A workflow for identifying metabolically active chemicals to complement in vitro toxicity screening

Abstract

The new paradigm of toxicity testing approaches involves rapid screening of thousands of chemicals across hundreds of biological targets through use of assays. Such assays may lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system are unable to be replicated in an environment. In the current study, a workflow is presented for complementing testing results with and techniques to identify inactive parents that may produce active metabolites. A case study applying this workflow involved investigating the influence of metabolism for over 1,400 chemicals considered inactive across18 assays related to the estrogen receptor (ER) pathway. Over 7,500 first-generation and second-generation metabolites were generated for these inactive chemicals using an software program. Next, a consensus model comprised of four individual quantitative structure activity relationship (QSAR) models was used to predict ER-binding activity for each of the metabolites. Binding activity was predicted for ~8-10% of metabolites in each generation, with these metabolites linked to 259 inactive parent chemicals. Metabolites were enriched in substructures consisting of alcohol, aromatic, and phenol bonds relative to their inactive parent chemicals, suggesting these features are potentially favorable for ER-binding. The workflow presented here can be used to identify parent chemicals that can be potentially bioactive, to aid confidence in high throughput risk screening.

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.…”

Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements

“…While consideration of the structural features within each cluster coupled with computer-based metabolism predictions can provide some insights regarding the potential for this confounding effect, our work does not specifically address this outstanding issue. There has been considerable progress in recent years both with better in silico predictions ( Pinto et al, 2016 ; Leonard et al, 2018 ; Ring et al, 2021 ) and in vitro approaches ( DeGroot et al, 2018 ; Deisenroth et al, 2020 ; Franzosa et al, 2021 ). As these efforts continue to progress in parallel with efforts such as ours, the robustness of an in vitro testing paradigm should rapidly increase.…”

Developing context appropriate toxicity testing approaches using new alternative methods (NAMs)

“…Currently a variety of tools are available for estimating physicochemical properties and environmental fate endpoints: e.g., EPA's EPI Suite and ECOSAR (USEPA, 2018a), for predicting toxicological endpoints: TIMES (Mekenyan et al, 2004) and Leadscope (Roberts et al, 2000), for metabolites likely formed in vivo: (Leonard et al, 2018), Meteor Nexus (Marchant et al, 2008), BioTransformer (Djoumbou-Feunang et al, 2019, and ADMET Predictor ® , and for both thresholds of toxicological concern (TTC) and possible exposure levels: (Patlewicz et al, 2018). With the collaborative estrogen receptor activity prediction project (CERAPP), large-scale modeling using 32,464 structures showed the possibility of screening large libraries of chemicals using a consensus of different in silico approaches (Mansouri et al, 2016).…”