Advances in the design and delivery of RNA vaccines for infectious diseases
Advanced Drug Delivery Reviews,
Год журнала:
2024,
Номер
213, С. 115419 - 115419
Опубликована: Авг. 5, 2024
RNA
medicines
represent
a
paradigm
shift
in
treatment
and
prevention
of
critical
diseases
global
significance,
e.g.,
infectious
diseases.
The
highly
successful
messenger
(mRNA)
vaccines
against
severe
acute
respiratory
syndrome
coronavirus
2
(SARS-CoV-2)
were
developed
at
record
speed
during
the
disease
2019
pandemic.
A
consequence
this
is
exceptionally
shortened
vaccine
development
times,
which
combination
with
adaptability
makes
technology
attractive
for
pandemic
preparedness.
Here,
we
review
state
art
design
delivery
based
on
different
modalities,
including
linear
mRNA,
self-amplifying
RNA,
trans-amplifying
circular
RNA.
We
provide
an
overview
clinical
pipeline
diseases,
present
analytical
procedures,
are
paramount
characterizing
quality
attributes
guaranteeing
their
quality,
discuss
future
perspectives
using
to
combat
pathogens
beyond
SARS-CoV-2.
Язык: Английский
Optimization of recombinant antibody fragment production via machine learning models: Model development and validation
Process Safety and Environmental Protection,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 1, 2025
Язык: Английский
Synergistic Integration of Digital Twins and Neural Networks for Advancing Optimization in the Construction Industry: A Comprehensive Review
Construction Materials and Products,
Год журнала:
2024,
Номер
7(4), С. 7 - 7
Опубликована: Авг. 9, 2024
The
object
of
research
is
the
potential
application
digital
twins
and
neural
network
modeling
for
optimizing
construction
processes.
Method.
Adopting
a
perspective
approach,
conducts
an
extensive
review
existing
literature
delineates
theoretical
framework
integrating
technologies.
Insights
from
inform
development
methodologies,
while
case
studies
practical
applications
are
explored
to
deepen
understanding
these
integrated
approaches
system
optimization.
Results.
yields
following
key
findings:
Digital
Twins:
Offer
capability
create
high-fidelity
virtual
representations
physical
systems,
enabling
real-time
data
collection,
analysis,
visualization
throughout
project
lifecycle.
This
allows
proactive
decision-making,
improved
constructability
enhanced
coordination
between
design
field
operations.
Neural
Network
Modeling:
Possesses
power
learn
complex
relationships
vast
datasets,
predictive
optimization
behavior.
networks
can
be
employed
forecast
timelines,
identify
risks,
optimize
scheduling
resource
allocation.
Integration
Twins
Networks:
Presents
transformative
avenue
processes
by
facilitating
data-driven
design,
maintenance
equipment
infrastructure,
performance
monitoring.
synergistic
approach
lead
significant
improvements
in
efficiency,
reduced
costs,
overall
quality.
Язык: Английский
‘Applications of machine learning in liposomal formulation and development’
Pharmaceutical Development and Technology,
Год журнала:
2025,
Номер
30(1), С. 126 - 136
Опубликована: Янв. 2, 2025
Machine
learning
(ML)
has
emerged
as
a
transformative
tool
in
drug
delivery,
particularly
the
design
and
optimization
of
liposomal
formulations.
This
review
focuses
on
intersection
ML
technology,
highlighting
how
advanced
algorithms
are
accelerating
formulation
processes,
predicting
key
parameters,
enabling
personalized
therapies.
ML-driven
approaches
restructuring
development
by
optimizing
liposome
size,
stability,
encapsulation
efficiency
while
refining
release
profiles.
Additionally,
integration
enhances
therapeutic
outcomes
precision-targeted
delivery
minimizing
side
effects.
presents
current
breakthroughs,
challenges,
future
opportunities
applying
to
systems,
aiming
improve
efficacy
patient
various
disease
treatments.
Язык: Английский
In‐line prediction of viability and viable cell density through machine learning‐based soft sensor modeling and an integrated systems approach: An industrially relevant PAT case study
Shivesh K. Suman,
Michaela Murr,
J. E. Crowe
и другие.
Biotechnology Progress,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 23, 2025
Abstract
The
biopharmaceutical
industry
is
shifting
toward
employing
digital
analytical
tools
for
improved
understanding
of
systems
biology
data
and
production
quality
products.
implementation
these
technologies
can
streamline
the
manufacturing
process
by
enabling
faster
responses,
reducing
manual
measurements,
building
continuous
automated
capabilities.
This
study
discusses
use
soft
sensor
models
prediction
viability
viable
cell
density
(VCD)
in
CHO
culture
processes
using
in‐line
optical
permittivity
sensors.
