Towards understanding aromatase inhibitory activity via QSAR modeling

Authors

  • Watshara Shoombuatong Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
  • Nalini Schaduangrat Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
  • Chanin Nantasenamat Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand

DOI:

https://doi.org/10.17179/excli2018-1417

Keywords:

aromatase, aromatase inhibitors, breast cancer, estrogen, QSAR, structure-activity relationship, data mining

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.

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Published

2018-07-20

How to Cite

Shoombuatong, W., Schaduangrat, N., & Nantasenamat, C. (2018). Towards understanding aromatase inhibitory activity via QSAR modeling. EXCLI Journal, 17, 688–708. https://doi.org/10.17179/excli2018-1417

Issue

Vol. 17 (2018)

Section

Review articles

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