Albrecht

Antoon

Auvray

Bansal

Booth

Cadoo

Calle

Cantón

ChemAxon Ltd

Davies

Dinno

Eisen

Fontham

Fourches

Ghosh

Hellberg

Khodarahmi

Kuhn

Liaw

Lipton

Mevik

Meyer

Nantasenamat

Pingaew

Prusis

R Core Team

Schomburg

Shoombuatong

Simpson

Suvannang

Worachartcheewan

Zwick

PeerJ

English

Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high selectivity, lower toxicity and increased potency. Designing a novel therapeutic agent against aromatase could be achieved computationally by means of ligand-based and structure-based methods. For over a decade, we have utilized both approaches to design potential AIs for which quantitative structure–activity relationships and molecular docking were used to explore inhibitory mechanisms of AIs towards aromatase. However, such approaches do not consider the effects that aromatase variants have on different AIs. In this study, proteochemometrics modeling was applied to analyze the interaction space between AIs and aromatase variants as a function of their substructural and amino acid features. Good predictive performance was achieved, as rigorously verified by 10-fold cross-validation, external validation, leave-one-compound-out cross-validation, leave-one-protein-out cross-validation and Y-scrambling tests. The investigations presented herein provide important insights into the mechanisms of aromatase inhibitory activity that could aid in the design of novel potent AIs as breast cancer therapeutic agents.</jats:p

Saw Simeon

Ola Spjuth

Maris Lapins

Sunanta Nabu

Nuttapat Anuwongcharoen

Virapong Prachayasittikul

Jarl E.S. Wikberg

Chanin Nantasenamat

Crossref

Origin of aromatase inhibitory activity via proteochemometric modeling

Data S1: Supplementary information and supplementary tables describing the data set used in this study

Data S1: Supplementary information and supplementary tables describing the data set used in this study

Abstract

Similar works

Full text

Available Versions

Crossref