In our efforts to increase the survival of drug candidates, we undertook a detailed study of the chemical space for CNS molecules. Ultimately, we were interested in optimizing the number of design cycles and in vivo toxicology testing needed to advance candidates from idea to proof of concept clinical studies. We focused on understanding the relationships between physicochemical properties, in vitro absorption, distribution, metabolism, and excretion (ADME) and safety attributes, and in vitro binding efficiencies for over 200 marketed CNS drugs and Pfizer CNS candidates. This analysis together with medicinal chemistry knowledge was used to create and validate a prospective design tool which used an overall desirability score for drug-likeness. The novel CNS multi-parameter optimization desirability (CNS MPO Desirability) algorithm, based on six physicochemical parameters, showed that 74% of marketed CNS drugs displayed a high desirability score (>4, using a scale of 0-6). In addition, a relationship between an increasing desirability score and alignment of key in vitro ADME and safety attributes was seen in the marketed CNS drug set, the Pfizer candidate set, and a Pfizer proprietary diversity set. The new CNS MPO Desirability score also offers advantages over hard cutoffs or utilization of single parameters, expanding medicinal chemistry design space. The CNS MPO Desirability score is thus an algorithm in the medicinal chemistry toolbox that may be used prospectively at the design stage to accelerate the identification of compounds with increased probability of success. Furthermore, application of this tool to new clinical drug candidates has challenged the long-held notion that CNS molecules need to be highly lipophilic with low polar surface, moving the CNS design field forward in a new direction.

Design of candidates for the central nervous system:  Exploiting a more polar property space

241st National Meeting (2011)

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