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Understanding matrix-assisted continuous co-crystallization using a data mining approach in Quality by Design (QbD)
Chabalenge, Billy Korde, Sachin A. Kelly, Adrian L. Neagu, Daniel
Chabalenge, Billy
Korde, Sachin A.
Kelly, Adrian L.
Neagu, Daniel
Publication Date
2020-07
End of Embargo
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©2020 ACS. This document is the Accepted Manuscript version of a Published Work that appeared in final form in Crystal Growth & Design, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.cgd.0c00338.
Peer-Reviewed
Yes
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Accepted for publication
2020-05
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Abstract
The present study demonstrates the application of decision tree algorithms to the co-crystallization process. Fifty four (54) batches of carbamazepine-salicylic acid co-crystals embedded in poly(ethylene oxide) were manufactured via hot melt extrusion and characterized by powder X-ray diffraction, differnetial scanning calorimetry, and near-infrared spectroscopy. This dataset was then applied in WEKA, which is an open-sourced machine learning software to study the effect of processing temperature, screw speed, screw configuration, and poly(ethylene oxide) concentration on the percentage of co-crystal conversion. The decision trees obtained provided statistically meaningful and easy-to-interpret rules, demonstrating the potential to use the method to make rational decisions during the development of co-crystallization processes.
Version
Accepted manuscript
Citation
Chabalenge B, Korde S, Kelly AL et al (2020) Understanding matrix-assisted continuous co-crystallization using a data mining approach in Quality by Design (QbD). Crystal Growth & Design. 20(7): 4540-4549.
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Article