期刊
INTERNATIONAL JOURNAL OF PHARMACEUTICS
卷 511, 期 1, 页码 111-126出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ijpharm.2016.06.060
关键词
Solubility; High throughput technologies; Analytical chemistry; Computational ADME; In silico modeling; Computer aided drug design; ROC curve analysis
资金
- National Institutes of Health National Institute on Drug Abuse [R01DA023223]
This research describes a rapid solubility classification approach that could be used in the discovery and development of new molecular entities. Compounds (N = 635) were divided into two groups based on information available in the literature: high solubility (BDDCS/BCS 1/3) and low solubility (BDDCS/BCS 2/4). We established decision rules for determining solubility classes using measured log solubility in molar units (MLogS(M)) or measured solubility (MSol) in mg/ml units. ROC curve analysis was applied to determine statistically significant threshold values of MSol and MLogS(M). Results indicated that NMEs with MLogS(M) > -3.05 or MSol > 0.30 mg/mL will have >= 85% probability of being highly soluble and new molecular entities with MLogS(M) < -3.05 or MSol < 0.30 mg/mL will have >85% probability of being poorly soluble. When comparing solubility classification using the threshold values of MLogS(M) or MSol with BDDCS, we were able to correctly classify 85% of compounds. We also evaluated solubility classification of an independent set of 108 orally administered drugs using MSol (0.3 mg/mL) and our method correctly classified 81% and 95% of compounds into high and low solubility classes, respectively. The high/low solubility classification using MLogS(M) or MSol is novel and independent of traditionally used dose number criteria. (C) 2016 Elsevier B.V. All rights reserved.
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