Computational Evaluation of Bioactive Compounds from Colocasia affinis Schott as a Novel EGFR Inhibitor for Cancer Treatment.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2021-10-08 eCollection Date: 2021-01-01 DOI:10.1177/11769351211049244
Toheeb A Balogun, Nureni Ipinloju, Olayemi T Abdullateef, Segun I Moses, Damilola A Omoboyowa, Akinwumi C James, Oluwatosin A Saibu, Wumi F Akinyemi, Ebenezer A Oni
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Abstract

Introduction: Epidermal growth factor receptor (EGFR) is a transmembrane protein that belongs to the ErbB/HER-family of tyrosine kinase receptors. Somatic mutations and overexpression of EGFR have been reported to play a vital role in cancer cell development and progression, including cell proliferation, differentiation, angiogenesis, apoptosis, and metastatic spread. Hence, EGFR is an important therapeutic target for the treatment of various types of epithelial cancers. Somatic mutations have led to resistance to clinically approved synthetic EGFR inhibitors. Furthermore, synthetic EGFR inhibitors have been associated with several side effects. Thus, there is a need to develop novel EGFR inhibitors with an acceptable biosafety profile and high efficacy.

Methods: Herein, we employed structural bioinformatics and theoretical chemistry techniques via molecular docking, molecular mechanics generalized Born surface area (MM-GBSA) calculation, density functional theory analysis (DFT), and pharmacokinetic study to identify novel EGFR inhibitors.

Results: The stringent molecular docking and MM-GBSA calculations identified MET 793, LYS 745, PHE 723, ASP 855, ARG 411, and THR 854 as principal amino acid residues for EGFR-ligands interactions. Furthermore, Colocasia affinis Schott compounds exhibited higher binding energy and more stable interactions than the reference compound (gefitinib). DFT analysis also ascertains better bioactivity and chemical reactivity of C. affinis Schott with favorable intramolecular charge transfer between electron-donor and electron acceptor groups. The pharmacokinetic profile of C. affinis Schott bioactive compounds satisfies Lipinski's rule of five assessment.

Conclusion: Collectively, C. affinis Schott compounds demonstrated higher inhibitory potentials against EGFR and better pharmacological properties when compared with gefitinib. C. affinis Schott compounds are therefore suggested as promising therapeutic EGFR inhibitors for cancer treatment.

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将来自 Colocasia affinis Schott 的生物活性化合物作为治疗癌症的新型表皮生长因子受体抑制剂的计算评估。
导言:表皮生长因子受体(EGFR)是一种跨膜蛋白,属于酪氨酸激酶受体的ErbB/HER家族。据报道,表皮生长因子受体(EGFR)的体细胞突变和过表达在癌细胞的发育和进展中起着至关重要的作用,包括细胞增殖、分化、血管生成、凋亡和转移扩散。因此,表皮生长因子受体是治疗各类上皮癌的重要治疗靶点。体细胞突变导致了对临床批准的合成表皮生长因子受体抑制剂的耐药性。此外,合成表皮生长因子受体抑制剂还伴有多种副作用。方法:在本文中,我们采用了结构生物信息学和理论化学技术,通过分子对接、分子力学广义伯恩表面积(MM-GBSA)计算、密度泛函理论分析(DFT)和药代动力学研究来鉴定新型表皮生长因子受体抑制剂:严格的分子对接和 MM-GBSA 计算确定 MET 793、LYS 745、PHE 723、ASP 855、ARG 411 和 THR 854 为表皮生长因子受体与配体相互作用的主要氨基酸残基。此外,与参考化合物(吉非替尼)相比,Colocasia affinis Schott 化合物表现出更高的结合能和更稳定的相互作用。DFT 分析还确定,C. affinis Schott 具有更好的生物活性和化学反应活性,电子供体和电子受体基团之间的分子内电荷转移非常有利。C. affinis Schott 生物活性化合物的药代动力学特征符合利宾斯基的五项评估规则:总之,与吉非替尼相比,C. affinis Schott化合物对表皮生长因子受体具有更高的抑制潜力和更好的药理特性。因此,C. affinis Schott 化合物有望成为治疗癌症的表皮生长因子受体抑制剂。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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