Understanding matrix-assisted continuous co-crystallization using a data mining approach in Quality by Design (QbD)
| dc.contributor.author | Chabalenge, Billy | |
| dc.contributor.author | Korde, Sachin A. | |
| dc.contributor.author | Kelly, Adrian L. | |
| dc.contributor.author | Neagu, Daniel | |
| dc.contributor.author | Paradkar, Anant R | |
| dc.date.accessioned | 2020-07-27T11:48:27Z | |
| dc.date.accessioned | 2020-08-12T09:24:15Z | |
| dc.date.available | 2020-07-27T11:48:27Z | |
| dc.date.available | 2020-08-12T09:24:15Z | |
| dc.date.issued | 2020-07 | |
| dc.identifier.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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10454/17941 | |
| dc.description | Yes | en_US |
| dc.description.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. | en_US |
| dc.description.sponsorship | Commonwealth Scholarship Commission in the UK (ZMCS-2018-783) and Engineering and Physical Sciences Research Council (EPSRC EP/J003360/1 and EP/L027011/1) | en_US |
| dc.language.iso | en | en_US |
| dc.rights | ©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. | en_US |
| dc.subject | Co-crystallization process | en_US |
| dc.subject | Decision tree algorithms | en_US |
| dc.subject | Hot melt extrusion | en_US |
| dc.subject | Quality by Design (QbD) | en_US |
| dc.title | Understanding matrix-assisted continuous co-crystallization using a data mining approach in Quality by Design (QbD) | en_US |
| dc.status.refereed | Yes | en_US |
| dc.date.Accepted | 2020-05 | |
| dc.date.application | 2020-06-08 | |
| dc.type | Article | en_US |
| dc.type.version | Accepted manuscript | en_US |
| dc.identifier.doi | https://doi.org/10.1021/acs.cgd.0c00338 | |
| dc.date.updated | 2020-07-27T10:48:30Z | |
| refterms.dateFOA | 2020-08-12T09:25:21Z |
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