Journal of Controlled Release,
Journal Year:
2024,
Volume and Issue:
374, P. 219 - 229
Published: Aug. 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
Pharmaceutics,
Journal Year:
2023,
Volume and Issue:
15(7), P. 1916 - 1916
Published: July 10, 2023
Artificial
intelligence
(AI)
has
emerged
as
a
powerful
tool
that
harnesses
anthropomorphic
knowledge
and
provides
expedited
solutions
to
complex
challenges.
Remarkable
advancements
in
AI
technology
machine
learning
present
transformative
opportunity
the
drug
discovery,
formulation,
testing
of
pharmaceutical
dosage
forms.
By
utilizing
algorithms
analyze
extensive
biological
data,
including
genomics
proteomics,
researchers
can
identify
disease-associated
targets
predict
their
interactions
with
potential
candidates.
This
enables
more
efficient
targeted
approach
thereby
increasing
likelihood
successful
approvals.
Furthermore,
contribute
reducing
development
costs
by
optimizing
research
processes.
Machine
assist
experimental
design
pharmacokinetics
toxicity
capability
prioritization
optimization
lead
compounds,
need
for
costly
animal
testing.
Personalized
medicine
approaches
be
facilitated
through
real-world
patient
leading
effective
treatment
outcomes
improved
adherence.
comprehensive
review
explores
wide-ranging
applications
delivery
form
designs,
process
optimization,
testing,
pharmacokinetics/pharmacodynamics
(PK/PD)
studies.
an
overview
various
AI-based
utilized
technology,
highlighting
benefits
drawbacks.
Nevertheless,
continued
investment
exploration
industry
offer
exciting
prospects
enhancing
processes
care.
Signal Transduction and Targeted Therapy,
Journal Year:
2023,
Volume and Issue:
8(1)
Published: Aug. 7, 2023
Cancer
remains
a
highly
lethal
disease
in
the
world.
Currently,
either
conventional
cancer
therapies
or
modern
immunotherapies
are
non-tumor-targeted
therapeutic
approaches
that
cannot
accurately
distinguish
malignant
cells
from
healthy
ones,
giving
rise
to
multiple
undesired
side
effects.
Recent
advances
nanotechnology,
accompanied
by
our
growing
understanding
of
biology
and
nano-bio
interactions,
have
led
development
series
nanocarriers,
which
aim
improve
efficacy
while
reducing
off-target
toxicity
encapsulated
anticancer
agents
through
tumor
tissue-,
cell-,
organelle-specific
targeting.
However,
vast
majority
nanocarriers
do
not
possess
hierarchical
targeting
capability,
their
indices
often
compromised
poor
accumulation,
inefficient
cellular
internalization,
inaccurate
subcellular
localization.
This
Review
outlines
current
prospective
strategies
design
organelle-targeted
nanomedicines,
highlights
latest
progress
technologies
can
dynamically
integrate
these
three
different
stages
static
maximize
outcomes.
Finally,
we
briefly
discuss
challenges
future
opportunities
for
clinical
translation
nanomedicines.
Toxicological Sciences,
Journal Year:
2022,
Volume and Issue:
189(1), P. 7 - 19
Published: July 21, 2022
Abstract
Machine
learning
and
artificial
intelligence
approaches
have
revolutionized
multiple
disciplines,
including
toxicology.
This
review
summarizes
representative
recent
applications
of
machine
in
different
areas
toxicology,
physiologically
based
pharmacokinetic
(PBPK)
modeling,
quantitative
structure-activity
relationship
modeling
for
toxicity
prediction,
adverse
outcome
pathway
analysis,
high-throughput
screening,
toxicogenomics,
big
data,
toxicological
databases.
By
leveraging
approaches,
now
it
is
possible
to
develop
PBPK
models
hundreds
chemicals
efficiently,
create
silico
predict
a
large
number
with
similar
accuracies
compared
vivo
animal
experiments,
analyze
amount
types
data
(toxicogenomics,
high-content
image
etc.)
generate
new
insights
into
mechanisms
rapidly,
which
was
impossible
by
manual
the
past.
To
continue
advancing
field
sciences,
several
challenges
should
be
considered:
(1)
not
all
are
equally
useful
particular
type
toxicology
thus
important
test
methods
determine
optimal
approach;
(2)
current
prediction
mainly
on
bioactivity
classification
(yes/no),
so
additional
studies
needed
intensity
effect
or
dose-response
relationship;
(3)
as
more
become
available,
crucial
perform
rigorous
quality
check
infrastructure
store,
share,
analyze,
evaluate,
manage
data;
(4)
convert
user-friendly
interfaces
facilitate
their
both
computational
bench
scientists.
