Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across. (December 2022)

• Record Type: Journal Article
• Authors: Banerjee, Arkaprava; De, Priyanka; Kumar, Vinay; Kar, Supratik; Roy, Kunal
• Is Part Of: Chemosphere. Volume 309:Part 1(2022)
• Publication Date: 2022-12
• Subjects: Endocrine disruptors -- Androgen receptor binding affinity -- QSAR -- Read-across -- Docking -- Pharmacophore -- q-RASAR; QSAR Quantitative structure-activity relationship -- q-RASAR Quantitative read-across structure-activity relationship -- PLS Partial least squares -- RBA Receptor binding affinity -- EDC Endocrine disruptor chemical -- MM/GBSA Molecular mechanics/Generalized Born surface area -- AR Androgen receptor -- OECD Organization for Economic Co-operation and Development; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2022.136579
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the receptor binding data from the EDKB database (https://www.fda.gov/science-research/endocrine-disruptor-knowledge-base/accessing-edkb-database ) and then employed the DTC-QSAR tool, available from https://dtclab.webs.com/software-tools, for dataset division, feature selection, and model development. The final partial least squares model was evaluated using various stringent validation criteria. From the model, we interpreted that hydrophobicity, steroidal nucleus, bulkiness and a hydrogen bond donor at an appropriate position contribute to the receptor binding affinity, while presence of electron rich features like aromaticity and polar groups decrease the receptor binding affinity. Additionally we have also performed chemical Read-Across predictions using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better than the QSAR-derived predictions. The best quality of external predictions emerged from the q-RASAR approach which combines both read-across and QSAR. To explore the essential features responsible for the receptor binding, pharmacophore mapping, molecular docking along with molecular dynamics simulation were also performed, and the results are in accordance with the QSAR/q-RASAR findings. Graphical abstract: Exploring androgen receptor binding affinity by QSAR, read-across, pharmacophore, molecular docking and molecular dynamics simulation studies Image 1 Highlights: Androgen receptor binding affinity of androgen disruptor chemicals has been subjected to QSAR analyses. Interpretable 2D descriptors explore essential features of binding. Predictions are also made based on application of Chemical Read-Across and q-RASAR. The developed 2D-QSAR and q-RASAR models are reproducible and easily transferable for application. The selected features are also supported by 3D-pharmacophore mapping, molecular docking and dynamics simulation.