Machine
learning
(ML)
is
revolutionizing
drug
repurposing,
offering
a
more
efficient,
cost-effective
approach
to
discovery
by
identifying
new
therapeutic
uses
for
existing
drugs.
ML
algorithms
process
large,
complex
biomedical
datasets,
find
hidden
patterns
that
reveal
unexpected
links
between
drugs
and
diseases,
predict
potential
side
effects.
This
advancement
holds
significant
promise
precision
medicine
personalized
healthcare.
chapter
aims
explore
the
growing
role
of
in
an
emergent
frontier
identify
drugs,
thereby
accelerating
pace
medical
innovation
while
mitigating
cost
risk.
The
discusses
various
case
studies,
demonstrating
application
drug–disease
connections
predicting
adverse
reactions,
significantly
contributing
medicine.
In
addition,
investigates
successes
challenges
encountered
this
nascent
field,
highlighting
modernize
discovery.
Emphasis
placed
on
ethical
privacy
concerns
surrounding
use
patient
data
models,
urging
need
robust
regulations.
comprehensive
review
serves
as
practical
guide
those
at
intersection
pharmaceutical
research,
clinical
practice,
computer
sciences,
advocating
synergetic
these
fields
advancing
Biomedicine & Pharmacotherapy,
Год журнала:
2023,
Номер
161, С. 114408 - 114408
Опубликована: Фев. 24, 2023
Antibody
Drug
Conjugate
(ADC)
is
an
emerging
technology
to
overcome
the
limitations
of
chemotherapy
by
selectively
targeting
cancer
cells.
ADC
binds
with
antigen,
specifically
over
expressed
on
surface
cells,
results
decrease
in
bystander
effect
and
increase
therapeutic
index.
The
potency
ideal
entirely
depending
several
physicochemical
factors
such
as
site
conjugation,
molecular
weight,
linker
length,
Steric
hinderance,
half-life,
conjugation
method,
binding
energy
so
on.
Inspite
fact
that
there
more
than
100
ADCs
are
clinical
trial
only
14
approved
FDA
for
use.
However,
design
still
challenging
much
be
done.
Here
this
review,
we
have
discussed
key
components
along
their
significant
role
or
contribution
towards
efficacy
ADC.
Moreover,
also
explained
about
recent
advancement
method.
Additionally,
spotlit
mode
action
ADC,
challenges,
future
perspective
regarding
profound
knowledge
properties
will
help
synthesis
production
different
engineered
ADCs.
Therefore,
contributes
develop
low
safety
concern
high
We
hope
review
improve
understanding
encourage
practicing
research
anticancer
development.
Journal of Translational Medicine,
Год журнала:
2024,
Номер
22(1)
Опубликована: Апрель 30, 2024
Abstract
Upon
a
diagnosis,
the
clinical
team
faces
two
main
questions:
what
treatment,
and
at
dose?
Clinical
trials'
results
provide
basis
for
guidance
support
official
protocols
that
clinicians
use
to
base
their
decisions.
However,
individuals
do
not
consistently
demonstrate
reported
response
from
relevant
trials.
The
decision
complexity
increases
with
combination
treatments
where
drugs
administered
together
can
interact
each
other,
which
is
often
case.
Additionally,
individual's
treatment
varies
changes
in
condition.
In
practice,
drug
dose
selection
depend
significantly
on
medical
protocol
team's
experience.
As
such,
are
inherently
varied
suboptimal.
Big
data
Artificial
Intelligence
(AI)
approaches
have
emerged
as
excellent
decision-making
tools,
but
multiple
challenges
limit
application.
AI
rapidly
evolving
dynamic
field
potential
revolutionize
various
aspects
of
human
life.
has
become
increasingly
crucial
discovery
development.
enhances
across
different
disciplines,
such
medicinal
chemistry,
molecular
cell
biology,
pharmacology,
pathology,
practice.
addition
these,
contributes
patient
population
stratification.
need
healthcare
evident
it
aids
enhancing
accuracy
ensuring
quality
care
necessary
effective
treatment.
pivotal
improving
success
rates
increasing
significance
discovery,
development,
trials
underscored
by
many
scientific
publications.
