Journal of Chemical Information and Modeling 2021, 61, 3, 1412-1426 (Pharmaceutical Modeling)
期刊：Journal of Chemical Information and Modeling
作者：ichard S. Hong, Alessandra Mattei, Ahmad Y. Sheikh,* Rajni Miglani Bhardwaj, Michael A. Bellucci, Keith F. McDaniel, M. Olivia Pierce, Guangxu Sun, Sizhu Li, Lingle Wang, Sayan Mondal, Jianguo Ji, and Thomas B. Borchardt
時間：2021-03-04

Drug design with patient centricity for ease of administration and pill burden requires robust understanding of the impact of chemical modifications on relevant physicochemical properties early in lead optimization. To this end, we have developed a physics-based ensemble approach to predict aqueous thermodynamic crystalline solubility, with a 2D chemical structure as the input. Predictions for the bromodomain and extraterminal domain (BET) inhibitor series show very close match (0.5 log unit) with measured thermodynamic solubility for cases with low crystal anisotropy and good match (1 log unit) for high anisotropy structures. The importance of thermodynamic solubility is clearly demonstrated by up to a 4 log unit drop in solubility compared to kinetic (amorphous) solubility in some cases and implications thereof, for instance on human dose. We have also demonstrated that incorporating predicted crystal structures in thermodynamic solubility prediction is necessary to differentiate (up to 4 log unit) between solubility of molecules within the series. Finally, our physics-based ensemble approach provides valuable structural insights into the origins of 3-D conformational landscapes, crystal polymorphism, and anisotropy that can be leveraged for both drug design and development.