Abstract
The development of breast cancer is closely related to ERα gene, which has been identified as an important target for the treatment of breast cancer. The establishment of effective Quantitative structure-activity relationship model (QSAR) of compounds can predict the biological activity of new compounds well and provide help for the research and development of anti-breast cancer drugs. However, it is not enough to screen potential compounds only depending on biological activity. ADMET properties of drugs also need to be considered. In this paper, based on the existing data set, we perform hierarchical clustering on 729 variables, and calculate the Pearson correlation coefficient between them and the pIC50 value of biological activity, and screen out five variables that have a significant impact on biological activity. Perform multiple linear regression on these five molecular descriptors and the biological activity values, and then use the multiple stepwise regression method to optimize to establish a QSAR model. Furthermore, Fisher discriminant analysis is used to classify and predict the ADMET properties of the new compounds. Both models have good statistical parameters and reliable prediction ability. As a result, we come to a conclusion that Oc1ccc(cc1)C2 = C(c3ccc(C = O)cc3)c4ccc(F)cc4OCC2 and other compounds not only have high biological activity, but also have great ADMET properties, which could be used as potential anti-breast cancer drug compounds. These results provide a certain theoretical basis for the development and validation of new anti-breast cancer drugs in the future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chou, J., Shen, R., Zhou, H.B., et al.: OBHS impairs the viability of breast cancer via decreasing ERα and Atg13. Biochem. Biophys. Res. Commun. 573, 69–75 (2021)
Yu, J., Li, F., Li, Y., et al.: The effects of hsa_circ_0000517/miR-326 axis on the progression of breast cancer cells and the prediction of miR-326 downstream targets in breast cancer. Pathol.-Res. Pract. 227, 153638 (2021)
Munne, P.M., Martikainen, L., Räty, I., et al.: Compressive stress-mediated p38 activation required for ERα+ phenotype in breast cancer. Nat. Commun. 12(1), 1–17 (2021)
Cherkasov, A., Muratov, E.N., Fourches, D., et al.: QSAR modeling: Where have you been? Where are you going to? J. Med. Chem. 57(12), 4977–5010 (2014)
Sun, J., Wang, Y.J., He, Z.G.: Biodistribution chromatography: High-throughput screening in drug membrane permeability and activity. Prog. Chem. 18(0708), 1002 (2006)
Tong, J.B., Zhang, X., Bian, S., Luo, D.: Drug design, molecular docking, and ADMET prediction of CCR5 inhibitors based on QSAR study. Chin. J. Struct. Chem. 41(02), 1–13 (2022)
Ran, B.B., et al.: Research progress on tumor protein biomarkers using high-throughput proteomics based on mass spectrometry. Chin. J. Clin. Oncol. 47(08), 411–417 (2020)
Zeng, S.N., Li, Q.W., Pan, T., et al.: Establishment and application of high-throughput screening model for antiviral agents targeting EV71 3C (pro). Prog. Biochem. Biophys. 44(9), 776–782 (2017)
Sagara, A., Nakata, K., Yamashita, T., et al.: New high-throughput screening detects compounds that suppress pancreatic stellate cell activation and attenuate pancreatic cancer growth. Pancreatology 21(6), 1071–1080 (2021)
Huang, H.J., Chetyrkina, M., Wong, C.W., et al.: Identification of potential descriptors of water-soluble fullerene derivatives responsible for antitumor effects on lung cancer cells via QSAR analysis. Comput. Struct. Biotechnol. J. 19, 812–825 (2021)
Wang, X.C., Yang, M.C., Zhang, M.X., et al.: 3D-QSAR, molecular docking and molecular dynamics simulations of 3-Phenylsulfonylaminopyridine derivatives as novel PI3Kα inhibitors. Chin. J. Struct. Chem. 40(12), 1567–1585 (2021)
Zekri, A., Harkati, D., Kenouche, S., et al.: QSAR modeling, docking, ADME and reactivity of indazole derivatives as antagonizes of estrogen receptor alpha (ER-α) positive in breast cancer. J. Mol. Struct. 1217, 128442 (2020)
He, L., Jurs, P.C.: Assessing the reliability of a QSAR model’s predictions. J. Mol. Graph. Model. 23(6), 503–523 (2005)
Putri, D.E.K., Pranowo, H.D., Wijaya, A.R., et al.: The predicted models of anti-colon cancer and anti-hepatoma activities of substituted 4-anilino coumarin derivatives using quantitative structure-activity relationship (QSAR). J. King Saud Univ.-Sci. 34(3), 101837 (2022)
Acknowledgments
Research work in this paper is supported by the National Natural Science Foundation of China (Grant No. 72171173) and Shanghai Science and Technology Innovation Action Plan (No. 19DZ1206800).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, W., Huang, Z., Zhang, H., Lu, J. (2022). Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-13829-4_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13828-7
Online ISBN: 978-3-031-13829-4
eBook Packages: Computer ScienceComputer Science (R0)