Path to another drug against COVID-19 The rapid development of vaccines has been crucial in battling the ongoing COVID-19 pandemic. However, access challenges remain, breakthrough infections occur, and emerging variants present increased risk. Developing antiviral therapeutics is therefore a high priority for the treatment of COVID-19. Some drug candidates in clinical trials act against the viral RNA-dependent RNA polymerase, but there are other viral enzymes that have been considered good targets for inhibition by drugs. Owen et al . report the discovery and characterization of a drug against the main protease involved in the cleavage of polyproteins involved in viral replication. The drug, PF-07321332, can be administered orally, has good selectivity and safety profiles, and protects against infection in a mouse model. In a phase 1 clinical trial, the drug reached concentrations expected to inhibit the virus based on in vitro studies. It also inhibited other coronaviruses, including severe acute respiratory syndrome coronavirus 1 and Middle East respiratory syndrome coronavirus, and could be in the armory against future viral threats. —VV

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.

We have recently proposed the hypothesis that inhibition of the cyclic nucleotide phosphodiesterase (PDE) 10A may represent a new pharmacological approach to the treatment of schizophrenia (Curr Opin Invest Drug 8: 54 -59, 2007 386 -396, 2006). Our current understanding of the physiological role of PDE10A and the therapeutic utility of PDE10A inhibitors derives in part from studies with papaverine, the only pharmacological tool for this target extensively profiled to date. However, this agent has significant limitations in this regard, namely, relatively poor potency and selectivity and a very short exposure half-life after systemic administration. In the present report, we describe the discovery of a new class of PDE10A inhibitors exemplified by TP-10 (2-{4-[-pyridin-4-yl-1-(2,2,2-trifluoro-ethyl)-1H-pyrazol-3-yl]-phenoxymethyl}-quinoline succinic acid), an agent with greatly improved potency, selectivity, and pharmaceutical properties. These new pharmacological tools enabled studies that provide further evidence that inhibition of PDE10A represents an important new target for the treatment of schizophrenia and related disorders of basal ganglia function.

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).

By utilizing structure-based drug design (SBDD) knowledge, a novel class of phosphodiesterase (PDE) 10A inhibitors was identified. The structure-based drug design efforts identified a unique "selectivity pocket" for PDE10A inhibitors, and interactions within this pocket allowed the design of highly selective and potent PDE10A inhibitors. Further optimization of brain penetration and drug-like properties led to the discovery of 2-[4-(1-methyl-4-pyridin-4-yl-1H-pyrazol-3-yl)-phenoxymethyl]-quinoline (PF-2545920). This PDE10A inhibitor is the first reported clinical entry for this mechanism in the treatment of schizophrenia.

Leucine rich repeat kinase 2 (LRRK2) has been genetically linked to Parkinson's disease (PD) by genome-wide association studies (GWAS). The most common LRRK2 mutation, G2019S, which is relatively rare in the total population, gives rise to increased kinase activity. As such, LRRK2 kinase inhibitors are potentially useful in the treatment of PD. We herein disclose the discovery and optimization of a novel series of potent LRRK2 inhibitors, focusing on improving kinome selectivity using a surrogate crystallography approach. This resulted in the identification of 14 (PF-06447475), a highly potent, brain penetrant and selective LRRK2 inhibitor which has been further profiled in in vivo safety and pharmacodynamic studies.