Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Gian Antonio Susto, Sean McLoone

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.
Original languageEnglish
Publication statusPublished - 28 Aug 2015
Event2015 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2015) - Gothenburg, Sweden
Duration: 24 Aug 201528 Aug 2015

Conference

Conference2015 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2015)
CountrySweden
CityGothenburg
Period24/08/201528/08/2015

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