» Articles » PMID: 30190660

Towards Understanding Aromatase Inhibitory Activity Via QSAR Modeling

Overview
Journal EXCLI J
Specialty Biology
Date 2018 Sep 8
PMID 30190660
Citations 12
Authors
Affiliations
Soon will be listed here.
Abstract

Aromatase is a rate-limiting enzyme for estrogen biosynthesis that is overproduced in breast cancer tissue. To block the growth of breast tumors, aromatase inhibitors (AIs) are employed to bind and inhibit aromatase in order to lower the amount of estrogen produced in the body. Although a number of synthetic aromatase inhibitors have been released for clinical use in the treatment of hormone-receptor positive breast cancer, these inhibitors may lead to undesirable side effects (e.g. increased rash, diarrhea and vomiting; effects on the bone, brain and heart) and therefore, the search for novel AIs continues. Over the past decades, there has been an intense effort in employing medicinal chemistry and quantitative structure-activity relationship (QSAR) to shed light on the mechanistic basis of aromatase inhibition. To the best of our knowledge, this article constitutes the first comprehensive review of all QSAR studies of both steroidal and non-steroidal AIs that have been published in the field. Herein, we summarize the experimental setup of these studies as well as summarizing the key features that are pertinent for robust aromatase inhibition.

Citing Articles

First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183.

Bhattacharjee A, Kar S, Ojha P Mol Divers. 2024; 28(6):4199-4220.

PMID: 38460065 DOI: 10.1007/s11030-024-10811-1.


Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists.

Ferdous N, Reza M, Hossain M, Mahmud S, Napis S, Chowdhury K PLoS One. 2023; 18(6):e0287179.

PMID: 37352252 PMC: 10289339. DOI: 10.1371/journal.pone.0287179.


Towards combating antibiotic resistance by exploring the quantitative structure-activity relationship of NDM-1 inhibitors.

Yu T, Malik A, Anuwongcharoen N, Eiamphungporn W, Nantasenamat C, Piacham T EXCLI J. 2022; 21:1331-1351.

PMID: 36540675 PMC: 9755517. DOI: 10.17179/excli2022-5380.


SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins.

Ahmad S, Charoenkwan P, Quinn J, Moni M, Hasan M, Lio P Sci Rep. 2022; 12(1):4106.

PMID: 35260777 PMC: 8904530. DOI: 10.1038/s41598-022-08173-5.


Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates.

Wang L, Ding J, Shi P, Fu L, Pan L, Tian J Arch Toxicol. 2021; 95(7):2443-2457.

PMID: 33934188 DOI: 10.1007/s00204-021-03056-6.


References
1.
Coombes R, Kilburn L, Snowdon C, Paridaens R, Coleman R, Jones S . Survival and safety of exemestane versus tamoxifen after 2-3 years' tamoxifen treatment (Intergroup Exemestane Study): a randomised controlled trial. Lancet. 2007; 369(9561):559-70. DOI: 10.1016/S0140-6736(07)60200-1. View

2.
Klebe G, Abraham U, Mietzner T . Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem. 1994; 37(24):4130-46. DOI: 10.1021/jm00050a010. View

3.
Simpson E, Mahendroo M, Means G, Kilgore M, Hinshelwood M, Amarneh B . Aromatase cytochrome P450, the enzyme responsible for estrogen biosynthesis. Endocr Rev. 1994; 15(3):342-55. DOI: 10.1210/edrv-15-3-342. View

4.
Barigye S, Freitas M, Ausina P, Zancan P, Sola-Penna M, Castillo-Garit J . Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity. ACS Comb Sci. 2018; 20(2):75-81. DOI: 10.1021/acscombsci.7b00155. View

5.
Nelson L, Bulun S . Estrogen production and action. J Am Acad Dermatol. 2001; 45(3 Suppl):S116-24. DOI: 10.1067/mjd.2001.117432. View