Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets
Issue Date
2017-11-01Author
Nariya, Maulik K.
Kim, Jae Hyun
Xiong, Jian
Kleindl, Peter Alan
Hewarathna, Asha
Fisher, Adam C.
Joshi, Sangeeta B.
Schöneich, Christian
Forrest, M. Laird
Middaugh, C. Russell
Volkin, David B.
Deeds, Eric J.
Publisher
Elsevier
Type
Article
Article Version
Scholarly/refereed, author accepted manuscript
Rights
© 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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There is growing interest in generating physicochemical and biological analytical data sets to compare complex mixture drugs, for example, products from different manufacturers. In this work, we compare various crofelemer samples prepared from a single lot by filtration with varying molecular weight cutoffs combined with incubation for different times at different temperatures. The 2 preceding articles describe experimental data sets generated from analytical characterization of fractionated and degraded crofelemer samples. In this work, we use data mining techniques such as principal component analysis and mutual information scores to help visualize the data and determine discriminatory regions within these large data sets. The mutual information score identifies chemical signatures that differentiate crofelemer samples. These signatures, in many cases, would likely be missed by traditional data analysis tools. We also found that supervised learning classifiers robustly discriminate samples with around 99% classification accuracy, indicating that mathematical models of these physicochemical data sets are capable of identifying even subtle differences in crofelemer samples. Data mining and machine learning techniques can thus identify fingerprint-type attributes of complex mixture drugs that may be used for comparative characterization of products.
Description
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Citation
Nariya, M. K., Kim, J. H., Xiong, J., Kleindl, P. A., Hewarathna, A., Fisher, A. C., … Deeds, E. J. (2017). Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets. Journal of pharmaceutical sciences, 106(11), 3270–3279. doi:10.1016/j.xphs.2017.07.013
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