medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 13, 2024
Abstract
Analysis
of
real-world
data
(RWD)
is
attractive
for
its
applicability
to
scenarios
but
RWD
typically
used
drug
repurposing
and
not
therapeutic
target
discovery.
Repurposing
studies
have
identified
few
effective
options
in
neuroinflammatory
diseases
with
relatively
patients
such
as
amyotrophic
lateral
sclerosis
(ALS),
which
characterized
by
progressive
muscle
weakness
death
no
disease-modifying
treatments
available.
We
previously
reclassified
drugs
their
simulated
effects
on
proteins
downstream
targets
observed
class-level
the
EHR,
implicating
protein
source
effect.
Here,
we
developed
a
novel
ALS-focused
pathways
model
using
from
patient
samples,
public
domain,
consortia.
With
this
model,
ALS
measured
class
overall
survival
retrospective
EHR
studies.
an
increased
non-significant
risk
taking
associated
complement
system
experimentally
validated
activation.
repeated
six
classes,
three
which,
including
multiple
chemokine
receptors,
were
significant
death,
suggesting
that
targeting
receptors
could
be
advantageous
these
patients.
recovered
activation
Parkinson’s
Myasthenia
Gravis
demonstrated
utility
network
medicine
testing
believe
approach
may
accelerate
discovery
diseases,
addressing
critical
need
new
options.
Nature Communications,
Год журнала:
2025,
Номер
16(1)
Опубликована: Фев. 19, 2025
Repurposed
drugs
provide
a
rich
source
of
potential
therapies
for
Alzheimer's
disease
(AD)
and
other
neurodegenerative
disorders
(NDD).
have
information
from
non-clinical
studies,
phase
1
dosing,
safety
tolerability
data
collected
with
the
original
indication.
Computational
approaches,
"omic"
drug
databases,
electronic
medical
records
help
identify
candidate
therapies.
Generic
repurposed
agents
lack
intellectual
property
protection
are
rarely
advanced
to
late-stage
trials
AD/NDD.
In
this
review
we
define
repurposing,
describe
advantages
challenges
offer
strategies
overcoming
obstacles,
key
contributions
repurposing
development
ecosystem.
review,
authors
discuss
obstacles
development.
International Journal of Molecular Sciences,
Год журнала:
2025,
Номер
26(5), С. 1935 - 1935
Опубликована: Фев. 24, 2025
The
role
of
amyloid
beta
peptide
(Aβ)
in
memory
regulation
has
been
a
subject
substantial
interest
and
debate
neuroscience,
because
both
physiological
clinical
issues.
Understanding
the
dual
nature
Aβ
is
crucial
for
developing
effective
treatments
Alzheimer's
disease
(AD).
Moreover,
accurate
detection
quantification
methods
isoforms
have
tested
diagnostic
purposes
therapeutic
interventions.
This
review
provides
insight
into
current
knowledge
about
vivo
vitro
by
fluid
tests
brain
imaging
(PET),
which
allow
preclinical
recognition
disease.
Currently,
priority
development
new
therapies
given
to
potential
changes
progression
In
light
increasing
amounts
data,
this
was
focused
on
employment
While
various
models
and
computational
tools
have
been
proposed
for
structure
property
analysis
of
molecules,
generating
molecules
that
conform
to
all
desired
structures
properties
remains
a
challenge.
We
introduce
multi-constraint
molecular
generation
large
language
model,
TSMMG,
which,
akin
student,
incorporates
knowledge
from
small
tools,
namely,
the
"teachers."
To
train
we
construct
set
text-molecule
pairs
by
extracting
these
"teachers,"
enabling
it
generate
novel
descriptions
through
text
prompts.
experimentally
show
TSMMG
remarkably
performs
in
meet
complex
requirements
described
natural
across
two-,
three-,
four-constraint
tasks,
with
an
average
validity
over
99%
success
ratio
82.58%,
68.03%,
67.48%,
respectively.
The
model
also
exhibits
adaptability
zero-shot
testing,
creating
satisfy
combinations
not
encountered.
It
can
comprehend
inputs
styles,
extending
beyond
confines
outlined
presents
effective
using
language.
This
framework
is
only
applicable
drug
discovery
but
serves
as
reference
other
related
fields.
Journal of Parkinson s Disease,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 27, 2025
Recent
years
have
seen
successes
in
symptomatic
drugs
for
Parkinson's
disease,
but
the
development
of
treatments
stopping
disease
progression
continues
to
fail
clinical
drug
trials,
largely
due
lack
efficacy
drugs.
This
may
be
related
limited
understanding
mechanisms,
data
heterogeneity,
poor
target
screening
and
candidate
selection,
challenges
determining
optimal
dosage
levels,
reliance
on
animal
models,
insufficient
patient
participation,
adherence
trials.
Most
recent
applications
digital
health
technologies
artificial
intelligence
(AI)-based
tools
focused
mainly
stages
before
used
AI-based
algorithms
or
models
discover
novel
targets,
inhibitors
indications,
recommend
candidates
dosage,
promote
remote
collection.
paper
reviews
state
literature
highlights
strengths
limitations
approaches
discovery
from
2021
2024,
offers
recommendations
future
research
practice
success
Clinical Pharmacology & Therapeutics,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 1, 2025
Although
attractive
for
relevance
to
real-world
scenarios,
data
(RWD)
is
typically
used
drug
repurposing
and
not
therapeutic
target
discovery.
