4 research outputs found
Evaluation of environmental chemicals computationally predicted to disrupt angiogenesis
Presented at the Annual Society of Toxicology meetin
In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning
There
are little available toxicity data on the vast majority of
chemicals in commerce. High-throughput screening (HTS) studies, such
as those being carried out by the U.S. Environmental Protection Agency
(EPA) ToxCast program in partnership with the federal Tox21 research
program, can generate biological data to inform models for predicting
potential toxicity. However, physicochemical properties are also needed
to model environmental fate and transport, as well as exposure potential.
The purpose of the present study was to generate an open-source quantitative
structure–property relationship (QSPR) workflow to predict
a variety of physicochemical properties that would have cross-platform
compatibility to integrate into existing cheminformatics workflows.
In this effort, decades-old experimental property data sets available
within the EPA EPI Suite were reanalyzed using modern cheminformatics
workflows to develop updated QSPR models capable of supplying computationally
efficient, open, and transparent HTS property predictions in support
of environmental modeling efforts. Models were built using updated
EPI Suite data sets for the prediction of six physicochemical properties:
octanol–water partition coefficient (logP), water solubility
(logS), boiling point (BP), melting point (MP), vapor pressure (logVP),
and bioconcentration factor (logBCF). The coefficient of determination
(<i>R</i><sup>2</sup>) between the estimated values and
experimental data for the six predicted properties ranged from 0.826
(MP) to 0.965 (BP), with model performance for five of the six properties
exceeding those from the original EPI Suite models. The newly derived
models can be employed for rapid estimation of physicochemical properties
within an open-source HTS workflow to inform fate and toxicity prediction
models of environmental chemicals
Development and Validation of a Computational Model for Androgen Receptor Activity
Testing thousands
of chemicals to identify potential androgen receptor
(AR) agonists or antagonists would cost millions of dollars and take
decades to complete using current validated methods. High-throughput
in vitro screening (HTS) and computational toxicology approaches can
more rapidly and inexpensively identify potential androgen-active
chemicals. We integrated 11 HTS ToxCast/Tox21 in vitro assays into
a computational network model to distinguish true AR pathway activity
from technology-specific assay interference. The in vitro HTS assays
probed perturbations of the AR pathway at multiple points (receptor
binding, coregulator recruitment, gene transcription, and protein
production) and multiple cell types. Confirmatory in vitro antagonist
assay data and cytotoxicity information were used as additional flags
for potential nonspecific activity. Validating such alternative testing
strategies requires high-quality reference data. We compiled 158 putative
androgen-active and -inactive chemicals from a combination of international
test method validation efforts and semiautomated systematic literature
reviews. Detailed in vitro assay information and results were compiled
into a single database using a standardized ontology. Reference chemical
concentrations that activated or inhibited AR pathway activity were
identified to establish a range of potencies with reproducible reference
chemical results. Comparison with existing Tier 1 AR binding data
from the U.S. EPA Endocrine Disruptor Screening Program revealed that
the model identified binders at relevant test concentrations (<100
ÎĽM) and was more sensitive to antagonist activity. The AR pathway
model based on the ToxCast/Tox21 assays had balanced accuracies of
95.2% for agonist (<i>n</i> = 29) and 97.5% for antagonist
(<i>n</i> = 28) reference chemicals. Out of 1855 chemicals
screened in the AR pathway model, 220 chemicals demonstrated AR agonist
or antagonist activity and an additional 174 chemicals were predicted
to have potential weak AR pathway activity
Non-animal methods to predict skin sensitization (II): an assessment of defined approaches<sup>**</sup>
<p>Skin sensitization is a toxicity endpoint of widespread concern, for which the mechanistic understanding and concurrent necessity for non-animal testing approaches have evolved to a critical juncture, with many available options for predicting sensitization without using animals. Cosmetics Europe and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods collaborated to analyze the performance of multiple non-animal data integration approaches for the skin sensitization safety assessment of cosmetics ingredients. The Cosmetics Europe Skin Tolerance Task Force (STTF) collected and generated data on 128 substances in multiple <i>in vitro</i> and <i>in chemico</i> skin sensitization assays selected based on a systematic assessment by the STTF. These assays, together with certain <i>in silico</i> predictions, are key components of various non-animal testing strategies that have been submitted to the Organization for Economic Cooperation and Development as case studies for skin sensitization. Curated murine local lymph node assay (LLNA) and human skin sensitization data were used to evaluate the performance of six defined approaches, comprising eight non-animal testing strategies, for both hazard and potency characterization. Defined approaches examined included consensus methods, artificial neural networks, support vector machine models, Bayesian networks, and decision trees, most of which were reproduced using open source software tools. Multiple non-animal testing strategies incorporating <i>in vitro, in chemico</i>, and <i>in silico</i> inputs demonstrated equivalent or superior performance to the LLNA when compared to both animal and human data for skin sensitization.</p