ACS Pharmacology & Translational Science,
Год журнала:
2024,
Номер
7(8), С. 2251 - 2279
Опубликована: Июль 12, 2024
Nanoparticles
(NPs)
have
been
widely
used
to
improve
the
pharmacokinetic
properties
and
tissue
distribution
of
small
molecules
such
as
targeting
a
specific
interest,
enhancing
their
systemic
circulation,
enlarging
therapeutic
properties.
NPs
unique
complicated
in
vivo
disposition
compared
molecule
drugs
due
complex
multifunctionality.
Physiologically
based
(PBPK)
modeling
has
powerful
tool
simulation
absorption,
distribution,
metabolism,
elimination
(ADME)
characteristics
materials,
it
can
be
characterization
prediction
disposition,
toxicity,
efficacy,
target
exposure
various
types
nanoparticles.
In
this
review,
recent
advances
PBPK
model
applications
related
nanoparticles
with
properties,
dispositional
features
biological
systems,
ADME
characteristics,
description
transport
processes
model,
challenges
development
are
delineated
juxtaposed
those
encountered
models.
Nanoparticle
related,
non-nanoparticle-related,
interspecies-scaling
methods
applied
reviewed.
vitro
extrapolation
(IVIVE)
being
promising
computational
provide
predictions
from
results
silico
studies
discussed.
Finally,
advancement
ML/AI-based
approaches
estimation
parameters
(PK)
analysis
introduced.
ACS Nano,
Год журнала:
2023,
Номер
17(20), С. 19810 - 19831
Опубликована: Окт. 9, 2023
Low
tumor
delivery
efficiency
is
a
critical
barrier
in
cancer
nanomedicine.
This
study
reports
an
updated
version
of
“Nano-Tumor
Database”,
which
increases
the
number
time-dependent
concentration
data
sets
for
different
nanoparticles
(NPs)
tumors
from
previous
376
with
1732
points
200
studies
to
current
534
2345
297
published
2005
2021.
Additionally,
database
includes
1972
five
major
organs
(i.e.,
liver,
spleen,
lung,
heart,
and
kidney)
total
8461
points.
Tumor
organ
distribution
are
calculated
using
three
pharmacokinetic
parameters,
including
efficiency,
maximum
concentration,
coefficient.
The
median
0.67%
injected
dose
(ID),
low
but
consistent
studies.
Employing
best
regression
model
we
generate
hypothetical
scenarios
combinations
NP
factors
that
may
lead
higher
>3%ID,
requires
further
experimentation
confirm.
In
healthy
organs,
highest
accumulation
liver
(10.69%ID/g),
followed
by
spleen
6.93%ID/g
kidney
3.22%ID/g.
Our
perspective
on
how
facilitate
design
clinical
translation
presented.
substantially
expanded
Database”
several
statistical
models
help
nanomedicine
future.
Journal of drug targeting,
Год журнала:
2024,
Номер
32(10), С. 1247 - 1266
Опубликована: Авг. 19, 2024
Nano-based
drug
delivery
systems
(DDSs)
have
demonstrated
the
ability
to
address
challenges
posed
by
therapeutic
agents,
enhancing
efficiency
and
reducing
side
effects.
Various
nanoparticles
(NPs)
are
utilised
as
DDSs
with
unique
characteristics,
leading
diverse
applications
across
different
diseases.
However,
complexity,
cost
time-consuming
nature
of
laboratory
processes,
large
volume
data,
in
data
analysis
prompted
integration
artificial
intelligence
(AI)
tools.
AI
has
been
employed
designing,
characterising
manufacturing
nanosystems,
well
predicting
treatment
efficiency.
AI's
potential
personalise
based
on
individual
patient
factors,
optimise
formulation
design
predict
properties
highlighted.
By
leveraging
datasets,
developing
safe
effective
can
be
accelerated,
ultimately
improving
outcomes
advancing
pharmaceutical
sciences.
