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Molecular Drivers of Crystallization Kinetics for Drugs in Supersaturated Aqueous Solutions
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.ORCID iD: 0000-0002-8917-2612
2019 (English)In: Journal of Pharmaceutical Sciences, ISSN 0022-3549, E-ISSN 1520-6017, Vol. 108, no 1, p. 252-259Article in journal (Refereed) Published
Abstract [en]

In this study, we explore molecular properties of importance in solution-mediated crystallization occurring in supersaturated aqueous drug solutions. Furthermore, we contrast the identified molecular properties with those of importance for crystallization occurring in the solid state. A literature data set of 54 structurally diverse compounds, for which crystallization kinetics from supersaturated aqueous solutions and in melt-quenched solids were reported, was used to identify molecular drivers for crystallization kinetics observed in solution and contrast these to those observed for solids. The compounds were divided into fast, moderate, and slow crystallizers, and in silico classification was developed using a molecular K-nearest neighbor model. The topological equivalent of Grav3 (related to molecular size and shape) was identified as the most important molecular descriptor for solution crystallization kinetics; the larger this descriptor, the slower the crystallization. Two electrotopological descriptors (the atom-type E-state index for -Caa groups and the sum of absolute values of pi Fukui(+) indices on C) were found to separate the moderate and slow crystallizers in the solution. The larger these descriptors, the slower the crystallization. With these 3 descriptors, the computational model correctly sorted the crystallization tendencies from solutions with an overall classification accuracy of 77% (test set).

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC , 2019. Vol. 108, no 1, p. 252-259
Keywords [en]
crystallization, glass, in silico modeling, supersaturation, physicochemical properties, precipitation
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-377373DOI: 10.1016/j.xphs.2018.11.006ISI: 000456898100043PubMedID: 30423342OAI: oai:DiVA.org:uu-377373DiVA, id: diva2:1291391
Funder
Swedish Research Council, 621-2011-2445Swedish Research Council, 621-2014-3309Available from: 2019-02-25 Created: 2019-02-25 Last updated: 2019-02-25Bibliographically approved

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Alhalaweh, AmjadAlzghoul, AhmadBergström, Christel A. S.

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