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.
|Publication status||Published - 28 Aug 2015|
|Event||2015 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2015) - Gothenburg, Sweden|
Duration: 24 Aug 2015 → 28 Aug 2015
|Conference||2015 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2015)|
|Period||24/08/2015 → 28/08/2015|
Susto, G. A., & McLoone, S. (2015). Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach. Paper presented at 2015 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2015), Gothenburg, Sweden. http://case2015.org/program/