Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review
Pharmaceutics,
Journal Year:
2025,
Volume and Issue:
17(5), P. 551 - 551
Published: April 23, 2025
Computational
intelligence
(CI)
mimics
human
by
expanding
the
capabilities
of
machines
in
data
analysis,
pattern
recognition,
and
making
informed
decisions.
CI
has
shown
promising
contributions
to
advancements
drug
discovery,
formulation,
manufacturing.
Its
ability
analyze
vast
amounts
patient
optimize
formulations
predicting
pharmacokinetic
pharmacodynamic
responses
makes
it
a
very
useful
platform
for
personalized
medicine.
The
integration
with
3D
printing
further
strengthens
this
potential,
as
enables
fabrication
medicines
precise
doses,
controlled-release
profiles,
complex
formulations.
Furthermore,
automated
digital
make
suitable
CI.
proven
material
printability,
optimizing
release
rates,
designing
structures,
ensuring
quality
control,
improving
manufacturing
processes
printing.
In
context
customizing
from
3D-printed
products,
techniques
have
been
applied
predict
input
variables
design
geometries
that
achieve
desired
profile.
This
review
explores
role
It
provides
overview
limitations
printing;
how
can
overcome
these
challenges,
its
potential
release;
comparison
other
methods
optimization;
real-world
examples
Language: Английский
Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical CO2: a machine learning perspective
Frontiers in Medicine,
Journal Year:
2024,
Volume and Issue:
11
Published: Sept. 2, 2024
Inadequate
bioavailability
of
therapeutic
drugs,
which
is
often
the
consequence
their
unacceptable
solubility
and
dissolution
rates,
an
indisputable
operational
challenge
pharmaceutical
companies
due
to
its
detrimental
effect
on
efficacy.
Over
recent
decades,
application
supercritical
fluids
(SCFs)
(mainly
SCCO
2
)
has
attracted
attentions
many
scientists
as
promising
alternative
toxic
environmentally-hazardous
organic
solvents
possessing
positive
advantages
like
low
flammability,
availability,
high
performance,
eco-friendliness
safety/simplicity
operation.
Nowadays,
different
machine
learning
(ML)
a
versatile,
robust
accurate
approach
for
prediction
momentous
parameters
been
great
non-affordability
time-wasting
nature
experimental
investigations.
The
prominent
goal
this
article
review
role
ML-based
tools
solubility/bioavailability
drugs
using
.
Moreover,
importance
factor
in
industry
possible
techniques
increasing
amount
parameter
poorly-soluble
are
comprehensively
discussed.
At
end,
efficiency
improving
manufacturing
process
drug
nanocrystals
aimed
be
Language: Английский