OBJECTIVES: An increasing trend towards low solubility is a major issue for drug development as formulation of low solubility compounds can be problematic. This paper presents a model which de-convolutes the solubility of pharmaceutical compounds into solvation and packing properties with the intention to understand the solubility limiting features. METHODS: The Cambridge Crystallographic Database was the source of structural information. Lattice energies were calculated via force-field based approaches using Materials Studio. The solvation energies were calculated applying quantum chemistry models using Cosmotherm software. KEY FINDINGS: The solubilities of 54 drug-like compounds were mapped onto a solvation energy/crystal packing grid. Four quadrants were identified were different balances of solvation and packing were defining the solubility. A version of the model was developed which allows for the calculation of the two features even in absence of crystal structure. CONCLUSION: Although there are significant number of in-silico models, it has been proven very difficult to predict aqueous solubility accurately. Therefore, we have taken a different approach where the solubility is not predicted directly but is de-convoluted into two constituent features.
OBJECTIVES: An increasing trend towards low solubility is a major issue for drug development as formulation of low solubility compounds can be problematic. This paper presents a model which de-convolutes the solubility of pharmaceutical compounds into solvation and packing properties with the intention to understand the solubility limiting features. METHODS: The Cambridge Crystallographic Database was the source of structural information. Lattice energies were calculated via force-field based approaches using Materials Studio. The solvation energies were calculated applying quantum chemistry models using Cosmotherm software. KEY FINDINGS: The solubilities of 54 drug-like compounds were mapped onto a solvation energy/crystal packing grid. Four quadrants were identified were different balances of solvation and packing were defining the solubility. A version of the model was developed which allows for the calculation of the two features even in absence of crystal structure. CONCLUSION: Although there are significant number of in-silico models, it has been proven very difficult to predict aqueous solubility accurately. Therefore, we have taken a different approach where the solubility is not predicted directly but is de-convoluted into two constituent features.
Authors: Benjoe Rey B Visayas; Shyam K Pahari; Tugba Ceren Gokoglan; James A Golen; Ertan Agar; Patrick J Cappillino; Maricris L Mayes Journal: Chem Sci Date: 2021-11-26 Impact factor: 9.825
Authors: Chang Wang; Ian Rosbottom; Thomas D Turner; Sydney Laing; Andrew G P Maloney; Ahmad Y Sheikh; Robert Docherty; Qiuxiang Yin; Kevin J Roberts Journal: Pharm Res Date: 2021-05-19 Impact factor: 4.200