Repurposing
studies
have
identified
few
effective
options
in
neurological
diseases
such
as
the
rare
disease,
amyotrophic
lateral
sclerosis
(ALS),
which
has
no
disease-modifying
treatments
available.
We
previously
reclassified
drugs
by
their
simulated
effects
on
proteins
downstream
of
targets
observed
class-level
EHR,
implicating
protein
source
effect.
Here,
we
developed
a
novel
ALS-focused
network
medicine
model
using
from
patient
samples,
public
domain,
consortia.
With
this
model,
ALS
measured
class
overall
survival
retrospective
EHR
studies.
an
increased
but
non-significant
risk
death
patients
taking
with
complement
system
experimentally
validated
activation.
repeated
six
classes,
three
which,
including
multiple
chemokine
receptors,
were
associated
significantly
death,
suggesting
that
targeting
CXCR5,
CXCR3,
signaling
generally,
or
neuropeptide
Y
(NPY)
could
be
advantageous
these
patients.
expanded
our
analysis
neuroinflammatory
condition,
myasthenia
gravis,
neurodegenerative
Parkinson's,
recovered
similar
effect
sizes.
demonstrated
utility
testing
RWD
believe
approach
may
accelerate
discovery
diseases,
addressing
critical
need
new
options.
Brain Sciences,
Год журнала:
2025,
Номер
15(5), С. 486 - 486
Опубликована: Май 5, 2025
Alzheimer’s
disease,
a
complex
neurodegenerative
is
characterized
by
the
pathological
aggregation
of
insoluble
amyloid
β
and
hyperphosphorylated
tau.
Multiple
models
this
disease
have
been
employed
to
investigate
etiology,
pathogenesis,
multifactorial
aspects
facilitate
therapeutic
development.
Mammals,
especially
mice,
are
most
common
for
studying
pathogenesis
in
vivo.
To
date,
scientific
literature
has
documented
more
than
280
mouse
exhibiting
diverse
pathogenesis.
Other
mammalian
species,
including
rats,
pigs,
primates,
also
utilized
as
models.
Selected
modeled
simpler
model
organisms,
such
Drosophila
melanogaster,
Caenorhabditis
elegans,
Danio
rerio.
It
possible
not
only
creating
genetically
modified
animal
lines
but
inducing
symptoms
disease.
This
review
discusses
main
methods
induced
models,
with
particular
focus
on
modeling
cell
cultures.
Induced
pluripotent
stem
(iPSC)
technology
facilitated
novel
investigations
into
mechanistic
underpinnings
diseases,
Alzheimer’s.
Progress
culturing
brain
tissue
allows
personalized
studies
how
drugs
affect
brain.
Recent
years
witnessed
substantial
advancements
intricate
cellular
system
development,
spheroids,
three-dimensional
scaffolds,
microfluidic
Microfluidic
technologies
emerged
cutting-edge
tools
intercellular
interactions,
microenvironment,
role
blood–brain
barrier
(BBB).
Modern
biology
experiencing
significant
paradigm
shift
towards
utilizing
big
data
omics
technologies.
Computational
represents
powerful
methodology
researching
wide
array
human
Bioinformatic
methodologies
analysis
extensive
datasets
generated
via
high-throughput
experimentation.
imperative
underscore
significance
integrating
techniques
elucidating
pathogenic
mechanisms
their
entirety.
medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 7, 2024
Abstract
Alzheimer’s
Disease
(AD)
significantly
aggravates
human
dignity
and
quality
of
life.
While
newly
approved
amyloid
immunotherapy
has
been
reported,
effective
AD
drugs
remain
to
be
identified.
Here,
we
propose
a
novel
AI-driven
drug-repurposing
method,
DeepDrug,
identify
lead
combination
treat
patients.
DeepDrug
advances
methodology
in
four
aspects.
Firstly,
it
incorporates
expert
knowledge
extend
candidate
targets
include
long
genes,
immunological
aging
pathways,
somatic
mutation
markers
that
are
associated
with
AD.
Secondly,
signed
directed
heterogeneous
biomedical
graph
encompassing
rich
set
nodes
edges,
node/edge
weighting
capture
crucial
pathways
Thirdly,
encodes
the
weighted
through
Graph
Neural
Network
into
new
embedding
space
granular
relationships
across
different
nodes.
Fourthly,
systematically
selects
high-order
drug
combinations
via
diminishing
return-based
thresholds.
A
five-drug
combination,
consisting
Tofacitinib,
Niraparib,
Baricitinib,
Empagliflozin,
Doxercalciferol,
selected
from
top
candidates
based
on
scores
achieve
maximum
synergistic
effect.
These
five
target
neuroinflammation,
mitochondrial
dysfunction,
glucose
metabolism,
which
all
related
pathology.
offers
AI-and-big-data,
expert-guided
mechanism
for
discovery
other
neuro-degenerative
diseases,
immediate
clinical
applications.
