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
Journal of drug targeting,
Journal Year:
2024,
Volume and Issue:
32(10), P. 1247 - 1266
Published: Aug. 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.
Frontiers in Immunology,
Journal Year:
2024,
Volume and Issue:
15
Published: Nov. 8, 2024
Immunotherapy
has
ushered
in
a
new
era
of
cancer
treatment,
yet
remains
leading
cause
global
mortality.
Among
various
therapeutic
strategies,
vaccines
have
shown
promise
by
activating
the
immune
system
to
specifically
target
cells.
While
current
are
primarily
prophylactic,
advancements
targeting
tumor-associated
antigens
(TAAs)
and
neoantigens
paved
way
for
vaccines.
The
integration
artificial
intelligence
(AI)
into
vaccine
development
is
revolutionizing
field
enhancing
aspect
design
delivery.
This
review
explores
how
AI
facilitates
precise
epitope
design,
optimizes
mRNA
DNA
instructions,
enables
personalized
strategies
predicting
patient
responses.
By
utilizing
technologies,
researchers
can
navigate
complex
biological
datasets
uncover
novel
targets,
thereby
improving
precision
efficacy
Despite
AI-powered
vaccines,
significant
challenges
remain,
such
as
tumor
heterogeneity
genetic
variability,
which
limit
effectiveness
neoantigen
prediction.
Moreover,
ethical
regulatory
concerns
surrounding
data
privacy
algorithmic
bias
must
be
addressed
ensure
responsible
deployment.
future
lies
seamless
create
immunotherapies
that
offer
targeted
effective
treatments.
underscores
importance
interdisciplinary
collaboration
innovation
overcoming
these
advancing
development.
Expert Opinion on Drug Delivery,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 8, 2024
Applying
artificial
intelligence
(AI)
to
nanomedicine
has
greatly
increased
the
production
of
specially
engineered
nanoscale
materials
for
tailored
medicine,
marking
a
significant
advancement
in
healthcare.
With
use
AI,
researchers
can
search
through
massive
databases
and
find
nano-properties
that
support
range
therapeutic
objectives,
eventually
producing
safer,
customized
nanomaterials.
AI
analyzes
patient
data,
including
clinical
genetic
information,
predict
results
individualized
care
makes
recommendations
therapy
improvement.
Furthermore,
logically
creates
nanocarriers
give
precise
controlled
drug
release
patterns
optimize
advantages
minimize
undesirable
side
effects.
Even
though
lot
potential
nanomedicine,
there
are
still
issues
data
integration
techniques,
moral
dilemmas,
requirement
governmental
backing.
Future
developments
tools
multidisciplinary
cooperation
between
scientists
with
expertise
biological
sciences
nanoengineering
essential
nanomedicine.
Together,
these
disciplines
propel
advancements
precision
contributing
ultimate
objective—a
future
which
combine
provide
really
The
authors
this
editorial
encourage
call
on
scientists,
physicians,
legislators
acknowledge
its
transform
treatment.
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
