Data analytics on raw material properties to accelerate pharmaceutical drug development.
Manufacturability of active pharmaceutical ingredients (APIs) is often evaluated by an empirical approach during development due to limited material availability. This brings challenges in designing flexible yet robust manufacturing processes under highly accelerated timelines. Hence, good utilisation of a limited material dataset is key to accelerate the delivery of high quality final drug product into the market at minimum cost and maximum process capacity. In this study, we present a data-driven method to investigate a raw materials database where the integration of multivariate analysis and machine learning modelling aids the selection of new incoming materials based on their manufacturability. The procedure was applied to an industrial representative database of thirty-four APIs and seven excipients where eight measurements relevant to flow properties for each of those forty-one materials were collected. The models identified four clusters of materials with different flow properties. These models can serve as a risk assessment tool for new API in early product development phases based on the nearest surrogate material which behave similarly, as well as to identify targeted and material sparring experiments to address key risks during secondary process selection.
['Benedetti|Antonio|A|', 'Khoo|Jiyi|J|', 'Sharma|Sandeep|S|', 'Facco|Pierantonio|P|', 'Barolo|Massimiliano|M|', 'Zomer|Simeone|S|']
D016208:Databases, Factual D000076722:Drug Development D005079:Excipients D008962:Models, Theoretical D010316:Particle Size D004364:Pharmaceutical Preparations D012212:Rheology D060388:Support Vector Machine D013499:Surface Properties
Data analytics Raw materials database Machine learning Material clustering Multivariate data analysis Pharmaceutical drug product development