Rytting Erik, Lentz Kimberley A, Chen Xue-Qing, Qian Feng, Vakatesh Srini
Discovery Pharmaceutics, Preclinical Candidate Optimization, Bristol-Myers Squibb Pharmaceutical Research Institute, Wallingford, CT 06492, USA.
AAPS J. 2005 Apr 26;7(1):E78-105. doi: 10.1208/aapsj070110.
Recently 2 QSPR-based in silico models were developed in our laboratories to predict the aqueous and non-aqueous solubility of drug-like organic compounds. For the intrinsic aqueous solubility model, a set of 321 structurally diverse drugs was collected from literature for the analysis. For the PEG 400 cosolvent model, experimental data for 122 drugs were obtained by a uniform experimental procedure at 4 volume fractions of PEG 400 in water, 0%, 25%, 50%, and 75%. The drugs used in both models represent a wide range of compounds, with log P values from -5 to 7.5, and molecular weights from 100 to >600 g/mol. Because of the standardized procedure used to collect the cosolvent data and the careful assessment of quality used in obtaining literature data, both data sets have potential value for the scientific community for use in building various models that require experimental solubility data.
最近，我们实验室开发了2个基于定量构效关系（QSPR）的计算机模拟模型，用于预测类药有机化合物的水相和非水相溶解度。对于固有水相溶解度模型，从文献中收集了一组321种结构多样的药物进行分析。对于聚乙二醇400（PEG 400）助溶剂模型，通过统一的实验程序，在水相中PEG 400的4个体积分数（0%、25%、50%和75%）下获得了122种药物的实验数据。两个模型中使用的药物代表了广泛的化合物，其log P值范围为-5至7.5，分子量范围为100至>600 g/mol。由于收集助溶剂数据采用了标准化程序，以及在获取文献数据时对质量进行了仔细评估，这两个数据集对于科学界在构建各种需要实验溶解度数据的模型方面都具有潜在价值。
