The challenge of designing aqueous soluble organic molecules in drug discovery is acutely felt in many projects. The approach of adding solubilizing groups appears attractive but often leads to its own problems, such as increasing hERG binding and reducing permeability. Matched Molecular Pair Analysis (MMPA) provides an attractive mechanism to compare molecules and gauge the true effect of functional groups to improve solubility. MedChemica worked in collaboration with three pharmaceutical companies and analyzed solubility data using MMPA. Standarisation of chemical information and statistical methods allow multi-parameter optimization knowledge to be gained. For example we can find which solubilizing basic side chains with improve solubility further and also reduce hERG binding and metabolism. A recent collaboration with Cambridge Crystallographic Data Centre (CCDC) to analyze the data further and also incorporate pKa measurement to tackle the ‘age-old’ solubility prediction problem. The talk will discuss the solubility knowledge sharing process, MMPA “Big Data” analysis and recently published results.

Matched molecular pair analysis and collaboration: Finding rules to the age-old problem

253rd National Meeting (2017)

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