发现表皮生长因子受体(EGFR)、人表皮生长因子受体2 (HER2)、雌激素受体(ER)和磷脂酰肌醇-3-激酶a (PI3Ka)用于个性化乳腺癌治疗的有希望的抑制剂

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2022-10-04 eCollection Date: 2022-01-01 DOI:10.1177/11769351221127862
Precious A Akinnusi, Samuel O Olubode, Ayomide O Adebesin, Toluwani A Nana, Sidiqat A Shodehinde
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引用次数: 2

摘要

尽管在改善治疗方面取得了快速发展和进步,但乳腺癌仍然是最致命的健康挑战之一,也是最常被诊断出来的肿瘤。治疗的主要问题之一是每个癌细胞表现出的独特差异。因此,基于肿瘤的特异性,如生长因子受体(表皮生长因子受体(EGFR)、人表皮生长因子受体2 (HER2))、激素受体(人雌激素受体α (ER))和参与与生长相关的关键信号传导的激酶(磷脂酰肌醇3-激酶(PI3K))的过表达,乳腺癌的治疗现在变得更加个性化。已经开发了几种化疗药物来抑制这种威胁,但相关的不良药物效应不容忽视。为此,本研究采用分子建模方法来鉴定天然来源的新型化合物,这些化合物可以潜在地拮抗与乳腺癌病理生理相关的受体(如上所述),同时产生很少或没有副作用。对118种花青素与蛋白靶点结合袋之间的生物相互作用进行分子模型分析,鉴定出5种与蛋白靶点具有良好结合亲和力的化合物(Pelargonin、Delphinidin 3-O-rutinoside、Malvin、Cyanidin-3-(6-乙酰糖苷)和Peonidin 3-O-rutinoside)。进一步的MM-GBSA计算得到了高结合能。本文分析并报道了化合物与靶标之间的特定分子相互作用。此外,所有化合物都表现出良好的药代动力学特征,因此建议进行进一步分析,因为它们可以作为广泛和个性化乳腺癌治疗的新治疗选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Discovery of Promising Inhibitors of Epidermal Growth Factor Receptor (EGFR), Human Epidermal Growth Factor Receptor 2 (HER2), Estrogen Receptor (ER), and Phosphatidylinositol-3-kinase a (PI3Ka) for Personalized Breast Cancer Treatment.

Discovery of Promising Inhibitors of Epidermal Growth Factor Receptor (EGFR), Human Epidermal Growth Factor Receptor 2 (HER2), Estrogen Receptor (ER), and Phosphatidylinositol-3-kinase a (PI3Ka) for Personalized Breast Cancer Treatment.

Discovery of Promising Inhibitors of Epidermal Growth Factor Receptor (EGFR), Human Epidermal Growth Factor Receptor 2 (HER2), Estrogen Receptor (ER), and Phosphatidylinositol-3-kinase a (PI3Ka) for Personalized Breast Cancer Treatment.

Discovery of Promising Inhibitors of Epidermal Growth Factor Receptor (EGFR), Human Epidermal Growth Factor Receptor 2 (HER2), Estrogen Receptor (ER), and Phosphatidylinositol-3-kinase a (PI3Ka) for Personalized Breast Cancer Treatment.

Despite the rapid developments and advancements to improve treatments, Breast cancer remains one of the deadliest health challenges and the most frequently diagnosed tumor. One of the major problems with treatment is the unique difference that each cancerous cell exhibits. As a result, treatment of breast cancer has now become more personalized based on the specific features of the tumor such as overexpression of growth factor receptors (Epidermal growth factor receptor (EGFR), Human Epidermal Growth Factor Receptor 2 (HER2)), hormone receptors (Human Estrogen receptor alpha (ER)) and kinases involved in pivotal signaling associated with growth (Phosphatidylinositol 3-kinase (PI3K)). Several chemotherapeutic agents have been developed to curb the menace, but the associated adverse drug effects cannot be overlooked. To this end, this study employed a molecular modeling approach to identify novel compounds of natural origin that can potentially antagonize the receptors (mentioned above) associated with the pathophysiology of breast cancer and at the same time pose very little or no side effects. The results of the molecular model of biological interactions between a library of 118 anthocyanins and the binding pockets of the protein targets identified 5 compounds (Pelargonin, Delphinidin 3-O-rutinoside, Malvin, Cyanidin-3-(6-acetylglucoside), and Peonidin 3-O-rutinoside) with good binding affinities to the protein targets. Further MM-GBSA calculations returned high binding energies. The specific molecular interactions between the compounds and the targets were analyzed and reported herein. Also, all the compounds exhibited good pharmacokinetic profiles and are therefore recommended for further analyses as they could be explored as new treatment options for a broad range and personalized breast cancer treatments.

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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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