The interplay among commonly used physicochemical properties in drug design was examined and utilized to create a prospective design tool focused on the alignment of key drug-like attributes.   Using a set of six physicochemical parameters (ClogP, ClogD, MW, TPSA, HBD, and pKa), a drug-likeness Central Nervous System Multi-Parameter Optimization (CNS MPO) algorithm was built and applied to a set of marketed CNS drugs (N = 119) and Pfizer CNS candidates (N = 108) as well as to a large diversity set.  A relationship between an increasing CNS MPO score and alignment of key in vitro attributes of drug discovery (permeability, P-gp efflux, metabolic stability, and safety) was seen in all three sets.   The CNS MPO scoring function offers advantages over hard cut-offs or utilization of single parameters to optimize structure-activity relationships (SAR) by expanding medicinal chemistry design space through a holistic assessment approach.

Moving beyond rules: The development of a central nervous system multi-parameter optimization (CNS MPO) approach to enable alignment of drug-like properties

240th National Meeting (2010)

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