A multivariate data analysis approach to tablet sticking on an industrial scale: a qualitative case study of an ibuprofen-based formulation. (26th November 2022)

• Record Type: Journal Article
• Authors: Dembélé, Mahamadou; Hudon, Sophie; Simard, Jean-Sébastien; Abatzoglou, Nicolas; Gosselin, Ryan
• Is Part Of: Pharmaceutical development and technology. Volume 27:Number 10(2022)
• Page Start: 1093
• Page End: 1109
• Publication Date: 2022-11-26
• Subjects: Tablet sticking -- powder compaction -- multivariate data analysis -- principal component analysis -- linear discriminant analysis -- partial least squares-discriminant analysis; Drug delivery systems -- Periodicals; Pharmaceutical technology -- Periodicals; Drugs -- Administration -- Research -- Periodicals; Drug Delivery Systems -- Periodicals; Pharmaceutical Preparations -- Periodicals; Technology, Pharmaceutical -- Periodicals; 615
• DOI: 10.1080/10837450.2022.2153866
• Languages: English
• ISSNs: 1083-7450

Abstract

Abstract: Objectives: Sticking is one of the most common and damaging issues that occur during tablet manufacturing. Sticking is the adhesion of powder onto tooling surfaces during compression. Because of the numerous factors involved in its occurrence, understanding tablet sticking requires the simultaneous investigation of these factors to clarify their possible interactions. However, conducting such a study experimentally can present a significant financial and technical burden. In this study, we aimed to leverage the large amount of data that is usually generated during industrial manufacturing to gain insights into sticking. Methods: This was achieved by collecting and analyzing a total of 71 historical batches that used an ibuprofen-based formulation. We associate each batch with a hundred parameters, including a qualitative descriptor of sticking, and employ a predefined methodology based primarily on multivariate data analysis. Results and Conclusions: Our results highlight the role of lubrication, water content, and the low melting point of ibuprofen in its sticking tendency. Based on these findings, we propose and discuss an industrial manufacturing data analysis approach to sticking and its associated systematic methodology, consisting of collection, exploration, and data modeling.