Maruthanila V L, Elancheran R, Roy Nand Kishor, Bhattacharya Anupam, Kunnumakkara Ajaikumar B, Kabilan S, Kotoky Jibon
Department of Biotechnology, A.V.C. College, Mannampandal-609305, Tamil Nadu, India.
Drug Discovery Lab, Life Sciences Division, Institute of Advanced Study in Science and Technology, Guwahati-781035, Assam, India.
Curr Comput Aided Drug Des. 2019;15(1):89-96. doi: 10.2174/1573409914666181008165356.
Breast cancer is one of the most common cancers diagnosed among women. It is now recognized that two receptors mediate estrogen action and the presence of estrogen receptor alpha (ERα) correlates with better prognosis and the likelihood of response to hormonal therapy. ERα is an attractive target for the treatment of breast cancer. Most of the drugs currently used for the breast cancer treatment have numerous side effects and they are often unsuccessful in removing the tumour completely. Hence, we focused on natural compounds like flavonoids, polyphenols, etc. which do not exhibit any high toxic effects against normal cells.
To identify the potential natural inhibitors for BCa through an optimised in silico approach.
Structural modification and molecular docking-based screening approaches were imposed to identify the novel natural compounds by using Schrödinger (Maestro 9.5). The Qikprop v3.5 was used for the evaluation of important ADME parameters and its permissible ranges. Cytotoxicity of the compounds was evaluated by MTT assay against MCF-7 Cell lines.
From the docking studies, we found that the compounds, Myricetin, Quercetin, Apigenin, Luteolin and Baicalein showed the highest Glide Scores -10.78, -9.48, -8.92, -8.87 and -8.82 kcal mol-1 respectively. Of these, Luteolin and Baicalein showed the significant IC50 values (25 ± 4.0 and 58.3 ± 4.4 µM, respectively) against MCF-7 cell line. The ADME profiling of the test compounds was evaluated to find the drug-likeness and pharmacokinetic parameters.
We mainly focused on in silico study to dock the compounds into the human estrogen receptor ligand binding domain (hERLBD) and compare their predicted binding affinity with known antiestrogens. Myricetin, Quercetin, Apigenin, Luteolin and Baicalein were identified as the most promising among all. Of these, Luteolin and Baicalein showed significant anticancer activities against MCF-7 cell line. These findings may provide basic information for the development of anti-breast cancer agents.
乳腺癌是女性中最常见的诊断出的癌症之一。现在已知两种受体介导雌激素作用，雌激素受体α（ERα）的存在与较好的预后以及对激素治疗的反应可能性相关。ERα是乳腺癌治疗的一个有吸引力的靶点。目前用于乳腺癌治疗的大多数药物有许多副作用，并且它们常常不能完全消除肿瘤。因此，我们专注于黄酮类、多酚类等天然化合物，它们对正常细胞没有任何高毒性作用。
通过优化的计算机模拟方法鉴定乳腺癌的潜在天然抑制剂。
采用结构修饰和基于分子对接的筛选方法，使用薛定谔软件（Maestro 9.5）鉴定新型天然化合物。使用Qikprop v3.5评估重要的药物代谢动力学参数及其允许范围。通过MTT法针对MCF-7细胞系评估化合物的细胞毒性。
从对接研究中，我们发现杨梅素、槲皮素、芹菜素、木犀草素和黄芩苷分别显示出最高的Glide评分-10.78、-9.48、-8.92、-8.87和-8.82千卡/摩尔。其中，木犀草素和黄芩苷对MCF-7细胞系显示出显著的IC50值（分别为25±4.0和58.3±4.4微摩尔）。评估测试化合物的药物代谢动力学特征以发现类药性质和药代动力学参数。
我们主要专注于计算机模拟研究，将化合物对接至人雌激素受体配体结合域（hERLBD），并将它们预测的结合亲和力与已知抗雌激素进行比较。杨梅素、槲皮素、芹菜素、木犀草素和黄芩苷被鉴定为所有化合物中最有前景的。其中，木犀草素和黄芩苷对MCF-7细胞系显示出显著的抗癌活性。这些发现可能为抗乳腺癌药物的开发提供基础信息。
