Advanced Drug Delivery Reviews,
Journal Year:
2023,
Volume and Issue:
204, P. 115138 - 115138
Published: Nov. 18, 2023
Despite
the
enormous
potential
of
nanomedicines
to
shape
future
medicine,
their
clinical
translation
remains
suboptimal.
Translational
challenges
are
present
in
every
step
development
pipeline,
from
a
lack
understanding
patient
heterogeneity
insufficient
insights
on
nanoparticle
properties
and
impact
material-cell
interactions.
Here,
we
discuss
how
adoption
advanced
optical
microscopy
techniques,
such
as
super-resolution
microscopies,
correlative
high-content
modalities,
could
aid
rational
design
nanocarriers,
by
characterizing
cell,
nanomaterial,
interaction
with
unprecedented
spatial
and/or
temporal
detail.
In
this
nanomedicine
arena,
will
implementation
these
versatility
specificity,
can
yield
high
volumes
multi-parametric
data;
machine
learning
rapid
advances
microscopy:
image
acquisition
data
interpretation.
Engineering,
Journal Year:
2023,
Volume and Issue:
27, P. 37 - 69
Published: April 28, 2023
Drug
discovery
and
development
affects
various
aspects
of
human
health
dramatically
impacts
the
pharmaceutical
market.
However,
investments
in
a
new
drug
often
go
unrewarded
due
to
long
complex
process
research
(R&D).
With
advancement
experimental
technology
computer
hardware,
artificial
intelligence
(AI)
has
recently
emerged
as
leading
tool
analyzing
abundant
high-dimensional
data.
Explosive
growth
size
biomedical
data
provides
advantages
applying
AI
all
stages
R&D.
Driven
by
big
biomedicine,
led
revolution
R&D,
its
ability
discover
drugs
more
efficiently
at
lower
cost.
This
review
begins
with
brief
overview
common
models
field
discovery;
then,
it
summarizes
discusses
depth
their
specific
applications
such
target
discovery,
design,
preclinical
research,
automated
synthesis,
influences
Finally,
major
limitations
R&D
are
fully
discussed
possible
solutions
proposed.
Journal of Molecular Liquids,
Journal Year:
2023,
Volume and Issue:
395, P. 123888 - 123888
Published: Dec. 27, 2023
Efficient
drug
delivery
systems
(DDSs)
play
a
pivotal
role
in
ensuring
pharmaceuticals'
targeted
and
effective
administration.
However,
the
intricate
interplay
between
formulations
poses
challenges
their
design
optimization.
Simulations
have
emerged
as
indispensable
tools
for
comprehending
these
interactions
enhancing
DDS
performance
to
address
this
complexity.
This
comprehensive
review
explores
latest
advancements
simulation
techniques
provides
detailed
analysis.
The
encompasses
various
methodologies,
including
molecular
dynamics
(MD),
Monte
Carlo
(MC),
finite
element
analysis
(FEA),
computational
fluid
(CFD),
density
functional
theory
(DFT),
machine
learning
(ML),
dissipative
particle
(DPD).
These
are
critically
examined
context
of
research.
article
presents
illustrative
case
studies
involving
liposomal,
polymer-based,
nano-particulate,
implantable
DDSs,
demonstrating
influential
simulations
optimizing
systems.
Furthermore,
addresses
advantages
limitations
It
also
identifies
future
directions
research
development,
such
integrating
multiple
techniques,
refining
validating
models
greater
accuracy,
overcoming
limitations,
exploring
applications
personalized
medicine
innovative
DDSs.
employing
like
MD,
MC,
FEA,
CFD,
DFT,
ML,
DPD
offer
crucial
insights
into
behaviour,
aiding
Despite
advantages,
rapid
cost-effective
screening,
require
validation
addressing
limitations.
Future
should
focus
on
models,
enhance
outcomes.
paper
underscores
contribution
emphasizing
providing
valuable
facilitating
development
optimization
ultimately
patient
As
we
continue
explore
impact
advancing
discovery
improving
DDSs
is
expected
be
profound.
Journal of Controlled Release,
Journal Year:
2024,
Volume and Issue:
373, P. 23 - 30
Published: June 27, 2024
For
decades,
drug
delivery
scientists
have
been
performing
trial-and-error
experimentation
to
manually
sample
parameter
spaces
and
optimize
release
profiles
through
rational
design.
To
enable
this
approach,
spend
much
of
their
career
learning
nuanced
drug-material
interactions
that
drive
system
behavior.
