Abstract
BCS
class
II
candidates
pose
challenges
in
drug
development
due
to
their
low
solubility
and
permeability.
Researchers
have
explored
various
techniques;
co-amorphous
solid
dispersion
are
major
approaches
enhance
in-vitro
dissolution.
However,
in-vivo
oral
bioavailability
remains
challenging.
Physiologically
based
pharmacokinetic
(PBPK)
modeling
with
a
detailed
understanding
of
absorption,
distribution,
metabolism,
excretion
(ADME)
using
mechanistic
approach
is
emerging.
This
review
summarizes
the
fundamentals
PBPK,
dissolution—absorption
models,
parameterization
absorption
for
drugs,
provides
information
about
newly
emerging
artificial
intelligence/machine
learning
(AI/ML)
linked
PBPK
advantages,
disadvantages,
areas
further
exploration.
Additionally,
fully
integrated
workflow
formulation
design
investigational
new
drugs
(INDs)
virtual
bioequivalence
generic
molecules
falling
under
BCS-II
discussed.
Graphical
Pharmacological Research,
Год журнала:
2025,
Номер
unknown, С. 107734 - 107734
Опубликована: Апрель 1, 2025
Drug
discovery
before
the
20th
century
often
focused
on
single
genes,
molecules,
cells,
or
organs,
failing
to
capture
complexity
of
biological
systems.
The
emergence
protein-protein
interaction
network
studies
in
2001
marked
a
turning
point
and
promoted
holistic
approach
that
considers
human
body
as
an
interconnected
system.
This
is
particularly
evident
study
bidirectional
interactions
between
central
nervous
system
(CNS)
peripheral
which
are
critical
for
understanding
health
disease.
Understanding
these
complex
requires
integrating
multi-scale,
heterogeneous
data
from
molecular
organ
levels,
encompassing
both
omics
(e.g.,
genomics,
proteomics,
microbiomics)
non-omics
imaging,
clinical
phenotypes).
Artificial
intelligence
(AI),
multi-modal
models,
has
demonstrated
significant
potential
analyzing
CNS-peripheral
by
processing
vast,
datasets.
Specifically,
AI
facilitates
identification
biomarkers,
prediction
therapeutic
targets,
simulation
drug
effects
multi-organ
systems,
thereby
paving
way
novel
strategies.
review
highlights
AI's
transformative
role
research,
focusing
its
applications
unraveling
disease
mechanisms,
discovering
optimizing
trials
through
patient
stratification
adaptive
trial
design.
Journal of Medical Internet Research,
Год журнала:
2025,
Номер
27, С. e65651 - e65651
Опубликована: Янв. 13, 2025
Background
At
the
end
of
2023,
Bayer
AG
launched
its
own
internal
large
language
model
(LLM),
MyGenAssist,
based
on
ChatGPT
technology
to
overcome
data
privacy
concerns.
It
may
offer
possibility
decrease
their
harshness
and
save
time
spent
repetitive
recurrent
tasks
that
could
then
be
dedicated
activities
with
higher
added
value.
Although
there
is
a
current
worldwide
reflection
whether
artificial
intelligence
should
integrated
into
pharmacovigilance,
medical
literature
does
not
provide
enough
concerning
LLMs
daily
applications
in
such
setting.
Here,
we
studied
how
this
tool
improve
case
documentation
process,
which
duty
for
authorization
holders
as
per
European
French
good
vigilance
practices.
Objective
The
aim
study
test
use
an
LLM
pharmacovigilance
process.
Methods
MyGenAssist
was
trained
draft
templates
letters
meant
sent
reporters.
Information
provided
within
template
changes
depending
case:
come
from
table
LLM.
We
measured
each
period
4
months
(2
before
using
2
after
implementation).
A
multiple
linear
regression
created
explained
variable,
all
parameters
influence
were
included
explanatory
variables
(use
type
recipient,
number
questions,
user).
To
if
impacts
compared
recipients’
response
rates
without
MyGenAssist.
