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Page de résumé pour ULgetd-12192012-155142

Auteur : Lebrun, Pierre
E-mail de l'auteur : P.Lebrun@ulg.ac.be
URN : ULgetd-12192012-155142
Langue : Français/French
Titre : Bayesian Design Space applied to Pharmaceutical Development
Intitulé du diplôme : Doctorat en sciences
Département : FS - Département de mathématique
Jury :
Nom : Titre :
Eilers, P. Membre du jury/Committee Member
Govaerts, B. Membre du jury/Committee Member
Hubert, Philippe Membre du jury/Committee Member
Lambert, Philippe Membre du jury/Committee Member
Peterson, J. Membre du jury/Committee Member
Albert, Adelin Président du jury/Committee Chair
Boulanger, Bruno Promoteur/Director
Haesbroeck, Gentiane Promoteur/Director
Mots-clés :
  • Pharmaceutical Development/Développement pharmaceutique
  • Bayesian Design Space/Espace de Conception Bayesien
Date de soutenance : 2012-06-21
Type d'accès : Public/Internet
Résumé :

Given the guidelines such as the Q8 document published by the International Conference on Harmonization (ICH), that describe the “Quality by Design” paradigm for the Pharmaceutical Development, the aim of this work is to provide a complete methodology addressing this problematic. As a result, various Design Spaces were obtained for different analytical methods and a manufacturing process.

In Q8, Design Space has been defined as the “the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” for the analytical outputs or processes involved in Pharmaceutical Development. Q8 is thus clearly devoted to optimization strategies and robustness studies.

In the beginning of this work, it was noted that existing statistical methodolo- gies in optimization context were limited as the predictive framework is based on mean response predictions. In such situations, the data and model uncertainties are generally completely ignored. This often leads to increase the risks of taking wrong decision or obtaining unreliable manufactured product. The reasons why it happens are also unidentified. The “assurance of quality” is clearly not addressed in this case.

To improve the predictive nature of statistical models, the Bayesian statistical framework was used to facilitate the identification of the predictive distribution of new outputs, using numerical simulations or mathematical derivations when possi- ble.

By use of the improved models in a risk-based environment, separation analytical methods such as the high performance liquid chromatography were studied. First, optimal solutions of separation of several compounds in mixtures were identified. Second, the robustness of the methods was simultaneously assessed thanks to the risk-based Design Space identification. The usefulness of the methodology was also demonstrated in the optimization of the separation of subsets of relevant compounds, without additional experiments.

The high guarantee of quality of the optimized methods allowed easing their use for their very purpose, i.e., the tracing of compounds and their quantification. Transfer of robust methods to high-end equipments was also simplified.

In parallel, one sub-objective was the total automation of analytical method de- velopment and validation. Some data treatments including the Independent Com- ponent Analysis and clustering methodologies were found more than promising to provide accurate automated results.

Next, the Design Space methodology was applied to a small-scale spray-dryer manufacturing process. It also allowed the expression of guarantees about the quality of the obtained powder.

Finally, other predictive models including mixed-effects models were used for the validation of analytical and bio-analytical

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