This
review
article
investigates
role
development
nano-DDSs,
a
focus
their
applications.
The
use
revolutionise
optimisation
improve
care.
Nanomaterials,
Год журнала:
2024,
Номер
14(2), С. 155 - 155
Опубликована: Янв. 10, 2024
Although
engineered
nanomaterials
(ENMs)
have
tremendous
potential
to
generate
technological
benefits
in
numerous
sectors,
uncertainty
on
the
risks
of
ENMs
for
human
health
and
environment
may
impede
advancement
novel
materials.
Traditionally,
can
be
evaluated
by
experimental
methods
such
as
environmental
field
monitoring
animal-based
toxicity
testing.
However,
it
is
time-consuming,
expensive,
impractical
evaluate
risk
increasingly
large
number
with
methods.
On
contrary,
artificial
intelligence
machine
learning,
silico
recently
received
more
attention
assessment
ENMs.
This
review
discusses
key
progress
computational
nanotoxicology
models
assessing
ENMs,
including
material
flow
analysis
models,
multimedia
physiologically
based
toxicokinetics
quantitative
nanostructure-activity
relationships,
meta-analysis.
Several
challenges
are
identified
a
perspective
provided
regarding
how
addressed.
Journal of Controlled Release,
Год журнала:
2024,
Номер
374, С. 219 - 229
Опубликована: Авг. 16, 2024
Nanoparticles
(NPs)
can
be
designed
for
targeted
delivery
in
cancer
nanomedicine,
but
the
challenge
is
a
low
efficiency
(DE)
to
tumor
site.
Understanding
impact
of
NPs'
physicochemical
properties
on
target
tissue
distribution
and
DE
help
improve
design
nanomedicines.
Multiple
machine
learning
artificial
intelligence
models,
including
linear
regression,
support
vector
machine,
random
forest,
gradient
boosting,
deep
neural
networks
(DNN),
were
trained
validated
predict
based
therapeutic
strategies
with
dataset
from
Nano-Tumor
Database.
Compared
other
DNN
model
had
superior
predictions
tumors
major
tissues.
The
determination
coefficients
(R
Nano TransMed,
Год журнала:
2024,
Номер
3, С. 100041 - 100041
Опубликована: Июль 9, 2024
Artificial
Intelligence
(AI)
and
Nanotechnology
are
two
cutting-edge
fields
that
hold
immense
promise
for
revolutionizing
various
aspects
of
science,
technology,
everyday
life.
This
review
delves
into
the
intersection
these
disciplines,
highlighting
synergistic
relationship
between
AI
Nanotechnology.
It
explores
how
techniques
such
as
machine
learning,
deep
neural
networks
being
employed
to
enhance
efficiency,
precision,
scalability
nanotechnology
applications.
Furthermore,
it
discusses
challenges,
opportunities,
future
prospects
integrating
with
nanotechnology,
paving
way
transformative
advancements
in
diverse
domains
ranging
from
healthcare
materials
science
environmental
sustainability
beyond.
Chemical Society Reviews,
Год журнала:
2024,
Номер
53(18), С. 9059 - 9132
Опубликована: Янв. 1, 2024
Nanodrugs,
which
utilise
nanomaterials
in
disease
prevention
and
therapy,
have
attracted
considerable
interest
since
their
initial
conceptualisation
the
1990s.
Substantial
efforts
been
made
to
develop
nanodrugs
for
overcoming
limitations
of
conventional
drugs,
such
as
low
targeting
efficacy,
high
dosage
toxicity,
potential
drug
resistance.
Despite
significant
progress
that
has
nanodrug
discovery,
precise
design
or
screening
with
desired
biomedical
functions
prior
experimentation
remains
a
challenge.
This
is
particularly
case
regard
personalised
precision
nanodrugs,
require
simultaneous
optimisation
structures,
compositions,
surface
functionalities
nanodrugs.
The
development
powerful
computer
clusters
algorithms
it
possible
overcome
this
challenge
through