Results
An
average
23.3%
(95%
CI
13.8%-32.8%)
saving
made
thanks
(P<.001;
adjusted
R2=0.286)
case,
represent
10.7
(SD
3.6)
working
days
saved
year.
answer
rate
modified
by
(20/48,
42%
vs
27/74,
36%;
P=.57)
recipient
physician
or
patient.
No
significant
difference
found
regarding
(mean
2.20,
SD
3.27
mean
2.65,
3.30
last
attempt
contact;
P=.64).
implementation
activity
only
required
2-hour
training
session
team.
Conclusions
Our
first
show
ChatGPT-based
can
efficiency
practice
needing
long
affected
workforce.
These
encouraging
results
incentive
other
processes.
International Journal of Molecular Sciences,
Год журнала:
2025,
Номер
26(3), С. 871 - 871
Опубликована: Янв. 21, 2025
Cancer
metastasis
is
a
leading
cause
of
cancer-related
deaths
and
represents
one
the
most
challenging
processes
to
study
due
its
complexity
dynamic
nature.
Zebrafish
(Danio
rerio)
have
become
an
invaluable
model
in
research,
offering
unique
advantages
such
as
optical
transparency,
rapid
development,
ability
visualize
tumor
interactions
with
microenvironment
real
time.
This
review
explores
how
zebrafish
models
elucidated
critical
steps
metastasis,
including
invasion,
vascular
remodeling,
immune
evasion,
while
also
serving
platforms
for
drug
testing
personalized
medicine.
Advances
patient-derived
xenografts
innovative
genetic
tools
further
established
cornerstone
cancer
particularly
understanding
molecular
drivers
identifying
therapeutic
targets.
By
bridging
experimental
findings
clinical
relevance,
continue
transforming
our
biology
therapy.
Antimicrobial
resistance
(AMR)
is
a
growing
public
health
issue,
complicating
the
treatment
of
bacterial
infections
and
increasing
morbidity
mortality
globally.
This
phenomenon,
which
occurs
as
result
ability
bacteria
to
adapt
evade
conventional
treatments,
requires
innovative
strategies
address
it.
Artificial
intelligence
(AI)
emerges
transformative
tool
in
this
context,
helping
accelerate
identification
molecules
with
antimicrobial
potential
optimize
design
new
drugs.
article
analyzes
usefulness
AI
antibiotic
development,
highlighting
its
benefits
terms
time,
cost,
efficiency
fight
against
resistant
bacteria,
well
challenges
associated
implementation
biomedical
field.
IntechOpen eBooks,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 6, 2025
One
of
the
main
issues
with
drug
delivery
system
is
delivering
to
specific
target
site
anticipated
concentration
produce
a
desired
therapeutic
potential
drug.
The
major
drawbacks
in
conventional
dosage
forms
are
lack
targeted
delivery,
selectivity,
non-specific
distribution,
poor
bioavailability,
frequent
regimen,
side
effects,
first-pass
metabolism,
solubility
for
poorly
soluble
drugs,
inability
cross
biological
barriers,
gastrointestinal
irritation,
interaction,
and
effectiveness.
Recent
advancements
molecular
pharmacology
action
sites
particular
diseases
have
made
new
revolution
develop
different
novel
systems.
These
systems
significantly
increase
thus
exploiting
effect
reducing
accumulation
drugs
off
site.
Different
include
microemulsion
microsphers;
nanodrug
nanoparticles,
nanogels,
nanoemulsion,
nanosuspension,
nanotubes,
dendrimers;
vesicular
includes
liposomes,
lipospheres,
niosomes,
phytosomes,
transfersomes,
ethosomes,
vesosomes,
herbosomes,
solid
lipid
so
on.
Parameters
such
as
particle
size,
shape,
solubility,
surface
morphology,
charge,
biocompatibility,
biodegradability,
release
play
significant
role
deliver
concentration.
This
chapter
outlines
discovery
molecule,
development
process,
limitations
form,
current
system,
application
nanoparticles
disease
diagnosis,
treatment
like
cancer,
regulatory
challenges.
Further
artificial
intelligence
has
been
outlined
future
perspectives
system.