In
relatively
simple
systems,
design
criteria
allow
us
fine
tune
efficacious
therapies.
However,
as
materials
drugs
become
increasingly
sophisticated
non-linear
compounding
effects,
the
field
is
suffering
Curse
Dimensionality
which
prevents
from
comprehending
complex
structure-function
relationships.
past,
we
embraced
complexity
by
implementing
high-throughput
screens
increase
probability
finding
ideal
compositions.
brute
force
method
was
inefficient
led
many
abandon
these
fishing
expeditions.
Fortunately,
methods
in
data
science
including
artificial
intelligence
/
machine
(AI/ML)
are
providing
analytical
tools
model
ascertain
quantitative
Oration,
I
speak
potential
value
with
particular
focus
on
polymeric
systems.
Here,
do
not
suggest
AI/ML
will
simply
replace
mechanistic
understanding
Rather,
propose
should
be
yet
another
useful
tool
lab
navigate
spaces.
The
recent
hype
around
breathtaking
potentially
over
inflated,
but
poised
revolutionize
how
perform
science.
Therefore,
encourage
readers
consider
adopting
skills
applying
own
problems.
If
done
successfully,
believe
all
realize
a
paradigm
shift
our
approach
delivery.
Asian Journal of Pharmaceutical and Clinical Research,
Journal Year:
2025,
Volume and Issue:
unknown, P. 8 - 26
Published: Feb. 7, 2025
Hepatocellular
carcinoma
(HCC)
represents
a
significant
threat
to
global
health
and
is
responsible
for
mortality
rates
worldwide.
Conventional
treatment
options
such
as
surgery
chemotherapy
have
inherent
limitations.
In
order
remedy
these
deficits,
the
development
of
novel
therapeutic
strategies
essential.
Nanomedicines
shown
promise
in
HCC
they
offer
improved
stability,
controlled
release,
increased
drug
loading
capacity.
This
review
explores
application
nanoconstructs
treatment,
including
active
passive
targeting
strategies.
addition,
liver
cell
approaches,
moieties,
conjugation
chemistry
surface
functionalization
are
investigated.
A
compact
overview
various
approaches
also
given.
Nano Convergence,
Journal Year:
2023,
Volume and Issue:
10(1)
Published: Aug. 7, 2023
Abstract
Cancer
immunotherapy,
which
harnesses
the
power
of
immune
system,
has
shown
immense
promise
in
fight
against
malignancies.
Messenger
RNA
(mRNA)
stands
as
a
versatile
instrument
this
context,
with
its
capacity
to
encode
tumor-associated
antigens
(TAAs),
cell
receptors,
cytokines,
and
antibodies.
Nevertheless,
inherent
structural
instability
mRNA
requires
development
effective
delivery
systems.
Lipid
nanoparticles
(LNPs)
have
emerged
significant
candidates
for
cancer
providing
both
protection
enhanced
intracellular
efficiency.
In
review,
we
offer
comprehensive
summary
recent
advancements
LNP-based
systems,
focus
on
strategies
optimizing
design
mRNA-encoded
therapeutics
treatment.
Furthermore,
delve
into
challenges
encountered
field
contemplate
future
perspectives,
aiming
improve
safety
efficacy
immunotherapies.
Graphical
Materials Today Bio,
Journal Year:
2023,
Volume and Issue:
20, P. 100672 - 100672
Published: May 18, 2023
Over
the
past
three
decades,
nanoscience
has
offered
a
unique
solution
for
reducing
systemic
toxicity
of
chemotherapy
drugs
and
increasing
drug
therapeutic
efficiency.
However,
poor
accumulation
pharmacokinetics
nanoparticles
are
some
key
reasons
their
slow
translation
into
clinic.
The
is
intimately
linked
to
non-biological
nature
aberrant
features
solid
cancer,
which
together
significantly
compromise
nanoparticle
delivery.
New
findings
on
properties
tumors
interactions
with
human
body
suggest
that,
contrary
what
was
long-believed,
tumor
may
be
more
mirage
than
miracle,
as
enhanced
permeability
retention
based
efficacy
estimated
low
1%.
In
this
review,
we
highlight
current
barriers
available
solutions
pave
way
approved
nanoformulations.
Furthermore,
aim
discuss
main
solve
inefficient
delivery
use
nanobioengineering
nanocarriers
environment.
Finally,
will
suggested
strategies
overcome
two
or
biological
one
nanocarrier.
variety
design
formats,
applications
implications
each
these
methods
also
evaluated.