A
significant
innovation
this
development
a
simplified
empirical
model
adoption
an
integrated
approach
prediction.
initial
evaluation
demonstrated
promising
accuracy
with
96%
residuals
within
±5%
error
limit
Final
Day
mean
absolute
percentage
≤5%
across
various
scales
conditions.
was
VCD
utilizing
Gaussian
Process
Regressor
Matern
Kernel
(nu
=
0.5),
selected
from
over
hundred
advanced
machine
learning
techniques.
had
R
2
0.92
89%
predictions
±10%
significantly
outperformed
commonly
used
partial
least
squares
regression
models.
results
validated
real‐time
highlighted
potential
to
substantially
reduce
reliance
on
labor‐intensive
discrete
offline
measurements.
integration
innovative
aligns
regulatory
guidelines
establishes
foundation
further
advancements
biomanufacturing
industry,
control,
efficiency,
compliance
standards.
Язык: Английский
A Novel Paradigm on Data and Knowledge-Driven Drug Formulation Development: Opportunities and Challenges of Machine Learning
Journal of Industrial Information Integration,
Год журнала:
2025,
Номер
unknown, С. 100796 - 100796
Опубликована: Фев. 1, 2025
Язык: Английский
Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review
Pharmaceutics,
Год журнала:
2025,
Номер
17(5), С. 551 - 551
Опубликована: Апрель 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
Язык: Английский
Digitally Enabled Generic Analytical Framework Accelerating the Pace of Liquid Chromatography Method Development for Vaccine Adjuvant Formulations
Опубликована: Апрель 15, 2024
The
growing
use
of
adjuvants
in
the
fast-paced
formulation
new
vaccines
has
created
an
unprecedented
need
for
meaningful
analytical
assays
that
deliver
reliable
quantitative
data
from
complex
adjuvant
and
adjuvant-antigen
mixtures.
Due
to
their
chemical
physical
properties,
method
development
separation
vaccine
is
considered
a
highly
challenging
laborious
task.
Reversed-phase
liquid
chromatography
(RPLC)
among
most
important
tests
(bio)pharmaceutical
industry
release
stability
indicating
measurements
including
content,
identity,
purity
profile.
However,
time
constraints
developing
“on-demand”
robust
methods
prior
each
change
can
easily
lead
sample
analysis
becoming
bottleneck
development.
Herein
simple
efficient
generic
framework
capable
chromatographically
resolving
commonly
used
non-aluminum
based
across
academic
industrial
sectors
introduced.
This
was
designed
seek
more
proactive
approach
assay
endeavors
evolved
extensive
stationary
phase
screening
conjunction
with
multifactorial
silico
simulations
retention
(RT)
as
function
gradient
time,
temperature,
organic
modifier
blending,
buffer
concentration.
models
yield
3D
resolution
maps
excellent
baseline
all
single
run,
which
found
be
very
accurate,
differences
between
experimental
simulated
times
less
than
1%.
described
here
also
includes
introduction
versatile
by
introducing
dynamic
RT
database
covering
entire
library
broad
mechanisms
action
numerous
formulations
linearity,
accuracy,
precision,
specificity.
power
this
demonstrated
generated
rapidly
guiding
processes
formulations.
Analytical
work
covers
profile
RPLC-UV-CAD,
component
identification
(RPLC-MS)
formulations,
surfactants
(e.g.,
polysorbates),
well
targets.
Язык: Английский
Digitally Enabled Generic Analytical Framework Accelerating the Pace of Liquid Chromatography Method Development for Vaccine Adjuvant Formulations
ACS Pharmacology & Translational Science,
Год журнала:
2024,
Номер
7(10), С. 3108 - 3118
Опубликована: Сен. 11, 2024
The
growing
use
of
adjuvants
in
the
fast-paced
formulation
new
vaccines
has
created
an
unprecedented
need
for
meaningful
analytical
assays
that
deliver
reliable
quantitative
data
from
complex
adjuvant
and
adjuvant-antigen
mixtures.
Due
to
their
chemical
physical
properties,
method
development
separation
vaccine
is
considered
a
highly
challenging
laborious
task.
Reversed-phase
liquid
chromatography
(RPLC)
among
most
important
tests
(bio)pharmaceutical
industry
release
stability
indicating
measurements
including
content,
identity,
purity
profile.
However,
time
constraints
developing
"on-demand"
robust
methods
prior
each
change
can
easily
lead
sample
analysis
becoming
bottleneck
development.
Herein,
simple
efficient
generic
framework
capable
chromatographically
resolving
commonly
used
non-aluminum-based
across
academic
industrial
sectors
introduced.
This
was
designed
seek
more
proactive
approach
assay
endeavors
evolved
extensive
stationary
phase
screening
conjunction
with
multifactorial
Язык: Английский