Identifying potential endocrine disruptors among industrial chemicals and their metabolites – development and evaluation of in silico tools

Abstract

The aim of this study was to improve the identification of endocrine disrupting chemicals (EDCs) by developing and evaluating in silico tools that predict interactions at the estrogen (E) and androgen (A) receptors, and binding to transthyretin (T). In particular, the study focuses on evaluating the use of the EAT models in combination with a metabolism simulator to study the significance of bioactivation for endocrine disruption. Balanced accuracies of the EAT models ranged from 77-87%, 62-77%, and 65-89% for E-, A-, and T-binding respectively. The developed models were applied on a set of more than 6000 commonly used industrial chemicals of which 9% were predicted E- and/or A-binders and 1% were predicted T-binders. The numbers of E- and T-binders increased 2- and 3-fold, respectively, after metabolic transformation, while the number of A-binders marginally changed. In-depth validation confirmed that several of the predicted bioactivated E- or T-binders demonstrated in vivo estrogenic activity or influenced blood levels of thyroxine in vivo. The metabolite simulator was evaluated using in vivo data from the literature which showed a 50% accuracy for studied chemicals. The study stresses, in summary, the importance of including metabolic activation in prioritization activities of potentially emerging contaminants.

QSAR without borders

“…Therefore, incorporation of information about metabolic activation can improve toxicity QSAR models. 96 AOP facilitates mechanistic interpretation of models, provides a better understanding of toxicity, and allows the development of new in vitro tests. 97 Currently, the development and validation of such tests is an emerging topic in predictive toxicology.…”

MetaPASS: A Web Application for Analyzing the Biological Activity Spectrum of Organic Compounds Taking into Account their Biotransformation

“…Drug metabolism affects drug efficiency and toxicity, [6,7] and incorporating information about metabolic activation can improve QSAR models of toxicity [8] . Therefore, it is reasonable to include the prediction of the metabolite‘s biological activity in predicting the biological activity spectrum (BAS) for a parent compound.…”

Transformer-CNN: Fast and Reliable Tool for QSAR

“…Lipo Lipophilicity [47] 4,200 BBBP Blood-brain barrier [43] 2,039 BACE IC50 of human β-secretase 1 (BACE-1) inhibitors [43] 1,513 JAK3 Janus kinase 3 inhibitor [48] 886 DHFR Dihydrofolate reductase inhibition [49] 739 BioDeg Biodegradability [50] 1,737 LEL Lowest effect level [51] 483 RP AR Endocrine disruptors [52] 930 To validate the model, we sampled 500,000 ChEMBL-like SMILES (only 8,617 (1.7%) of them were canonical) from a generator [53] and checked how accurately the model can restore canonical SMILES for these molecules. We intentionally selected the generated SMILES keeping in mind possible application of the proposed method in the artificial intelligence-driven pipelines of de-novo development of new drugs.…”