REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal.
Curr Top Med Chem. 2018;18(3):219-232. doi: 10.2174/1568026618666180329123023.
Epidermal Growth Factor Receptor (EGFR) is still the main target of the Head and Neck Squamous Cell Cancer (HNSCC) because its overexpression has been detected in more than 90% of this type of cancer. This overexpression is usually linked with more aggressive disease, increased resistance to chemotherapy and radiotherapy, increased metastasis, inhibition of apoptosis, promotion of neoplastic angiogenesis, and, finally, poor prognosis and decreased survival. Due to this reason, the main target in the search of new drugs and inhibitors candidates is to downturn this overexpression. Quantitative Structure-Activity Relationship (QSAR) is one of the most widely used approaches while looking for new and more active inhibitors drugs. In this contest, a lot of authors used this technique, combined with others, to find new drugs or enhance the activity of well-known inhibitors. In this paper, on one hand, we will review the most important QSAR approaches developed in the last fifteen years, spacing from classical 1D approaches until more sophisticated 3D; the first paper is dated 2003 while the last one is from 2017. On the other hand, we will present a completely new QSAR approach aimed at the prediction of new EGFR inhibitors drugs. The model presented here has been developed over a dataset consisting of more than 1000 compounds using various molecular descriptors calculated with the DRAGON 7.0© software.
表皮生长因子受体（EGFR）仍然是头颈部鳞状细胞癌（HNSCC）的主要靶点，因为超过 90%的这种癌症都检测到其过表达。这种过表达通常与更具侵袭性的疾病、增加对化疗和放疗的耐药性、增加转移、抑制细胞凋亡、促进肿瘤血管生成有关，最终导致预后不良和生存率降低。由于这个原因，在寻找新药物和抑制剂候选物时，主要目标是降低这种过表达。定量构效关系（QSAR）是寻找新的、更有效的抑制剂药物时最广泛使用的方法之一。在这方面，许多作者使用这种技术，结合其他技术，寻找新的药物或增强知名抑制剂的活性。在本文中，一方面，我们将回顾过去十五年开发的最重要的 QSAR 方法，从经典的 1D 方法到更复杂的 3D 方法；第一篇论文发表于 2003 年，最后一篇论文发表于 2017 年。另一方面，我们将介绍一种全新的 QSAR 方法，旨在预测新的 EGFR 抑制剂药物。这里提出的模型是在一个由超过 1000 种化合物组成的数据集上开发的，使用了各种分子描述符，这些描述符是用 DRAGON 7.0©软件计算的。