Despite
numerous
advantages
AI,
advancing
Precision
Medicine
(PM)
remote
monitoring,
unlocking
its
full
requires
addressing
fundamental
concerns.
These
concerns
include
quality,
lack
well-annotated
large
datasets,
privacy
safety
issues,
biases
algorithms,
legal
ethical
challenges,
obstacles
related
cost
implementation.
Nevertheless,
integrating
medicine
will
improve
diagnostic
outcomes,
contribute
more
efficient
delivery,
reduce
costs,
facilitate
better
experiences,
making
sustainable.
This
article
reviews
applications
development
sustainable,
highlights
limitations
applying
AI.
Journal of Translational Medicine,
Год журнала:
2024,
Номер
22(1)
Опубликована: Фев. 5, 2024
Abstract
Advancements
in
data
acquisition
and
computational
methods
are
generating
a
large
amount
of
heterogeneous
biomedical
from
diagnostic
domains
such
as
clinical
imaging,
pathology,
next-generation
sequencing
(NGS),
which
help
characterize
individual
differences
patients.
However,
this
information
needs
to
be
available
suitable
promote
support
scientific
research
technological
development,
supporting
the
effective
adoption
precision
medicine
approach
practice.
Digital
biobanks
can
catalyze
process,
facilitating
sharing
curated
standardized
imaging
data,
clinical,
pathological
molecular
crucial
enable
development
comprehensive
personalized
data-driven
disease
management
fostering
predictive
models.
This
work
aims
frame
perspective,
first
by
evaluating
state
standardization
then
identifying
challenges
proposing
possible
solution
towards
an
integrative
that
guarantee
suitability
shared
through
digital
biobank.
Our
analysis
art
shows
presence
use
reference
standards
and,
generally,
repositories
for
each
specific
domain.
Despite
this,
integration
reproducibility
numerical
descriptors
generated
domain,
e.g.
radiomic,
pathomic
-omic
features,
is
still
open
challenge.
Based
on
cases
scenarios,
model,
based
JSON
format,
proposed
address
problem.
Ultimately,
how,
with
promotion
efforts,
biobank
model
become
enabling
technology
study
diseases
technologies
at
service
medicine.
Journal of Medical Virology,
Год журнала:
2023,
Номер
95(4)
Опубликована: Март 22, 2023
Cancer
management
is
major
concern
of
health
organizations
and
viral
cancers
account
for
approximately
15.4%
all
known
human
cancers.
Due
to
large
number
patients,
efficient
treatments
are
needed.
De
novo
drug
discovery
time
consuming
expensive
process
with
high
failure
rate
in
clinical
stages.
To
address
this
problem
provide
patients
suffering
from
faster,
repurposing
emerges
as
an
effective
alternative
which
aims
find
the
other
indications
Food
Drug
Administration
approved
drugs.
Applied
cancers,
studies
following
niche
have
tried
if
already
existing
drugs
could
be
used
treat
Multiple
approaches
till
date
been
introduced
successful
results
many
successfully
repurposed
various
Here
study,
a
critical
review
cancer
related
databases,
tools,
different
machine
learning,
deep
learning
virtual
screening-based
focusing
on
provided.
Additionally,
mechanism
presented
along
case
study
specific
each
cancer.
Finally,
limitations
challenges
possible
solutions
Journal of Biomedical Informatics,
Год журнала:
2023,
Номер
142, С. 104373 - 104373
Опубликована: Апрель 27, 2023
Cancer
is
the
second
leading
cause
of
death
globally,
trailing
only
heart
disease.
In
United
States
alone,
1.9
million
new
cancer
cases
and
609,360
deaths
were
recorded
for
2022.
Unfortunately,
success
rate
drug
development
remains
less
than
10%,
making
disease
particularly
challenging.
This
low
largely
attributed
to
complex
poorly
understood
nature
etiology.
Therefore,
it
critical
find
alternative
approaches
understanding
biology
developing
effective
treatments.
One
such
approach
repurposing,
which
offers
a
shorter
timeline
lower
costs
while
increasing
likelihood
success.
this
review,
we
provide
comprehensive
analysis
computational
biology,
including
systems
multi-omics,
pathway
analysis.
Additionally,
examine
use
these
methods
repurposing
in
cancer,
databases
tools
that
are
used
research.
