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VUF 10350 | 1080622-97-4

中文名称
——
中文别名
——
英文名称
VUF 10350
英文别名
6-chloro-N,N-dimethyl-2-(4-methylpiperazin-1-yl)quinazolin-4-amine
VUF 10350化学式
CAS
1080622-97-4
化学式
C15H20ClN5
mdl
——
分子量
305.81
InChiKey
GLWWAUFFFXRHOL-UHFFFAOYSA-N
BEILSTEIN
——
EINECS
——
  • 物化性质
  • 计算性质
  • ADMET
  • 安全信息
  • SDS
  • 制备方法与用途
  • 上下游信息
  • 反应信息
  • 文献信息
  • 表征谱图
  • 同类化合物
  • 相关功能分类
  • 相关结构分类

计算性质

  • 辛醇/水分配系数(LogP):
    2.9
  • 重原子数:
    21
  • 可旋转键数:
    2
  • 环数:
    3.0
  • sp3杂化的碳原子比例:
    0.47
  • 拓扑面积:
    35.5
  • 氢给体数:
    0
  • 氢受体数:
    5

反应信息

  • 作为产物:
    描述:
    2,4,6-三氯喹唑啉乙醇乙酸乙酯 为溶剂, 生成 VUF 10350
    参考文献:
    名称:
    Design, Synthesis, and Structure–Activity Relationships of Highly Potent 5-HT3 Receptor Ligands
    摘要:
    The 5-HT3 receptor, a pentameric ligand-gated ion channel (pLGIC), is an important therapeutic target. During a recent fragment screen, 6-chloro-N-methyl-2-(4-methyl-1,4-diazepan-1-yl)quinazolin-4-amine (1) was identified as a 5-HT3R hit fragment. Here we describe the synthesis and structure activity relationships (SAR) of a series of (iso)quinoline and quinazoline compounds that were synthesized and screened for 5-HT3R affinity using a [H-3]granisetron displacement assay. These studies resulted in the discovery of several high affinity ligands of which compound 22 showed the highest affinity (pK(i) > 10) for the 5-HT3 receptor. The observed SAR is in agreement with established pharmacophore models for 5-HT3 ligands and is used for ligand-receptor binding mode prediction using homology modeling and in silico docking approaches.
    DOI:
    10.1021/jm300801u
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文献信息

  • Discovery of Quinazolines as Histamine H<sub>4</sub> Receptor Inverse Agonists Using a Scaffold Hopping Approach
    作者:Rogier A. Smits、Iwan J. P. de Esch、Obbe P. Zuiderveld、Joachim Broeker、Kamonchanok Sansuk、Elena Guaita、Gabriella Coruzzi、Maristella Adami、Eric Haaksma、Rob Leurs
    DOI:10.1021/jm800876b
    日期:2008.12.25
    From a series of small fragments that was designed to probe the histamine H-4 receptor (H4R), we previously described quinoxaline-containing fragments that were grown into high affinity H4R ligands in a process that was guided by pharmacophore modeling. With a scaffold hopping exercise and using the same in silico models, we now report the identification and optimization of a series of quinazoline-containing H4R compounds. This approach led to the discovery of 6-chloi-o-N-(furan-3-yl)methyl)2-(4-methylpiperzin-1-yl)quinazolin-4-amine (VUF10499, 54) and 6-chloro-2-(4-methylpiperazin-1-yl)-N-(thiophen-2-ylmethyl)quinazolin-4-amine (VUF10497, 55) as potent human H4R inverse agonists (pK(i) = 8.12 and 7.57, respectively). Interestingly, both compounds also possess considerable affinity for the human histamine Hi receptor (H1R) and therefore represent a novel class of dual action H1R/H4R ligands, a profile that potentially leads to added therapeutic benefit. Compounds from this novel series of quirlazolines are antagonists at the rat H4R and were found to possess anti-inflammatory properties in vivo in the rat.
  • Design, Synthesis, and Structure–Activity Relationships of Highly Potent 5-HT<sub>3</sub> Receptor Ligands
    作者:Mark H. P. Verheij、Andrew J. Thompson、Jacqueline E. van Muijlwijk-Koezen、Sarah C. R. Lummis、Rob Leurs、Iwan J. P. de Esch
    DOI:10.1021/jm300801u
    日期:2012.10.25
    The 5-HT3 receptor, a pentameric ligand-gated ion channel (pLGIC), is an important therapeutic target. During a recent fragment screen, 6-chloro-N-methyl-2-(4-methyl-1,4-diazepan-1-yl)quinazolin-4-amine (1) was identified as a 5-HT3R hit fragment. Here we describe the synthesis and structure activity relationships (SAR) of a series of (iso)quinoline and quinazoline compounds that were synthesized and screened for 5-HT3R affinity using a [H-3]granisetron displacement assay. These studies resulted in the discovery of several high affinity ligands of which compound 22 showed the highest affinity (pK(i) > 10) for the 5-HT3 receptor. The observed SAR is in agreement with established pharmacophore models for 5-HT3 ligands and is used for ligand-receptor binding mode prediction using homology modeling and in silico docking approaches.
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