D4 receptor antagonists(University of Bari)

The neurotransmitter dopamine signals through G protein‐coupled receptors that regulate a variety of neurophysiological functions, including movement, emotional regulation, motivation, and cognition. Dopamine D4 receptors (D4Rs) are enriched in the prefrontal cortical regions critical to attention, cognition, decision‐making, and executive function. The physiological relevance of D4R signaling in the brain is not well understood and but a variety of evidence indicates its importance in neuropsychiatric disorders related to cognition and behavioral control, including Alzheimer’s disease, ADHD, and substance use disorders (SUDs). Preclinical studies indicate that D4R‐selective ligands can improve outcomes in animal models of these disorders, but the field is limited by the availability of highly selective ligands with suitable pharmacological characterization. Herein we report the development and structure‐activity relationships of a novel class of highly D4R‐selective ligands, primarily full antagonists, based on a phenylpiperazine (PP) scaffold and benzothiazole secondary pharmacophore. Following comprehensive in vitro binding and functional analyses, selected ligands were evaluated for pharmacokinetics in rat and human liver microsomes, followed by preliminary in vivo behavioral analysis. Overall, we identified several novel compounds with high binding affinity and D4R selectivity (Ki ≤ 100 nM and >100‐fold vs. other D2‐like receptors). Lead compound CAB‐01‐019, one of the most selective and high‐affinity D4R antagonists in this set, has favorable in vivo pharmacokinetics in rats, and inhibited cocaine self‐administration in rats. This new class of compounds represents a new set of tools to enhance medications development at D4R and to better understand the role of D4R signaling in complex behaviors.

We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on- and off-target binding. The approach translates the nature-inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure-activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype-selective and multitarget-modulating dopamine D4 antagonists, as well as ligands selective for the sigma-1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand-based computer-aided molecular design method may guide target-focused combinatorial chemistry.