Discovery of N-(1,4-Benzoxazin-3-one) urea analogs as Mode-Selective TRPV1 antagonists.

A series of 1,4-benzoxazin-3-one analogs were investigated to discover mode-selective TRPV1 antagonists, since such antagonists are predicted to minimize target-based adverse effects. Using the high-affinity antagonist 2 as the lead structure, the structure activity relationship was studied by modifying the A-region through incorporation of a polar side chain on the benzoxazine and then by changing the C-region with a variety of substituted pyridine, pyrazole and thiazole moieties. The t-butyl pyrazole and thiazole C-region analogs provided high potency as well as mode-selectivity. Among them, antagonist 36 displayed potent and capsaicin-selective antagonism with IC50 = 2.31 nM for blocking capsaicin activation and only 47.5 % inhibition at 3 µM concentration toward proton activation, indicating that more than a 1000-fold higher concentration of 36 was required to inhibit proton activation than was required to inhibit capsaicin activation. The molecular modeling study of 36 with our homology model indicated that two π-π interactions with the Tyr511 and Phe591 residues by the A- and C-region and hydrogen bonding with the Thr550 residue by the B-region were critical for maintaining balanced and stable binding. Systemic optimization of antagonist 2, which has high-affinity but full antagonism for activators of all modes, led to the mode-selective antagonist 36 which represents a promising step in the development of clinical TRPV1 antagonists minimizing side effects such as hyperthermia and impaired heat sensation.

Discovery of N-(1-(2-hydroxyethyl)quinolin-2-one)-N'-(1-phenyl-1H-pyrazol-5-yl)methyl) urea as Mode-Selective TRPV1 antagonist.

To discover mode-selective TRPV1 antagonists as thermoneutral drug candidates, the previous potent antagonist benzopyridone 2 was optimized based on the pharmacophore A- and C-regions. The structure activity relationship was investigated systematically by modifying the A-region by incorporating a polar side chain on the pyridone and then by changing the C-region with a variety of substituted pyridine and pyrazole moieties. The 3-t-butyl and 3-(1-methylcyclopropyl) pyrazole C-region analogs provided high potency as well as mode-selectivity. Among them, 51 and 54 displayed potent and capsaicin-selective antagonism with IC50 = 2.85 and 3.27 nM to capsaicin activation and 28.5 and 31.5 % inhibition at 3 µM concentration toward proton activation, respectively. The molecular modeling study of 51 with our homology model indicated that the hydroxyethyl side chain in the A-region interacted with Arg557 and Glu570, the urea B-region engaged in hydrogen bonding with Tyr511 and Thr550, respectively, and the pyrazole C-region made two hydrophobic interactions with the receptor. Optimization of antagonist 2, which has full antagonism for activators of all modes, lead to mode-selective antagonists 51 and 54. These observations will provide insight into the future development of clinical TRPV1 antagonists without target-based side effects.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

Benzo[a]pyrene represses synaptic vesicle exocytosis by inhibiting P/Q-type calcium channels in hippocampal neurons.

Humans are exposed to the common carcinogen benzo[a]pyrene (BaP) by ingesting contaminated foods and water or inhaling polluted air. Given the enriched lipids and reduced antioxidative properties in the brain and the accumulation of BaP in the brain due to its high lipophilicity, the brain is susceptible to BaP-induced toxicity. Exposure to BaP leads to impairments in learning and memory, increased anxiety behavior, and neuronal death. It induces protein dysfunctions in neuronal compartments that play essential roles in neuronal activity or physiology. However, the neurotoxicity of BaP on presynaptic terminals, which is crucial to neurotransmission by releasing synaptic vesicles that contain neurotransmitters, has not yet been investigated. In the present study, we investigated the toxicity of BaP at presynaptic terminals in living hippocampal neurons. These neurons were sourced from transgenic mice pups (postnatal 1-day, a total of 12 pups, equal numbers for each sex) that endogenously express synaptic vesicle-fused pHluorin, which is a green fluorescent protein that enables monitoring of synaptic vesicle dynamics. We observed that BaP suppressed synaptic vesicle exocytosis by inhibiting presynaptic Ca2+ entry via P/Q-type Ca2+ channels. Together with molecular docking simulation, we speculate that BaP and metabolites may bind to the P/Q Ca2+ channels. These results suggest the toxic mechanism of BaP exposure-induced abnormal behavior that provides a basis to evaluate the risk assessment of BaP-induced neurotoxicity.

Systematic analyses of the sequence conservation and ligand interaction patterns of purinergic P1 and P2Y receptors provide a structural basis for receptor selectivity.

Purinergic receptors are membrane proteins that regulate numerous cellular functions by catalyzing reactions involving purine nucleotides or nucleosides. Among the three receptor families, i.e., P1, P2X, and P2Y, the P1 and P2Y receptors share common structural features of class A GPCR. Comprehensive sequence and structural analysis revealed that the P1 and P2Y receptors belong to two distinct groups. They exhibit different ligand-binding site features that can distinguish between specific activators. These specific amino acid residues in the binding cavity may be involved in the selectivity and unique pharmacological behavior of each subtype. In this study, we conducted a structure-based analysis of purinergic P1 and P2Y receptors to identify their evolutionary signature and obtain structural insights into ligand recognition and selectivity. The structural features of the P1 and P2Y receptor classes were compared based on sequence conservation and ligand interaction patterns. Orthologous protein sequences were collected for the P1 and P2Y receptors, and sequence conservation was calculated based on Shannon entropy to identify highly conserved residues. To analyze the ligand interaction patterns, we performed docking studies on the P1 and P2Y receptors using known ligand information extracted from the ChEMBL database. We analyzed how the conserved residues are related to ligand-binding sites and how the key interacting residues differ between P1 and P2Y receptors, or between agonists and antagonists. We extracted new similarities and differences between the receptor subtypes, and the results can be used for designing new ligands by predicting hotspot residues that are important for functional selectivity.