BME Frontiers,
Journal Year:
2023,
Volume and Issue:
4
Published: Jan. 1, 2023
The
effective
treatment
of
patients
with
cancer
hinges
on
the
delivery
therapeutics
to
a
tumor
site.
Nanoparticles
provide
an
essential
transport
system.
We
present
5
principles
consider
when
designing
nanoparticles
for
targeting:
(a)
acquire
biological
identity
in
vivo,
(b)
organs
compete
circulation,
(c)
must
enter
solid
tumors
target
components,
(d)
navigate
microenvironment
cellular
or
organelle
targeting,
and
(e)
size,
shape,
surface
chemistry,
other
physicochemical
properties
influence
their
process
target.
This
review
article
describes
these
application
engineering
nanoparticle
systems
carry
disease
targets.
Journal of Controlled Release,
Journal Year:
2023,
Volume and Issue:
361, P. 53 - 63
Published: July 31, 2023
The
critical
barrier
for
clinical
translation
of
cancer
nanomedicine
stems
from
the
inefficient
delivery
nanoparticles
(NPs)
to
target
solid
tumors.
Rapid
growth
computational
power,
new
machine
learning
and
artificial
intelligence
(AI)
approaches
provide
tools
address
this
challenge.
In
study,
we
established
an
AI-assisted
physiologically
based
pharmacokinetic
(PBPK)
model
by
integrating
AI-based
quantitative
structure-activity
relationship
(QSAR)
with
a
PBPK
simulate
tumor-targeted
efficiency
(DE)
biodistribution
various
NPs.
QSAR
was
developed
using
deep
neural
network
algorithms
that
were
trained
datasets
published
"Nano-Tumor
Database"
predict
input
parameters
model.
optimized
NP
cellular
uptake
kinetic
used
maximum
(DEmax)
DE
at
24
(DE24)
168
h
(DE168)
different
NPs
in
tumor
after
intravenous
injection
achieved
determination
coefficient
R2
=
0.83
[root
mean
squared
error
(RMSE)
3.01]
DE24,
0.56
(RMSE
2.27)
DE168,
0.82
3.51)
DEmax.
AI-PBPK
predictions
correlated
well
available
experimentally-measured
profiles
tumors
(R2
≥
0.70
133
out
288
datasets).
This
provides
efficient
screening
tool
rapidly
on
its
physicochemical
properties
without
relying
animal
training
dataset.
Cancer Nanotechnology,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: Jan. 31, 2024
Abstract
Owing
to
unique
facets,
such
as
large
surface
area,
tunable
synthesis
parameters,
and
ease
of
functionalization,
mesoporous
silica
nanoparticles
(MSNs)
have
transpired
a
worthwhile
platform
for
cancer
theranostics
over
the
last
decade.
The
full
potential
MSNs
in
theranostics,
however,
is
yet
be
realized.
While
can
employed
targeted
drug
delivery
imaging,
their
effectiveness
frequently
hindered
by
factors,
biological
barriers,
complex
tumor
microenvironment,
target
non-specificity
ineffectiveness
individual
functionalized
moieties.
primary
purpose
this
review
highlight
technological
advances
tumor-specific,
stimuli-responsive
“smart”
multimodal
MSN-based
hybrid
nanoplatforms
that
overcome
these
limitations
improve
MSN
theranostics.
This
article
offers
an
extensive
overview
technology
outlining
key
directions
future
research
well
challenges
are
involved
aspect.
We
aim
underline
vitality
relevance
current
advancements
field
potentially
enhance
clinical
outcomes
through
provision
more
precise
focused
theranostic
approaches.
Journal of Drug Delivery Science and Technology,
Journal Year:
2024,
Volume and Issue:
95, P. 105599 - 105599
Published: March 26, 2024
Despite
considerable
progress
in
patient
care,
the
global
incidence
of
various
cancer
types
continues
to
rise.
Developing
safer
and
more
efficient
anti-cancer
treatment
approaches
are
great
interest.
In
recent
decades,
nanotechnology
has
emerged
as
a
promising
innovative
medical
approach
for
diagnosis
treatment.
However,
nanomedicine
advances,
it
is
important
understand
address
challenges.
Herein,
we
identify
gaps
current
understanding
effectiveness
on
clinical
outcomes
provide
an
outlook
improved
application
medicine.
We
discuss
use
different
nanoparticles
therapy
impact
efficiency
existing
treatments,
such
chemotherapeutic,
anti-angiogenic,
immunotherapeutic
drugs,
radiotherapy.
Additionally,
update
status
trials
nanoparticle-based
treatments
provided.
