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dc.contributor.authorAlhalaweh, Amjad
dc.contributor.authorAlzghoul, Ahmad
dc.contributor.authorKaialy, Waseem
dc.contributor.authorMahlin, Denny
dc.contributor.authorBergström, Christel A S
dc.date.accessioned2015-08-03T11:04:38Zen
dc.date.available2015-08-03T11:04:38Zen
dc.date.issued2014-09-02
dc.identifier.citationComputational predictions of glass-forming ability and crystallization tendency of drug molecules. 2014, 11 (9):3123-32 Mol. Pharm.
dc.identifier.issn1543-8392
dc.identifier.pmid25014125
dc.identifier.doi10.1021/mp500303a
dc.identifier.urihttp://hdl.handle.net/2436/562213
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.
dc.language.isoen
dc.publisherACS Publications
dc.subjectamorphous
dc.subjectglass forming ability
dc.subjectcrystallization tendency
dc.subjectsupport vector machine
dc.subjectmolecular descriptors
dc.subject.meshChemistry, Pharmaceutical
dc.subject.meshComputer Simulation
dc.subject.meshCrystallization
dc.subject.meshGlass
dc.subject.meshHydrogen Bonding
dc.subject.meshMolecular Weight
dc.subject.meshPharmaceutical Preparations
dc.subject.meshSolubility
dc.subject.meshTechnology, Pharmaceutical
dc.titleComputational predictions of glass-forming ability and crystallization tendency of drug molecules.
dc.typeJournal article
dc.identifier.journalMolecular pharmaceutics
html.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.


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