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

2.50
Hdl Handle:
http://hdl.handle.net/2436/562213
Title:
Computational predictions of glass-forming ability and crystallization tendency of drug molecules.
Authors:
Alhalaweh, Amjad; Alzghoul, Ahmad; Kaialy, Waseem; Mahlin, Denny; Bergström, Christel A S
Abstract:
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.
Citation:
Computational predictions of glass-forming ability and crystallization tendency of drug molecules. 2014, 11 (9):3123-32 Mol. Pharm.
Publisher:
ACS Publications
Journal:
Molecular pharmaceutics
Issue Date:
2-Sep-2014
URI:
http://hdl.handle.net/2436/562213
DOI:
10.1021/mp500303a
PubMed ID:
25014125
Type:
Article
Language:
en
ISSN:
1543-8392
Appears in Collections:
Pharmacy and Natural Products Research Group

Full metadata record

DC FieldValue Language
dc.contributor.authorAlhalaweh, Amjaden
dc.contributor.authorAlzghoul, Ahmaden
dc.contributor.authorKaialy, Waseemen
dc.contributor.authorMahlin, Dennyen
dc.contributor.authorBergström, Christel A Sen
dc.date.accessioned2015-08-03T11:04:38Zen
dc.date.available2015-08-03T11:04:38Zen
dc.date.issued2014-09-02en
dc.identifier.citationComputational predictions of glass-forming ability and crystallization tendency of drug molecules. 2014, 11 (9):3123-32 Mol. Pharm.en
dc.identifier.issn1543-8392en
dc.identifier.pmid25014125en
dc.identifier.doi10.1021/mp500303aen
dc.identifier.urihttp://hdl.handle.net/2436/562213en
dc.description.abstractAmorphization 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.en
dc.language.isoenen
dc.publisherACS Publicationsen
dc.rightsArchived with thanks to Molecular pharmaceuticsen
dc.subjectamorphousen
dc.subjectglass forming abilityen
dc.subjectcrystallization tendencyen
dc.subjectsupport vector machineen
dc.subjectmolecular descriptorsen
dc.subject.meshChemistry, Pharmaceuticalen
dc.subject.meshComputer Simulationen
dc.subject.meshCrystallizationen
dc.subject.meshGlassen
dc.subject.meshHydrogen Bondingen
dc.subject.meshMolecular Weighten
dc.subject.meshPharmaceutical Preparationsen
dc.subject.meshSolubilityen
dc.subject.meshTechnology, Pharmaceuticalen
dc.titleComputational predictions of glass-forming ability and crystallization tendency of drug molecules.en
dc.typeArticleen
dc.identifier.journalMolecular pharmaceuticsen

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