Tags

Type your tag names separated by a space and hit enter

Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets.
J Pharm Sci 2017; 106(11):3270-3279JP

Abstract

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.

Authors+Show Affiliations

Department of Physics and Astronomy, University of Kansas, Lawrence, Kansas 66045.Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66045.Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66045; Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66045.Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66045.Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66045.Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, U.S. Food and Drug Administration, Silver Spring, Maryland 20993.Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66045; Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66045.Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66045.Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66045.Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66045; Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66045.Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66045; Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66045.Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66045; Center for Computational Biology, University of Kansas, Lawrence, Kansas 66045; Santa Fe Institute, Santa Fe, New Mexico 87501. Electronic address: deeds@ku.edu.

Pub Type(s)

Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, P.H.S.

Language

eng

PubMed ID

28743607

Citation

Nariya, Maulik K., et al. "Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets." Journal of Pharmaceutical Sciences, vol. 106, no. 11, 2017, pp. 3270-3279.
Nariya MK, Kim JH, Xiong J, et al. Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets. J Pharm Sci. 2017;106(11):3270-3279.
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), pp. 3270-3279. doi:10.1016/j.xphs.2017.07.013.
Nariya MK, et al. Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets. J Pharm Sci. 2017;106(11):3270-3279. PubMed PMID: 28743607.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets. AU - Nariya,Maulik K, AU - Kim,Jae Hyun, AU - Xiong,Jian, AU - Kleindl,Peter A, AU - Hewarathna,Asha, AU - Fisher,Adam C, AU - Joshi,Sangeeta B, AU - Schöneich,Christian, AU - Forrest,M Laird, AU - Middaugh,C Russell, AU - Volkin,David B, AU - Deeds,Eric J, Y1 - 2017/07/22/ PY - 2017/03/28/received PY - 2017/07/12/revised PY - 2017/07/18/accepted PY - 2017/7/27/pubmed PY - 2018/6/1/medline PY - 2017/7/27/entrez KW - comparative characterization KW - crofelemer KW - data mining KW - supervised learning SP - 3270 EP - 3279 JF - Journal of pharmaceutical sciences JO - J Pharm Sci VL - 106 IS - 11 N2 - 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. SN - 1520-6017 UR - https://www.unboundmedicine.com/medline/citation/28743607/Comparative_Characterization_of_Crofelemer_Samples_Using_Data_Mining_and_Machine_Learning_Approaches_With_Analytical_Stability_Data_Sets L2 - https://linkinghub.elsevier.com/retrieve/pii/S0022-3549(17)30510-5 DB - PRIME DP - Unbound Medicine ER -