Department of Pharmacy, Uppsala University, Uppsala Biomedical Centre, P.O. Box 580, SE-751 23 Uppsala, Sweden.
Department of Information Technology, Uppsala University, Lägerhyddsv. 2, hus 1, Box 337, SE- 751 05 Uppsala, Sweden.
Int J Pharm. 2015 Nov 10;495(1):312-317. doi: 10.1016/j.ijpharm.2015.08.101. Epub 2015 Sep 1.
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 (Tg) 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 12h at temperatures 20°C above or below the Tg. 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°C below the Tg. Fourteen of the Class II compounds crystallized when stored above the Tg 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 Tg. The use of a large dataset revealed that molecular features related to aromaticity and π-π interactions reduce the inherent physical stability of amorphous drugs.
无定形材料本质上是不稳定的,并且在储存时往往会结晶。在这项研究中,我们研究了药物的物理稳定性和内在结晶倾向与其玻璃化转变温度(Tg)和热力学因素之间的关系。差示扫描量热法用于产生 52 种药物的无定形态[18 种化合物在加热时结晶(II 类),34 种化合物保持无定形态(III 类)],并在 Tg 以上或以下 20°C 的温度下对无定形材料进行 12 小时的原位储存。开发了基于支持向量机(SVM)算法的计算模型来预测结构-性能关系。所有药物在 Tg 以下 20°C 下储存时都保持其类别。当储存在 Tg 以上时,14 种 II 类化合物结晶,而 III 类化合物除一种外均保持无定形。这些结果仅与成玻璃能力有关,与例如热力学因素无关。实验数据用于计算建模,并开发了一种分类模型,该模型可正确预测 Tg 以上的物理稳定性。使用大型数据集表明,与芳香性和π-π相互作用相关的分子特征降低了无定形药物的固有物理稳定性。