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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03365naa a2200397 4500 | |
| 001 | oai:DiVA.org:uu-377373 | |
| 003 | SwePub | |
| 008 | 190225s2019 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3773732 URI |
| 024 | 7 | a https://doi.org/10.1016/j.xphs.2018.11.0062 DOI |
| 040 | a (SwePub)uu | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Alhalaweh, Amjadu Uppsala universitet,Institutionen för farmaci4 aut0 (Swepub:uu)amjal166 |
| 245 | 1 0 | a Molecular Drivers of Crystallization Kinetics for Drugs in Supersaturated Aqueous Solutions |
| 264 | 1 | b ELSEVIER SCIENCE INC,c 2019 |
| 338 | a electronic2 rdacarrier | |
| 520 | a 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). | |
| 650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Medicinska och farmaceutiska grundvetenskaperx Farmaceutiska vetenskaper0 (SwePub)301012 hsv//swe |
| 650 | 7 | a MEDICAL AND HEALTH SCIENCESx Basic Medicinex Pharmaceutical Sciences0 (SwePub)301012 hsv//eng |
| 653 | a crystallization | |
| 653 | a glass | |
| 653 | a in silico modeling | |
| 653 | a supersaturation | |
| 653 | a physicochemical properties | |
| 653 | a precipitation | |
| 700 | 1 | a Alzghoul, Ahmadu Uppsala universitet,Datalogi4 aut0 (Swepub:uu)ahmal479 |
| 700 | 1 | a Bergström, Christel A. S.,d 1973-u Uppsala universitet,Institutionen för farmaci4 aut0 (Swepub:uu)cjo29958 |
| 710 | 2 | a Uppsala universitetb Institutionen för farmaci4 org |
| 773 | 0 | t Journal of Pharmaceutical Sciencesd : ELSEVIER SCIENCE INCg 108:1, s. 252-259q 108:1<252-259x 0022-3549x 1520-6017 |
| 856 | 4 | u https://uu.diva-portal.org/smash/get/diva2:1291391/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print |
| 856 | 4 | u http://jpharmsci.org/article/S0022354918307081/pdf |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-377373 |
| 856 | 4 8 | u https://doi.org/10.1016/j.xphs.2018.11.006 |
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