a Unit for Drug Discovery, Department of Parasitology, Institute of Biomedical Sciences , University of São Paulo , São Paulo , Brazil.
b Fraunhofer Institute for Molecular Biology und Applied Ecology IME , Hamburg , Germany.
J Biomol Struct Dyn. 2018 Dec;36(16):4378-4391. doi: 10.1080/07391102.2017.1417161. Epub 2018 Jan 10.
Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q, Q, and Q), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.
法尼醇 X 受体 (FXR) 与脂质和葡萄糖稳态有关,被认为是治疗糖尿病、血脂异常和肝癌等代谢性疾病的重要分子靶点。目前,文献中有几种 FXR 激动剂被报道,其中一些正在进行肝脏疾病的临床试验。在此,我们采用了一系列化合物来生成 QSAR 模型,以更好地了解芳基丙氨酸衍生物 (AAD) 激活 FXR 的结构基础。此外,我们还评估了将 EC 值标准差 (SD) 纳入 QSAR 模型质量的效果。通过比较使用实验方差加平均值构建模型与仅考虑平均值的标准模型生成方法,评估了在模型构建中包含 SD 值的效果。与仅基于平均值的模型相比,基于 AAD 数据集包含 SD 值的 2D 和 3D QSAR 模型显示出相似的分子解释图谱和质量 (Q、Q 和 Q)。基于 SD 的模型对测试化合物集的预测更为准确,平均绝对误差指数更低,残差更接近零。此外,不同 QSAR 方法的直观解释与实验数据一致,突出了理解 AAD 生物学活性的关键因素。使用标准差值的方法可能为基于现有实验数据生成更准确的 QSAR 模型提供新的可能性。