Identification of Novel Quinolone and Quinazoline Alkaloids as Phosphodiesterase 10A Inhibitors for Parkinson’s Disease through a Computational Approach

ACS Omega, Journal Year: 2024, Volume and Issue: 9(14), P. 16262 - 16278

Published: March 26, 2024

Phosphodiesterases (PDEs) are vital in signal transduction, specifically by hydrolyzing cAMP and cGMP. Within the PDE family, PDE10A is notable for its prominence striatum regulatory function over neurotransmitters medium-spiny neurons. Given dopamine deficiency Parkinson's disease (PD) that affects striatal pathways, inhibitors could offer therapeutic benefits modulating D1 D2 receptor signaling. This study was motivated successful history of quinazoline/quinazoline scaffolds inhibition PDE10A. involved detailed silico evaluations through docking followed pharmacological, pharmacophoric, pharmacokinetic analyses, prioritizing central nervous system (CNS)-active drug criteria. Seven cyclic peptides, those featuring moiety at both termini, exhibited notably enhanced scores compared to remaining alkaloids within screened library. We identified 7 quinolines 1 quinazoline including Lepadin G, Aspernigerin, CJ-13536, Aurachin A, 2-Undecyl-4(1H)-quinolone, Huajiaosimuline 3-Prenyl-4-prenyloxyquinolin-2-one, Isaindigotone standard CNS active The dominant quinoline ring our related were evaluations; therefore, pharmacophoric features these highlighted. top met all CNS-active properties; while nonmutagenic without PAINS alerts, many indicated potential hepatotoxicity. Among compounds, particularly significant due alignment with lead-likeness Aspernigerin demonstrated affinity numerous receptors, which signifies alter dopaminergic neurotransmission directly PD. Interestingly, majority had biological targets primarily associated G protein-coupled critical PD pathophysiology. They exhibit superior excretion parameters toxicity end-points standard. Notably, selected stability binding pocket according molecular dynamic simulation results. Our findings emphasize as inhibitors. Further experimental studies may be necessary confirm their actual potency inhibiting before exploring

Language: Английский

Artificial intelligence for drug repurposing against infectious diseases

Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(2), P. 100071 - 100071

Published: June 12, 2024

Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated repurposing. AI allows researchers analyze massive datasets, revealing hidden connections between existing drugs, disease targets, potential treatments. This approach boasts several advantages. First, repurposing drugs leverages established safety data reduces development time costs. Second, can broaden search for effective therapies by identifying unexpected new targets. Finally, help mitigate limitations predicting minimizing side effects, optimizing repurposing, navigating intellectual property hurdles. The article explores specific strategies like virtual screening, target identification, structure base design natural language processing. Real-world examples highlight AI-driven in discovering treatments

Language: Английский