Identification of potential therapeutic dual inhibitors of EGFR/HER2 in breast cancer

Megha Jethwa, Aditi Gangopadhyay, Achintya Saha
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Abstract

Breast cancer (BC) is the leading cause of death among women worldwide. According to the Breast Cancer Research Foundation (BCRF), 25% of all cases of BC are positive for human epidermal growth factor receptor 2 (HER2), which is the most aggressive phenotype among the five BC subtypes. Previous studies have reported that the epidermal growth factor receptor (EGFR) is also overexpressed in HER2-positive BC, which elevates disease severity. Based on these findings, the present study aimed to identify dual inhibitors of EGFR and HER2 by employing chemometric modelling techniques. A dataset of chemical molecules with affinity for both EGFR and HER2 was prepared by literature review. The dataset was split into training and test sets based on the inhibitory concentration (IC50) for EGFR and HER2. The training set was used to generate two pharmacophore models, one each for EGFR (n = 30, R2 value = 0.82 with RMSD = 1.4, Δ cost = 151.84, and configuration cost = 20.3) and HER2 (n = 30, R2 value = 0.84 with RMSD = 1.0, Δ cost = 68.47, and configuration cost = 22.2). The developed models were validated using the test set (n = 214 and 201, andR2pred = 0.73 and 0.70, for EGFR and HER2, respectively), decoy set (decoys = 104, actives = 18), and an external dataset (n = 20). The robustness of the models was validated using Fischer's randomization method (at 95% confidence) and applicability domain analysis. The validated models for EGFR and HER2 were used to screen the Asinex library (n = 575,302) for identifying consensus hits against both targets. Molecules with predicted IC50 < 20 nM were subsequently screened, and their toxicity profiles were evaluated using ProTox II. The interactions, ligand efficiency, and binding affinities of the selected compounds were assessed from the docking scores and molecular mechanics with generalized Born and surface area solvation (MMGBSA) energy. Hit selection against EGFR and HER2 was finally achieved by molecular dynamics simulations using the OPLS4 force field in Desmond. The identified hit can serve as a reference for developing dual inhibitors of EGFR and HER2 in future.

确定乳腺癌表皮生长因子受体(EGFR)/表皮生长因子受体(HER2)的潜在双重治疗抑制剂
乳腺癌(BC)是导致全球妇女死亡的主要原因。根据乳腺癌研究基金会(BCRF)的数据,25%的乳腺癌病例的人类表皮生长因子受体 2(HER2)呈阳性,这是五种乳腺癌亚型中最具侵袭性的表型。以往的研究表明,表皮生长因子受体(EGFR)也在 HER2 阳性的 BC 中过度表达,从而加重了疾病的严重程度。基于这些发现,本研究采用化学计量建模技术,旨在确定表皮生长因子受体和 HER2 的双重抑制剂。通过查阅文献,准备了对表皮生长因子受体(EGFR)和表皮生长因子受体(HER2)具有亲和力的化学分子数据集。根据表皮生长因子受体(EGFR)和表皮生长因子受体(HER2)的抑制浓度(IC50),将数据集分为训练集和测试集。训练集用于生成两个药源模型,分别用于 EGFR(n = 30,R2 值 = 0.82,RMSD = 1.4,Δ成本 = 151.84,配置成本 = 20.3)和 HER2(n = 30,R2 值 = 0.84,RMSD = 1.0,Δ成本 = 68.47,配置成本 = 22.2)。使用测试集(n = 214 和 201,EGFR 和 HER2 的 R2pred 值分别为 0.73 和 0.70)、诱饵集(诱饵 = 104,活性 = 18)和外部数据集(n = 20)对所开发的模型进行了验证。模型的稳健性通过费舍尔随机方法(置信度为 95%)和适用域分析进行了验证。经过验证的表皮生长因子受体(EGFR)和表皮生长因子受体(HER2)模型被用于筛选Asinex库(n = 575,302),以确定针对这两个靶点的共识靶点。随后筛选了预测 IC50 < 20 nM 的分子,并使用 ProTox II 评估了它们的毒性特征。根据对接得分和广义玻恩与表面积溶解(MMGBSA)能分子力学评估了所选化合物的相互作用、配体效率和结合亲和力。最后,通过在 Desmond 中使用 OPLS4 力场进行分子动力学模拟,筛选出了针对表皮生长因子受体和 HER2 的药物。所确定的靶点可作为今后开发表皮生长因子受体和 HER2 双抑制剂的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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