Prediction of Estrogenic Bioactivity of Environmental Chemical Metabolites

Abstract

The US Environmental Protection Agency's (EPA) Endocrine Disruptor Screening Program (EDSP) is using in vitro data generated from ToxCast/Tox21 high-throughput screening assays to assess the endocrine activity of environmental chemicals. Considering that in vitro assays may have limited metabolic capacity, inactive chemicals that are biotransformed into metabolites with endocrine bioactivity may be missed for further screening and testing. Therefore, there is a value in developing novel approaches to account for metabolism and endocrine activity of both parent chemicals and their associated metabolites. We used commercially available software to predict metabolites of 50 parent compounds, out of which 38 chemicals are known to have estrogenic metabolites, and 12 compounds and their metabolites are negative for estrogenic activity. Three ER QSAR models were used to determine potential estrogen bioactivity of the parent compounds and predicted metabolites, the outputs of the models were averaged, and the chemicals were then ranked based on the total estrogenicity of the parent chemical and metabolites. The metabolite prediction software correctly identified known estrogenic metabolites for 26 out of 27 parent chemicals with associated metabolite data, and 39 out of 46 estrogenic metabolites were predicted as potential biotransformation products derived from the parent chemical. The QSAR models estimated stronger estrogenic activity for the majority of the known estrogenic metabolites compared to their parent chemicals. Finally, the three models identified a similar set of parent compounds as top ranked chemicals based on the estrogenicity of putative metabolites. This proposed in silico approach is an inexpensive and rapid strategy for the detection of chemicals with estrogenic metabolites and may reduce potential false negative results from in vitro assays.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

“…In CoMPARA, in addition to the lists included in CERAPP, we used the European inventory of existing commercial chemical substances (EINECS) containing ∼ 60,000 chemicals as a list of interest for in silico screening. We also incorporated ToxCast™ metabolites in the prediction set that had been generated as part of related ER studies (Leonard et al 2018;Pinto et al 2016). The goal of including metabolites in the CoMPARA project was to understand the effect of xenobiotic metabolism, which is lacking in most in vitro assays.…”

“…The goal of including metabolites in the CoMPARA project was to understand the effect of xenobiotic metabolism, which is lacking in most in vitro assays. For ER screening efforts, this step was conducted post CERAPP in two different studies generating a total of 15,406 metabolite structures for ToxCast™ parent chemicals using ChemAxon Metabolizer (discontinued 2018) (ChemAxon, Ltd.) (Leonard et al 2018;Pinto et al 2016). After QSAR-ready standardization and removal of duplicates, the CoMPARA list consisted of 55,450 QSAR-ready structures with unique CoMPARA integer IDs, including 6,592 nonredundant metabolite structures.…”

“…Collaborators from 25 international research groups (Supplemental Material S1) contributed a total of 91 qualitative and quantitative predictive QSAR models for binding, agonist, and antagonist AR activities. The total list of chemicals that CoMPARA participants screened using their models comprised 55,450 chemical structures, including CERAPP chemicals and ToxCast™-generated metabolites (Leonard et al 2018;Pinto et al 2016). These predictions were evaluated using curated literature data sets and then combined into binding, agonist, and antagonist consensus models.…”

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals

“…In parallel to the efforts in high-throughput toxicity testing, quantitative structure–activity relationship (QSAR) tools for predicting chemical metabolism ( Maltarollo et al. 2015 ; Pinto et al. 2016 ) or for generating categorical predictions, such as classifying cancer and noncancer hazards ( Jolly et al.…”

Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity

“…Thus, a better understanding of the mechanism of ligand recognition by ER α is of paramount importance for safer drug design. Previously, dedicated prediction methods have been addressing the question of whether a molecule is binding or not (Niu et al , 2016; Pinto et al , 2016; Ribay et al , 2016; Mansouri et al , 2016), and traditional structure-activity relationship (QSAR) modeling studies have been also performed with varying success on this nuclear receptor (Waller et al , 1995; Waller, 2004; Asikainen et al , 2004; Zhang et al , 2013; Zhao et al , 2017; Hou et al , 2018).…”