脱氢乙酸腙作为有效的酶抑制剂:设计、合成和计算研究

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2022-11-01 DOI:10.1016/j.comtox.2022.100239
Raman Lakhia , Neera Raghav , Rashmi Pundeer
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引用次数: 1

摘要

本研究为脱氢乙酸的腙衍生物的计算和合成研究提供了参考。采用不同的吸电子和放电子取代基设计了脱氢乙酸的腙衍生物。对腙和母体化合物(脱氢乙酸)进行了计算研究,以评估它们的药理学性质。通过应用Lipinski规则和ADMET预测来评估化合物。在所有研究的衍生物中,4-羟基-6-甲基-3-(1-(2-(2-硝基苯基)肼基)乙基)- 2h -吡喃-2- 1是最有效的衍生物,通过对接研究进一步对BSA、胰蛋白酶、淀粉酶、脂肪酶和组织蛋白酶(B和H)进行了评价。通过对衍生物的合成以及对合成的腙和母体化合物进行酶抑制研究,验证了计算结果。
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Dehydroacetic acid hydrazones as potent enzyme inhibitors: design, synthesis and computational studies

The present study offers the work on the hydrazone derivatives of dehydroacetic acid to be considered for computational and synthetic studies. The hydrazone derivatives of dehydroacetic acid were designed with different electron-withdrawing and electron-releasing substituents. The hydrazones and the parent compound (dehydroacetic acid) were subjected to computational studies to evaluate their pharmacological properties. The compounds were assessed by applying Lipinski’s rule followed by ADMET predictions. Among all the derivatives under studies, 4-hydroxy-6-methyl-3-(1-(2-(2-nitrophenyl) hydrazineylidene) ethyl)-2H-pyran-2-one was found to be the most effective derivative which was further evaluated against BSA, trypsin, amylase, lipase and cathepsins (B and H) by using docking studies. The computational results were also verified experimentally by synthesizing the derivative as well as performing enzyme inhibition studies on the synthesized hydrazone and the parent compound.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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