Predictive Tox-21 Methods for Assessing Emerging Pollutants in the Marine Environment
Published: Jan. 1, 2025
Language: Английский
A combination of high-throughput in vitro and in silico new approach methods (NAMs) for ecotoxicology hazard assessment for fish
Environmental Toxicology and Chemistry,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 6, 2025
Fish
acute
toxicity
testing
is
used
to
inform
environmental
hazard
assessment
of
chemicals.
In
silico
and
in
vitro
approaches
have
the
potential
reduce
number
fish
increase
efficiency
generating
data
for
assessing
ecological
hazards.
Here,
two
bioactivity
assays
were
adapted
use
high-throughput
chemical
screening.
First,
a
miniaturized
version
Organisation
Economic
Co-operation
Development
(OECD)
test
guideline
249
plate
reader-based
assay
RTgill-W1
cells
was
developed.
Second,
Cell
Painting
(CP)
along
with
an
imaging-based
cell
viability
assay.
Then,
225
chemicals
tested
each
Potencies
calls
from
reader
comparable.
The
CP
more
sensitive
than
either
that
it
detected
larger
as
bioactive,
phenotype
altering
concentrations
(PACs)
lower
decreased
viability.
An
disposition
(IVD)
model
accounted
sorption
plastic
over
time
applied
predict
freely
dissolved
PACs
compared
vivo
data.
Adjustment
using
IVD
modeling
improved
concordance
For
65
where
comparison
values
possible,
59%
adjusted
within
one
order
magnitude
lethal
50%
organisms.
protective
73%
This
combination
has
or
replace
testing.
Language: Английский
Extrapolation factors for calculating ecotoxicity effects in LCA
The International Journal of Life Cycle Assessment,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 10, 2024
Abstract
Purpose
This
study
focuses
on
updating,
improving,
and
expanding
the
extrapolation
factors
needed
to
convert
various
acute
or
chronic
effect
concentration
indicators
into
consistent
EC10eq
(effect
inducing
a
10%
response
over
background)
for
use
in
life
cycle
assessment
(LCA).
Our
main
objectives
include
(1)
present
detailed
approach
harmonization
of
ecotoxicity
data,
with
focus
deriving
factors,
(2)
estimate
both
generic
species
group-specific
facilitating
conversion
indicator
groups
(EC10eq
EC50eq)
EC10eq.
Methods
Experimental
data
were
sourced
from
CompTox
Version
2.1.1,
which
integrates
toxicity
information
ToxValDB
v9.1.1,
REACH
registration
dossiers.
We
developed
framework
harmonizing
ensuring
uniformity
high
quality
aquatic
these
sources.
Through
linear
regression
analysis,
then
derived.
Results
discussion
Harmonization
yielded
streamlined
dataset
339,729
datapoints
10,668
chemicals,
reflecting
54%
reduction
raw
datapoints.
The
geometric
mean-based
aggregation
process
produced
79,001
aggregated
at
level,
41,303
group
23,215
level
chemicals.
facilitated
derivation
3
24
allowing
across
two
exposure
classes
(acute
vs.
chronic)
groups,
as
defined
US
EPA
ECOTOX
knowledgebase,
including
algae,
amphibians,
fish,
crustaceans,
insects/spiders,
invertebrates,
molluscs,
worms.
Conclusions
derived
permit
integration
diverse
varying
durations
USEtox
characterization
factors.
has
potential
enhance
substance
coverage
characterizing
effects
chemicals
LCA
frameworks
by
permitting
wider
coverage.
More
generally,
this
is
part
global
efforts
extend
quantitative
environmental
impacts
an
framework.
Language: Английский
AI in Predictive Toxicology
Advances in medical technologies and clinical practice book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 79 - 134
Published: Sept. 14, 2024
The
field
of
toxicology
is
undergoing
a
significant
transformation
due
to
the
integration
artificial
intelligence
(AI).
In
addition
traditional
reliance
on
empirical
studies
and
animal
testing,
AI-powered
predictive
now
used
predict
toxic
effects
chemicals
drugs.
This
chapter
examines
role
AI
in
enhancing
accuracy,
efficiency,
breadth
toxicological
assessments
by
bridging
gap
between
approaches
advanced
techniques.
It
explores
various
methodologies,
such
as
machine
learning,
deep
neural
networks,
focusing
their
application
toxicity
prediction.
Furthermore,
this
investigates
with
databases
development
validation
models.
also
addresses
challenges
associated
toxicology,
including
data
quality,
model
interpretability,
scalability.
concludes
that
despite
facing
challenges,
powerful
tool
modern
analysis.
Language: Английский