Crystal Growth & Design,
Journal Year:
2025,
Volume and Issue:
25(10), P. 3374 - 3385
Published: April 29, 2025
Cocrystals
are
an
attractive
option
for
overcoming
drug
limitations,
such
as
a
low
dissolution
rate
and
absorption
of
poorly
water-soluble
compounds.
Nevertheless,
the
discovery
new
cocrystals
remains
trial-and-error
approach
in
which
hundreds
coformers
several
experimental
methods
often
tested.
To
streamline
cocrystal
screening,
computational
can
be
used
to
select
most
likely
form
cocrystal,
while
high-throughput
screening
(HTS)
approaches
rapidly
screen
them
experimentally.
In
this
manuscript,
extremely
soluble
cilnidipine
(solubility
≈30
ng/mL,
0.06
μM)
was
successfully
discovered
by
applying
HTS
approaches.
Only
one
resulted
from
with
total
52
coformers,
whereby
molecular
complementarity
ranked
coformer
(p-toluenesulfonamide)
at
third
position
list.
Dissolution
studies
conducted
on
blank
FaSSIF
(fasted-state
simulated
intestinal
fluid)
pH
6.5
revealed
enhanced
maximum
achieved
supersaturation
equal
seven
times
solubility
crystalline
drug.
rates
were
compared
better
mechanistic
understanding
dissolution-supersaturation-precipitation
behavior.
The
case
rare
occurrence
emphasized
importance
using
joint
enable
successful
identification
pharmaceutical
development.
Acta Pharmaceutica Sinica B,
Journal Year:
2021,
Volume and Issue:
11(8), P. 2537 - 2564
Published: March 23, 2021
Pharmaceutical
cocrystals
are
multicomponent
systems
in
which
at
least
one
component
is
an
active
pharmaceutical
ingredient
and
the
others
pharmaceutically
acceptable
ingredients.
Cocrystallization
of
a
drug
substance
with
coformer
promising
emerging
approach
to
improve
performance
pharmaceuticals,
such
as
solubility,
dissolution
profile,
pharmacokinetics
stability.
This
review
article
presents
comprehensive
overview
cocrystals,
including
preparation
methods,
physicochemical
properties,
applications.
Furthermore,
some
examples
highlighted
illustrate
effect
crystal
structures
on
various
aspects
ingredients,
physical
stability,
chemical
mechanical
optical
bioavailability,
sustained
release
therapeutic
effect.
will
provide
guidance
for
more
efficient
design
manufacture
desired
properties
CrystEngComm,
Journal Year:
2021,
Volume and Issue:
23(40), P. 7005 - 7038
Published: Jan. 1, 2021
This
highlight
presents
an
overview
of
pharmaceutical
cocrystal
production
and
its
potential
in
reviving
problematic
properties
drugs
different
dosage
forms.
The
challenges
future
outlook
translational
development
are
discussed.
Nature Communications,
Journal Year:
2021,
Volume and Issue:
12(1)
Published: Oct. 12, 2021
Abstract
Cocrystal
engineering
have
been
widely
applied
in
pharmaceutical,
chemistry
and
material
fields.
However,
how
to
effectively
choose
coformer
has
a
challenging
task
on
experiments.
Here
we
develop
graph
neural
network
(GNN)
based
deep
learning
framework
quickly
predict
formation
of
the
cocrystal.
In
order
capture
main
driving
force
crystallization
from
6819
positive
1052
negative
samples
reported
by
experiments,
feasible
GNN
is
explored
integrate
important
prior
knowledge
into
end-to-end
molecular
graph.
The
model
strongly
validated
against
seven
competitive
models
three
independent
test
sets
involving
pharmaceutical
cocrystals,
π–π
cocrystals
energetic
exhibiting
superior
performance
with
accuracy
higher
than
96%,
confirming
its
robustness
generalization.
Furthermore,
one
new
cocrystal
predicted
successfully
synthesized,
showcasing
high
potential
practice.
All
data
source
codes
are
available
at
https://github.com/Saoge123/ccgnet
for
aiding
community.
CrystEngComm,
Journal Year:
2024,
Volume and Issue:
26(11), P. 1505 - 1526
Published: Jan. 1, 2024
A
holistic
understanding
of
reaction
kinetics,
the
presence
catalysts,
and
annealing
conditions
can
advance
accelerate
screening
elusive
cocrystals,
expediting
development
novel
drug
cocrystals
for
future
clinical
use.
Crystal Growth & Design,
Journal Year:
2021,
Volume and Issue:
21(4), P. 2301 - 2314
Published: Feb. 26, 2021
Experimental
mechanochemical
screening
of
cocrystals
with
linezolid
(LIN)
resulted
in
the
formation
six
new
crystal
phases,
including
three
neat
and
cocrystal
hydrates,
addition
to
seven
previously
described
cocrystals.
