A multivariate raw material property database to facilitate drug product development and enable in-silico design of pharmaceutical dry powder processes

• Published in: International journal of pharmaceutics Vol. 549; no. 1-2; pp. 415 - 435
• Main Authors: Van Snick, Bernd, Dhondt, Jens, Pandelaere, Kenny, Bertels, Johny, Mertens, Roel, Klingeleers, Didier, Di Pretoro, Giustino, Remon, Jean Paul, Vervaet, Chris, De Beer, Thomas, Vanhoorne, Valérie
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 05.10.2018
• Subjects: Computer Simulation; Continuous direct compression; Databases, Chemical; Excipients - chemistry; Friction; In silico design; Material characterization; Models, Chemical; Models, Statistical; Multivariate Analysis; Multivariate data analysis; Particle Size; Permeability; Pharmaceutical drug product development; Pharmaceutical Preparations - chemistry; Porosity; Powders; Principal Component Analysis; Technology, Pharmaceutical - methods; Water - chemistry; Wettability; PSD; R2; Material characterization; MVDA; Continuous direct compression; Pharmaceutical drug product development; MCC; rpm; PCA; Multivariate data analysis; PC; In silico design; Q2X; R2X; API; PSHD; Principal component analysis
• ISSN: 0378-5173; 1873-3476
• DOI: 10.1016/j.ijpharm.2018.08.014

[Display omitted] In current study a holistic material characterization approach was proposed and an extensive raw material property database was developed including a wide variety of APIs and excipients with different functionalities. In total 55 different materials were characterized and described by over 100 raw material descriptors related to particle size and shape distribution, specific surface area, bulk, tapped and true density, compressibility, electrostatic charge, moisture content, hygroscopicity, permeability, flowability and wall friction. Principal component analysis (PCA) was applied to reveal similarities and dissimilarities between materials and to identify overarching properties. The developed PCA model allows to rationalize the number of critical characterization techniques in routine characterization and to identify surrogates for use during early drug product development stages when limited amounts of active pharmaceutical ingredients are available. Additionally, the developed database will be the basis to build predictive models for in silico process and formulation development based on (a selection of) property descriptors.