A decade in review: use of data analytics within the biopharmaceutical sector. (December 2021)

• Record Type: Journal Article
• Authors: Banner, Matthew; Alosert, Haneen; Spencer, Christopher; Cheeks, Matthew; Farid, Suzanne S; Thomas, Michael; Goldrick, Stephen
• Is Part Of: Current opinion in chemical engineering. Volume 34(2021)
• Publication Date: 2021-12
• Subjects: Chemical engineering -- Periodicals; Chemical engineering; Periodicals; 660.05
• DOI: 10.1016/j.coche.2021.100758
• Languages: English
• ISSNs: 2211-3398

Abstract

Highlights: Data analytics has increasing significantly in recent years in the biopharma sector. No clear trend observed between algorithm utilisation and data size. PLS was found to be most applied algorithm within the biopharmaceutical sector. Majority of the data analytics applications are focused on USP operations. Data analytics will play a key role as the sector transitions towards Industry 4.0. Abstract : There are large amounts of data generated within the biopharmaceutical sector. Traditionally, data analysis methods labelled as multivariate data analysis have been the standard statistical technique applied to interrogate these complex data sets. However, more recently there has been a surge in the utilisation of a broader set of machine learning algorithms to further exploit these data. In this article, the adoption of data analysis techniques within the biopharmaceutical sector is evaluated through a review of journal articles and patents published within the last ten years. The papers objectives are to identify the most dominant algorithms applied across different applications areas within the biopharmaceutical sector and to explore whether there is a trend between the size of the data set and the algorithm adopted.