Discovery of Small Molecule Inhibitors Targeting CTNNB1 (β-catenin) for Endometrial cancer: Employing 3D QSAR, Drug-Likeness Assessment, ADMET Predictions, Molecular Docking and Simulation

广告 生物信息学 数量结构-活动关系 化学 对接(动物) 药理学 公共化学 小分子 药物发现 计算生物学 药品 立体化学 生物 生物化学 医学 护理部 基因
作者
Israr Fatima,Abdur Rehman,Peng Wang,Zhijie He,Mingzhi Liao
出处
期刊:Current Medicinal Chemistry [Bentham Science Publishers]
卷期号:31 被引量:1
标识
DOI:10.2174/0109298673307257240826111754
摘要

Background: Endometrial carcinoma (EC) is a type of cancer that originates in the lining of the uterus, known as the endometrium. It is associated with various treatment options such as surgery, radiation therapy, chemotherapy, and hormone therapy, each presenting unique challenges and limitations. Beta-catenin, a protein involved in the development and progression of several cancers, including EC, plays a crucial role. Abnormal beta-catenin signaling is often linked to the emergence of specific EC subtypes, affecting tumor growth and invasion. Objectives: The study's objective is to identify compounds targeting the beta-catenin protein for treating endometrial cancer (EC) using in silico drug design. Our approach includes molecular docking to evaluate binding affinities, ADME profiling for pharmacokinetic properties, toxicity assessments, and molecular dynamics simulations to assess compound stability and interactions. Methods: Approximately one thousand anti-cancer phytochemicals were sourced from PubChem and subjected to molecular docking simulations against the beta-catenin protein. The compounds were evaluated based on their binding affinities, with the top five selected for further analysis. These five molecules underwent toxicity and ADME profiling. The Prediction of Activity Spectra for Substances (PASS) tool was used to identify compounds targeting CTNNB1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed to establish quantitative structure-activity relationship (QSAR) models for the five CTNNB1 antagonist molecules. Results: The selected five compounds, namely Pazopanib, Binimetinib, Telatinib, 4-(2,3-Dihydrobenzo[ b][1,4]dioxin-6-yl)-3-((5-nitrothiazol-2-yl)thio)-1H-1,2,4-triazol-5(4H)-one, and Ribavirin, demonstrated efficacy against CTNN1. MD simulations of the docked complexes confirmed the stability of these drugs in binding to the target protein. All five molecules showed promising safety and effectiveness profiles according to their ADME and toxicity evaluations. Conclusion: Through a comprehensive screening process employing in silico drug design methods, this study successfully identified five potential human anticancer drug candidates targeting the beta-catenin protein. These findings offer a foundation for further experimental validation and development towards the treatment of EC.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
huaizhuang发布了新的文献求助10
刚刚
刚刚
今后应助左丘幼旋1采纳,获得10
1秒前
1秒前
ggdodo完成签到,获得积分10
1秒前
123发布了新的文献求助10
1秒前
万能图书馆应助鱼憨儿采纳,获得10
2秒前
夏雨发布了新的文献求助20
2秒前
科研通AI6.2应助霜降采纳,获得10
2秒前
无辜问玉完成签到,获得积分10
2秒前
刘亦菲暧昧对象完成签到 ,获得积分10
2秒前
2秒前
独特的太阳完成签到,获得积分10
4秒前
搜集达人应助霸天狂龙采纳,获得10
4秒前
胡英俊完成签到,获得积分10
4秒前
4秒前
4秒前
123发布了新的文献求助10
4秒前
5秒前
dingly发布了新的文献求助10
5秒前
生动甜瓜发布了新的文献求助10
5秒前
5秒前
5秒前
5秒前
6秒前
Yael完成签到,获得积分10
6秒前
6秒前
alexia_liang发布了新的文献求助10
6秒前
hexiao完成签到,获得积分10
7秒前
7秒前
7秒前
7秒前
科研通AI6.4应助Maple采纳,获得10
7秒前
chenxi完成签到,获得积分10
7秒前
yuaasusanaann发布了新的文献求助10
7秒前
dde应助Maple采纳,获得10
8秒前
Cy完成签到,获得积分10
8秒前
万能图书馆应助Maple采纳,获得10
8秒前
情怀应助Maple采纳,获得10
8秒前
科研通AI2S应助Maple采纳,获得10
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Navigating Normative Orders. Interdisciplinary Perspectives 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
CLSI VET01S-2024 Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals (7th Ed) 500
A Case Study on Hotels as Noncongregate Emergency Living Accommodations for Returning Citizens 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7757112
求助须知:如何正确求助?哪些是违规求助? 9303589
关于积分的说明 20275045
捐赠科研通 7340667
什么是DOI,文献DOI怎么找? 3311745
关于科研通互助平台的介绍 2462624
邀请新用户注册赠送积分活动 2325433