of core importance at the end of the validation is the evaluation of the reliability of the individual results that will be generated during the routine application of the method. Regulatory guidelines provide a general framework to assess the validity of a method, but none address the issue of results reliability. In this study, a Bayesian approach is proposed to address this concern. Results reliability is defined here as “the probability of an analytical method to provide analytical results within predefined acceptance limits around their reference or conventional true concentration values over a defined concentration range and under given environmental and operating conditions.” By providing the minimum reliability probability needed for the subsequent routine application of the method, as well as specifications or acceptance limits , the proposed Bayesian approach provides the effective probability of obtaining reliable future analytical results over the whole concentration range investigated. This is summarized in a single graph: the reliability profile. This Bayesian reliability profile is also compared to two frequentist approaches, the first one derived from the work of Dewé et al. [Dewé W., Govaerts B., Boulanger B., Rozet E., Chiap P., Hubert Ph., Chemometr. Intell. Lab. Syst. 85 (2007) 262-268] and the second proposed by Govaerts et al. [B. Govaerts, W. Dewé, M. Maumy, B. Boulanger, Qual. Reliab. Engng. Int. 24 (2008) 667-680]. Furthermore, to illustrate the applicability of the Bayesian reliability profile, this approach is also applied here to a bioanalytical method dedicated to the determination of ketoglutaric acid (KG) and hydroxymethylfurfural (HMF) in human plasma by SPE-HPLC-UV. [less ▲]

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