Impact of Process Parameters and Formulation Properties on Dissolution Performance of an Extended Release Tablet: a Multivariate Analysis

• Published in: Journal of pharmaceutical innovation Vol. 17; no. 3; pp. 892 - 910
• Main Authors: Diab, Samir, Bano, Gabriele, Dhenge, Ranjit M., Taylor, James
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2022
• Subjects: Biochemical Engineering; Biomedical and Life Sciences; Biomedicine; Biotechnology; Industrial and Production Engineering; Original Article; Pharmacology/Toxicology; Multivariate analysis (MVA); Oral solid dose (OSD); Principal component analysis (PCA); Tablet manufacturing; Partial least squares (PLS) regression
• ISSN: 1872-5120; 1939-8042
• DOI: 10.1007/s12247-021-09570-5

Purpose Quality assurance in oral solid dose (OSD) manufacturing is essential for therapeutic efficacy and patient safety. This study uses multivariate analysis (MVA) to correlate process and material inputs vs. dissolution behaviour to enhance process understanding and elucidate potential control improvements in a commercial tabletting line for an extended release (ER) OSD product. Methods Partial least squares (PLS) regression and principal component analysis (PCA) are used to elucidate drivers for active pharmaceutical ingredient (API) dissolution variation from several batches produced at commercial scale. A PLS model is constructed to predict dissolution variation from granulator temperature variation for engineering teams to proactively flag potential quality risks. Results Variations in granulator temperatures, drug impurity content, and excipient size properties are highly correlated with the small levels of dissolution variation across all batches. The PLS model for dissolution variation showed good fit ( R 2  > 0.96) and will be corroborated with future tablet batches. Conclusion Multivariate modelling was used to elucidate drivers for dissolution variation in an OSD tabletting line and to flag potential product quality issues. The mechanism by which impurity levels affect the considered API’s dissolution will be the subject of future study. The methodology used here can be applied to other processes given sufficient data to gain insight into product quality drivers.