, Volume 21, Issue 2, pp 237244
First online:
A Quantitative StructureProperty Relationship for Predicting Drug Solubility in PEG 400/Water Cosolvent Systems
 Erik RyttingAffiliated withDiscovery Pharmaceutics, Preclinical Candidate Optimization, BristolMyers Squibb Pharmaceutical Research Institute
 , Kimberley A. LentzAffiliated withDiscovery Pharmaceutics, Preclinical Candidate Optimization, BristolMyers Squibb Pharmaceutical Research Institute
 , XueQing ChenAffiliated withDiscovery Pharmaceutics, Preclinical Candidate Optimization, BristolMyers Squibb Pharmaceutical Research Institute
 , Feng QianAffiliated withDiscovery Pharmaceutics, Preclinical Candidate Optimization, BristolMyers Squibb Pharmaceutical Research Institute
 , Srini VenkateshAffiliated withDiscovery Pharmaceutics, Preclinical Candidate Optimization, BristolMyers Squibb Pharmaceutical Research Institute
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Purpose. A quantitative structureproperty relationship (QSPR) was developed to predict drug solubility in binary mixtures of polyethylene glycol (PEG) 400 and water. The ability of the QSPR model to predict solubility was assessed and compared to the classic loglinear cosolvency model.
Methods. The solubility of 122 drugs, ranging in log P from 2.4 to 7.5, was determined in 0%, 25%, 50%, and 75% PEG (v/v in water) by the shakeflask method. Solubility data from 84 drugs were fit by linear regression using the following molecular descriptors: molecular weight, volume, radius of gyration, density, number of rotatable bonds, hydrogenbond donors, and hydrogenbond acceptors. The multiple linear regression model was optimized by a genetic algorithm guided selection method. The remaining 38 compounds were used to test the predictability of the model.
Results. QSPRbased models developed at each volume fraction with the training set compounds showed a reasonable correlation coefficient (r) of ∼0.9 and a root mean square (rms) error of <0.5 log unit. The model predicted solubility values of ∼78% of the testing set compounds within 1 log unit. The loglinear model was as effective as the QSPRbased model in predicting the testing set solubilities; however, many drugs, as expected, showed significant deviation from loglinearity.
Conclusions. The QSPR model requires only the chemical structure of the drug and has utility for guiding vehicle identification for early preclinical in vivo studies, especially when compound availability is limited and experimental data such as aqueous solubility and melting point are unknown. When experimental data are available, the loglinear model was verified to be a useful predictive tool.
 Title
 A Quantitative StructureProperty Relationship for Predicting Drug Solubility in PEG 400/Water Cosolvent Systems
 Journal

Pharmaceutical Research
Volume 21, Issue 2 , pp 237244
 Cover Date
 200402
 DOI
 10.1023/B:PHAM.0000016237.06815.7a
 Print ISSN
 07248741
 Online ISSN
 1573904X
 Publisher
 Kluwer Academic PublishersPlenum Publishers
 Additional Links
 Topics
 Keywords

 cosolvent
 in silico
 PEG 400
 prediction
 QSPR model
 solubility
 Industry Sectors
 Authors

 Erik Rytting ^{(1)}
 Kimberley A. Lentz ^{(1)}
 XueQing Chen ^{(2)}
 Feng Qian ^{(2)}
 Srini Venkatesh ^{(1)}
 Author Affiliations

 1. Discovery Pharmaceutics, Preclinical Candidate Optimization, BristolMyers Squibb Pharmaceutical Research Institute, Wallingford, Connecticut, 06492
 2. Discovery Pharmaceutics, Preclinical Candidate Optimization, BristolMyers Squibb Pharmaceutical Research Institute, Lawrenceville, New Jersey, 08543