Computational predictions of glass-forming ability and crystallization tendency of drug molecules

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Search: WFRF:(Alzghoul Ahmad) > Computational predi...

1 of 17
Previous record
Next record
To hitlist

Details
MARC

Alhalaweh, AmjadUppsala universitet,Institutionen för farmaci (author)

Computational predictions of glass-forming ability and crystallization tendency of drug molecules

Article/chapterEnglish2014

Publisher, publication year, extent ...

2014-07-30
American Chemical Society (ACS),2014
printrdacarrier

Numbers

LIBRIS-ID:oai:DiVA.org:uu-232174
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-232174URI
https://doi.org/10.1021/mp500303aDOI

Supplementary language notes

Language:English
Summary in:English

Part of subdatabase

SwePubSwePub

Classification

Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

Notes

Amorphization is an attractive formulation technique for drugs suffering from poor aqueous solubility as a result of their high lattice energy. Computational models that can predict the material properties associated with amorphization, such as glass-forming ability (GFA) and crystallization behavior in the dry state, would be a time-saving, cost-effective, and material-sparing approach compared to traditional experimental procedures. This article presents predictive models of these properties developed using support vector machine (SVM) algorithm. The GFA and crystallization tendency were investigated by melt-quenching 131 drug molecules in situ using differential scanning calorimetry. The SVM algorithm was used to develop computational models based on calculated molecular descriptors. The analyses confirmed the previously suggested cutoff molecular weight (MW) of 300 for glass-formers, and also clarified the extent to which MW can be used to predict the GFA of compounds with MW < 300. The topological equivalent of Grav3_3D, which is related to molecular size and shape, was a better descriptor than MW for GFA; it was able to accurately predict 86% of the data set regardless of MW. The potential for crystallization was predicted using molecular descriptors reflecting Hückel pi atomic charges and the number of hydrogen bond acceptors. The models developed could be used in the early drug development stage to indicate whether amorphization would be a suitable formulation strategy for improving the dissolution and/or apparent solubility of poorly soluble compounds.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

Alzghoul, AhmadUppsala universitet,Datalogi,Uppsala Database Laboratory(Swepub:uu)ahmal479 (author)
Kaialy, Waseem (author)
Mahlin, DennyUppsala universitet,Institutionen för farmaci(Swepub:uu)dma10394 (author)
Bergström, Christel A. S.Uppsala universitet,Institutionen för farmaci(Swepub:uu)cjo29958 (author)
Uppsala universitetInstitutionen för farmaci (creator_code:org_t)

Related titles

In:Molecular Pharmaceutics: American Chemical Society (ACS)11:9, s. 3123-31321543-83841543-8392

Internet link

Find in a library

Molecular Pharmaceutics (Search for host publication in LIBRIS)

To the university's database

1 of 17
Previous record
Next record
To hitlist

Find more in SwePub

By the author/editor: Alhalaweh, Amjad; Alzghoul, Ahmad; Kaialy, Waseem; Mahlin, Denny; Bergström, Chris ...

About the subject

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Basic Medicine; and Pharmaceutical S ...

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Science ...

Articles in the publication: Molecular Pharma ...

By the university: Uppsala University

Search outside SwePub

Extend your search to:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se