Finally,
present
case
studies
discussing
their
limitations
offering
recommendations
future
research
area.
Frontiers in Pharmacology,
Год журнала:
2023,
Номер
14
Опубликована: Апрель 25, 2023
The
inefficiency
of
existing
animal
models
to
precisely
predict
human
pharmacological
effects
is
the
root
reason
for
drug
development
failure.
Microphysiological
system/organ-on-a-chip
technology
(organ-on-a-chip
platform)
a
microfluidic
device
cultured
with
living
cells
under
specific
organ
shear
stress
which
can
faithfully
replicate
organ-body
level
pathophysiology.
This
emerging
organ-on-chip
platform
be
remarkable
alternative
broad
range
purposes
in
testing
and
precision
medicine.
Here,
we
review
parameters
employed
using
on
chip
as
plot
mimic
diseases,
genetic
disorders,
toxicity
different
organs,
biomarker
identification,
discoveries.
Additionally,
address
current
challenges
that
should
overcome
accepted
by
regulatory
agencies
pharmaceutical
industries.
Moreover,
highlight
future
direction
enhancing
accelerating
discoveries
personalized
Heliyon,
Год журнала:
2024,
Номер
10(5), С. e27465 - e27465
Опубликована: Март 1, 2024
BackgroundLactylation
is
a
significant
post-translational
modification
bridging
the
gap
between
cancer
epigenetics
and
metabolic
reprogramming.
However,
association
lactylation
prognosis,
tumor
microenvironment
(TME),
response
to
drug
therapy
in
various
cancers
remains
unclear.MethodsFirst,
expression,
prognostic
value,
genetic
epigenetic
alterations
of
genes
were
systematically
explored
pan-cancer
manner.
Lactylation
scores
derived
for
each
using
single-sample
gene
set
enrichment
analysis
(ssGSEA)
algorithm.
The
correlation
with
clinical
features,
TME
was
assessed
by
integrating
multiple
computational
methods.
In
addition,
GSE135222
data
used
assess
efficacy
predicting
immunotherapy
outcomes.
expression
breast
gliomas
verified
RNA-sequencing.ResultsLactylation
significantly
upregulated
most
types.
CREBBP
EP300
exhibited
high
mutation
rates
analysis.
impact
score
varied
type,
protective
factor
KIRC,
ACC,
READ,
LGG,
UVM,
risk
CHOL,
DLBC,
LAML,
OV.
associated
cold
TME.
infiltration
levels
CD8+
T,
γδT,
natural
killer
T
cell
(NKT),
NK
cells
lower
tumors
higher
scores.
Finally,
worse
patients
than
other
types.ConclusionLactylation
are
involved
malignancy
formation.
serves
as
promising
biomarker
patient
prognosis
efficacy.
Computational and Structural Biotechnology Journal,
Год журнала:
2022,
Номер
20, С. 6097 - 6107
Опубликована: Янв. 1, 2022
Psoriasis
is
a
skin
disease
which
results
in
scales
on
the
caused
by
flaky
patches.
triggered
various
conditions
such
as
drug
reactions,
trauma,
and
infection
etc.
Globally,
there
are
125
million
people
affected
psoriasis
yet
no
effective
treatment
available,
it
emphasizes
need
for
discovery
of
efficacious
treatments.
De-novo
development
takes
10∼17
years
$2∼$3
billion
investment
with
less
than
10%
success
rate
to
bring
from
concept
market
ready
product.
A
possible
alternative
repurposing,
aims
at
finding
other
indications
already
approved
drugs.
In
this
study,
computational
repurposing
framework
developed
applied
differential
gene
expressions
targets
obtained
publicly
available
database
(GEO).
This
strategy
uses
expression
signatures
compares
perturbagen
CMap.
Based
connected
signature
drugs
ranked
could
possibly
reverse
stop
psoriasis.
The
most
negative
connectivity
scores
efficient
vice
versa.
top
hit
verified
using
literature
survey
peer
reviewed
journal,
electronic
health
records,
patents,
hospital
database.
As
result,
50/150
37/150
confirmed
have
anti-psoriasis
efficacy
two
datasets.
Top
10
suggested
potential
repurposable
study
offers,
powerful
simple
approach
rapid
identification
candidates
any
interest.