A Bayesian implementation of Quality-by-Design for the development of Cationic Nano-Lipid for siRNA Transfection.
Fiche publication
Date publication
octobre 2022
Journal
IEEE transactions on nanobioscience
Auteurs
Membres identifiés du Cancéropôle Est :
Pr BASTOGNE Thierry
Tous les auteurs :
Bastogne T, Hassler L, Bruniaux J, Thomassin M, Gidrol X, Sulpice E, Navarro FP
Lien Pubmed
Résumé
Unlike Quality by Testing approach, where products were tested only after drug manufacturing, Quality by Design (QbD) is a proactive control quality paradigm, which handles risks from the early development steps. In QbD, regression models built from experimental data are used to predict a risk mapping called Design Space in which the developers can identify values of critical input factors leading to acceptable probabilities to meet the efficacy and safety specifications for the expected product. These empirical models are often limited to quantitative responses. Moreover, in practice the smallness and incompleteness of datasets degrade the quality of predictions. In this study, a Bayesian approach including variable selection, parameter estimation and model quality assessment is proposed and assessed using a real case study devoted to the development of a Cationic Nano-Lipid Structures for siRNA Transfection. Two original model structures are also included to describe both binary and percentage response variables. The results confirm the practical relevance and applicability of the Bayesian implementation of the QbD analysis.
Référence
IEEE Trans Nanobioscience. 2022 10 10;PP: