| 1. |
- Alhalaweh, Amjad, et al.
(författare)
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Computational predictions of glass-forming ability and crystallization tendency of drug molecules
- 2014
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Ingår i: Molecular Pharmaceutics. - : American Chemical Society (ACS). - 1543-8384 .- 1543-8392. ; 11:9, s. 3123-3132
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Tidskriftsartikel (refereegranskat)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.
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| 2. |
- Alhalaweh, Amjad, et al.
(författare)
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Physical stability of drugs after storage above and below the glass transition temperature : Relationship to glass-forming ability
- 2015
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Ingår i: International Journal of Pharmaceutics. - : Elsevier BV. - 0378-5173 .- 1873-3476. ; 495:1, s. 312-317
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Tidskriftsartikel (refereegranskat)abstract
- Amorphous materials are inherently unstable and tend to crystallize upon storage. In this study, we investigated the extent to which the physical stability and inherent crystallization tendency of drugs are related to their glass-forming ability (GFA), the glass transition temperature (T-g) and thermodynamic factors. Differential scanning calorimetry was used to produce the amorphous state of 52 drugs [ 18 compounds crystallized upon heating (Class II) and 34 remained in the amorphous state (Class III)] and to perform in situ storage for the amorphous material for 12 h at temperatures 20 degrees C above or below the T-g. A computational model based on the support vector machine (SVM) algorithm was developed to predict the structure-property relationships. All drugs maintained their Class when stored at 20 degrees C below the T-g. Fourteen of the Class II compounds crystallized when stored above the T-g whereas all except one of the Class III compounds remained amorphous. These results were only related to the glass-forming ability and no relationship to e. g. thermodynamic factors was found. The experimental data were used for computational modeling and a classification model was developed that correctly predicted the physical stability above the T-g. The use of a large dataset revealed that molecular features related to aromaticity and pi-pi interactions reduce the inherent physical stability of amorphous drugs.
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| 3. |
- Alzghoul, Ahmad, et al.
(författare)
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Experimental and Computational Prediction of Glass Transition Temperature of Drugs
- 2014
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Ingår i: JOURNAL OF CHEMICAL INFORMATION AND MODELING. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:12, s. 3396-3403
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Tidskriftsartikel (refereegranskat)abstract
- Glass transition temperature (T-g) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between T-g and melting temperature (T-m) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of T-g were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on T-m predicted T-g with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict T-g of drug-like molecules with high accuracy were developed. If T-m is available, a simple linear regression can be used to predict T-g. However, the results also suggest that support vector regression and calculated molecular descriptors can predict T-g with equal accuracy, already before compound synthesis.
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