This study developed
a novel classification scheme to assign chemicals
to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme
unifies and extends existing schemes and has, at its heart, direct
reference to molecular initiating events (MIEs) promoting adverse
outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity
(e.g., narcosis), reactive mechanisms (e.g., electrophilicity and
free radical action), and specific mechanisms (e.g., associated with
enzyme inhibition). The scheme is organized at three further levels
of detail beyond broad domains to separate out the mechanistic group,
specific mechanism, and the MIEs responsible. The novelty of this
approach comes from the reference to taxonomic diversity within the
classification, transparency, quality of supporting evidence relating
to MIEs, and that it can be updated readily.
The performance of chemical safety assessment within the domain of environmental toxicology is often impeded by a shortfall of appropriate experimental data describing potential hazards across the many compounds in regular industrial use. In silico schemes for assigning aquatic-relevant modes or mechanisms of toxic action to substances, based solely on consideration of chemical structure, have seen widespread employment�including those of Verhaar, Russom, and later Bauer (MechoA). Recently, development of a further system was reported by Sapounidou, which, in common with MechoA, seeks to ground its classifications in understanding and appreciation of molecular initiating events. Until now, this Sapounidou scheme has not seen implementation as a tool for practical screening use. Accordingly, the primary purpose of this study was to create such a resource�in the form of a computational workflow. This exercise was facilitated through the formulation of 183 structural alerts/rules describing molecular features associated with narcosis, chemical reactivity, and specific mechanisms of action. Output was subsequently compared relative to that of the three aforementioned alternative systems to identify strengths and shortcomings as regards coverage of chemical space.
Receptor-mediated molecular initiating events (MIEs)
and their
relevance in endocrine activity (EA) have been highlighted in literature.
More than 15 receptors have been associated with neurodevelopmental
adversity and metabolic disruption. MIEs describe chemical interactions
with defined biological outcomes, a relationship that could be described
with quantitative structure–activity relationship (QSAR) models.
QSAR uncertainty can be assessed using the conformal prediction (CP)
framework, which provides similarity (i.e., nonconformity) scores
relative to the defined classes per prediction. CP calibration can
indirectly mitigate data imbalance during model development, and the
nonconformity scores serve as intrinsic measures of chemical applicability
domain assessment during screening. The focus of this work was to
propose an in silico predictive strategy for EA.
First, 23 QSAR models for MIEs associated with EA were developed using
high-throughput data for 14 receptors. To handle the data imbalance,
five protocols were compared, and CP provided the most balanced class
definition. Second, the developed QSAR models were applied to a large
data set (∼55,000 chemicals), comprising chemicals representative
of potential risk for human exposure. Using CP, it was possible to
assess the uncertainty of the screening results and identify model
strengths and out of domain chemicals. Last, two clustering methods,
t-distributed stochastic neighbor embedding and Tanimoto similarity,
were used to identify compounds with potential EA using known endocrine
disruptors as reference. The cluster overlap between methods produced
23 chemicals with suspected or demonstrated EA potential. The presented
models could be utilized for first-tier screening and identification
of compounds with potential biological activity across the studied
MIEs.
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