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 druglike attributes. Using a set of six physicochemical parameters ((a) lipophilicity, calculated partition coefficient (ClogP); (b) calculated distribution coefficient at pH=7.4 (ClogD); (c) molecular weight (MW); (d) topological polar surface area (TPSA); (e) number of hydrogen bond donors (HBD); (f) most basic center (pK a )), a druglikeness central nervous system multiparameter 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 of Pfizer proprietary compounds (N = 11 303). The novel CNS MPO algorithm showed that 74% of marketed CNS drugs displayed a high CNS MPO score (MPO desirability score g 4, using a scale of 0-6), in comparison to 60% of the Pfizer CNS candidates. This analysis suggests that this algorithm could potentially be used to identify compounds with a higher probability of successfully testing hypotheses in the clinic. In addition, a relationship between an increasing CNS MPO score and alignment of key in vitro attributes of drug discovery (favorable permeability, P-glycoprotein (P-gp) efflux, metabolic stability, and safety) was seen in the marketed CNS drug set, the Pfizer candidate set, and the Pfizer proprietary diversity set. The CNS MPO scoring function offers advantages over hard cutoffs or utilization of single parameters to optimize structureactivity relationships (SAR) by expanding medicinal chemistry design space through a holistic assessment approach. Based on six physicochemical properties commonly used by medicinal chemists, the CNS MPO function may be used prospectively at the design stage to accelerate the identification of compounds with increased probability of success.

As part of our effort to increase survival of drug candidates and to move our medicinal chemistry design to higher probability space for success in the Neuroscience therapeutic area, we embarked on a detailed study of the property space for a collection of central nervous system (CNS) molecules. We carried out a thorough analysis of properties for 119 marketed CNS drugs and a set of 108 Pfizer CNS candidates. In particular, we focused on understanding the relationships between physicochemical properties, in vitro ADME (absorption, distribution, metabolism, and elimination) attributes, primary pharmacology binding efficiencies, and in vitro safety data for these two sets of compounds. This scholarship provides guidance for the design of CNS molecules in a property space with increased probability of success and may lead to the identification of druglike candidates with favorable safety profiles that can successfully test hypotheses in the clinic.

Significant progress has been made in prospectively designing molecules using the central nervous system multiparameter optimization (CNS MPO) desirability tool, as evidenced by the analysis reported herein of a second wave of drug candidates that originated after the development and implementation of this tool. This simple-to-use design algorithm has expanded design space for CNS candidates and has further demonstrated the advantages of utilizing a flexible, multiparameter approach in drug discovery rather than individual parameters and hard cutoffs of physicochemical properties. The CNS MPO tool has helped to increase the percentage of compounds nominated for clinical development that exhibit alignment of ADME attributes, cross the blood-brain barrier, and reside in lower-risk safety space (low ClogP and high TPSA). The use of this tool has played a role in reducing the number of compounds submitted to exploratory toxicity studies and increasing the survival of our drug candidates through regulatory toxicology into First in Human studies. Overall, the CNS MPO algorithm has helped to improve the prioritization of design ideas and the quality of the compounds nominated for clinical development.

To accelerate the discovery of novel small molecule central nervous system (CNS) positron emission tomography (PET) ligands, we aimed to define a property space that would facilitate ligand design and prioritization, thereby providing a higher probability of success for novel PET ligand development. Toward this end, we built a database consisting of 62 PET ligands that have successfully reached the clinic and 15 radioligands that failed in late-stage development as negative controls. A systematic analysis of these ligands identified a set of preferred parameters for physicochemical properties, brain permeability, and nonspecific binding (NSB). These preferred parameters have subsequently been applied to several programs and have led to the successful development of novel PET ligands with reduced resources and timelines. This strategy is illustrated here by the discovery of the novel phosphodiesterase 2A (PDE2A) PET ligand 4-(3-[(18)F]fluoroazetidin-1-yl)-7-methyl-5-{1-methyl-5-[4-(trifluoromethyl)phenyl]-1H-pyrazol-4-yl}imidazo[5,1-f][1,2,4]triazine, [(18)F]PF-05270430 (5).

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The recent increase in the number of X-ray crystal structures of G-protein coupled receptors (GPCRs) has been enabling for structure-based drug design (SBDD) efforts. These structures have revealed that GPCRs are highly dynamic macromolecules whose function is dependent on their intrinsic flexibility. Unfortunately, the use of static structures to understand ligand binding can potentially be misleading, especially in systems with an inherently high degree of conformational flexibility. Here, we show that docking a set of dopamine D3 receptor compounds into the existing eticlopride-bound dopamine D3 receptor (D3R) X-ray crystal structure resulted in poses that were not consistent with results obtained from site-directed mutagenesis experiments. We overcame the limitations of static docking by using large-scale high-throughput molecular dynamics (MD) simulations and Markov state models (MSMs) to determine an alternative pose consistent with the mutation data. The new pose maintains critical interactions observed in the D3R/eticlopride X-ray crystal structure and suggests that a cryptic pocket forms due to the shift of a highly conserved residue, F6.52. Our study highlights the importance of GPCR dynamics to understand ligand binding and provides new opportunities for drug discovery.

Positron Emission Tomography (PET), as a non-invasive translatable imaging technology, can be incorporated into various stages of the CNS drug discovery process to provide valuable information for key preclinical and clinical decision-making. Novel CNS PET ligand discovery efforts in the industry setting, however, are facing unique challenges associated with lead design and prioritization, and budget constraints. In this review, three strategies aiming toward improving the central nervous system (CNS) PET ligand discovery process are described: first, early determination of receptor density (Bmax) and bio-distribution to inform PET viability and resource allocation; second, rational design and design prioritization guided by CNS PET design parameters; finally, a cost-effective in vivo specific binding assessment using a liquid chromatography-mass spectrometry (LC-MS/MS) “cold tracer” method. Implementation of these strategies allowed a more focused and rational CNS PET ligand discovery effort to identify high quality PET ligands for neuroimaging.