In
an
attempt
understand
factors
governing
these
different
experimental
conditions
reactions
(polymorphic
forms
LIN
presence
solvents
create
liquid-assisted
grinding
conditions)
were
tested
results
compared
predictions
from
commonly
used
virtual
tools:
molecular
complementarity,
hydrogen
bond
propensity,
electrostatic
potential
maps.
It
is
shown
that
methods
can
be
help
a
molecule's
preferences
form
particular
coformers.
The
influence
conformation
on
outcome
also
evaluated.
A
comparison
between
prediction
indicates
while
considering
set
similar
coformers,
approach
based
maps
seems
more
consistent
than
complementarity
propensity
tools.
Instead,
two
latter
approaches
are
recommended
at
early
stages
coformer
selection.
addition,
intermolecular
energy
contribution
(lattice
energy)
total
coformers
was
found
indicative
feasibility
case
capable
forming
supramolecular
synthons.
Journal of Chemical Information and Modeling,
Journal Year:
2022,
Volume and Issue:
62(5), P. 1160 - 1171
Published: Feb. 28, 2022
Computational
chemistry
applications
have
become
an
integral
part
of
the
drug
discovery
workflow
over
past
35
years.
However,
computational
modeling
in
support
development
has
remained
a
relatively
uncharted
territory
for
significant
both
academic
and
industrial
communities.
This
review
considers
workflows
three
key
components
preclinical
clinical
development,
namely,
process
chemistry,
analytical
research
as
well
product
formulation
development.
An
overview
each
step
respective
is
presented.
Additionally,
context
solid
form
design,
special
consideration
given
to
modern
physics-based
virtual
screening
methods.
covers
rational
approaches
polymorph,
coformer,
counterion,
solvent
selection
design.
Crystal Growth & Design,
Journal Year:
2022,
Volume and Issue:
22(7), P. 4513 - 4527
Published: June 15, 2022
Controlling
the
physical
properties
of
solid
forms
for
active
pharmaceutical
ingredients
(APIs)
through
cocrystallization
is
an
important
part
drug
product
development.
However,
it
difficult
to
know
a
priori
which
coformers
will
form
cocrystals
with
given
API,
and
current
state-of-the-art
cocrystal
discovery
involves
expensive,
time-consuming,
and,
at
early
stages
development,
API
material-limited
experimental
screen.
We
propose
systematic,
high-throughput
computational
approach
primarily
aimed
identifying
API/coformer
pairs
that
are
unlikely
lead
experimentally
observable
can
therefore
be
eliminated
only
brief
check,
from
any
investigation.
On
basis
well-established
crystal
structure
prediction
(CSP)
methodology,
proposed
derives
its
efficiency
by
not
requiring
expensive
quantum
mechanical
calculations
beyond
those
already
performed
CSP
investigation
neat
itself.
The
assumptions
tested
on
30
potential
1:1
multicomponent
systems
(cocrystals
solvate)
involving
3
9
one
solvent.
This
complemented
detailed
all
pairs,
led
five
new
(three
API-coformer
combinations,
polymorphic
example,
different
stoichiometries)
cis-aconitic
acid
polymorph.
indicates
that,
some
APIs,
significant
proportion
could
investigated
thereby
saving
considerable
effort.
Pharmaceutics,
Journal Year:
2023,
Volume and Issue:
15(3), P. 836 - 836
Published: March 3, 2023
In
this
study,
the
existing
set
of
carbamazepine
(CBZ)
cocrystals
was
extended
through
successful
combination
drug
with
positional
isomers
acetamidobenzoic
acid.
The
structural
and
energetic
features
CBZ
3-
4-acetamidobenzoic
acids
were
elucidated
via
single-crystal
X-ray
diffraction
followed
by
QTAIMC
analysis.
ability
three
fundamentally
different
virtual
screening
methods
to
predict
correct
cocrystallization
outcome
for
assessed
based
on
new
experimental
results
obtained
in
study
data
available
literature.
It
found
that
hydrogen
bond
propensity
model
performed
worst
distinguishing
positive
negative
experiments
87
coformers,
attaining
an
accuracy
value
lower
than
random
guessing.
method
utilizes
molecular
electrostatic
potential
maps
machine
learning
approach
named
CCGNet
exhibited
comparable
terms
prediction
metrics,
albeit
latter
resulted
superior
specificity
overall
while
requiring
no
time-consuming
DFT
computations.
addition,
formation
thermodynamic
parameters
newly
evaluated
using
temperature
dependences
Gibbs
energy.
reactions
between
selected
coformers
be
enthalpy-driven,
entropy
being
statistically
from
zero.
observed
difference
dissolution
behavior
aqueous
media
thought
caused
variations
their
stability.