The document discusses high-throughput screening of drug combinations to identify synergistic interactions that could lead to increased efficacy, delayed resistance, or reduced toxicity. The author outlines their workflow for combination screening against diverse cancer cell lines and molecular libraries. Over 300 screens have been conducted to date, assessing over 1,000 drug combinations. Challenges include automated quality control of large combination datasets and effective analysis methods to rank and interpret combination responses based on multiple factors. Network representations are proposed to help analyze and visualize combination screening results.
IEEE Computer Society 2024 Technology Predictions Update
When the whole is better than the parts
1. When
the
whole
is
be-er
than
the
parts
Analy'cs
for
high
throughput
combina'on
screening
Rajarshi
Guha
NIH
Center
for
Advancing
Transla'onal
Science
Howard
University,
Washington
DC
March
26,
2014
4. How
to
Test
Combina'ons
• Many
procedures
described
in
the
literature
– Fixed
dose
ra'o
(aka
ray)
– Ray
contour
– Checkerboard
– Gene'c
algorithm
C5,D5 C5
C4,D4 C4
C3,D3 C3
C2,D2 C2
C1,D5 C1,D4 C1,D3 C1,D2 C1,D1 C1
D5 D4 D3 D2 D1 0
5. Mechanism
Interroga'on
PlateE
• Collec'on
of
~
2000
small
molecules
of
diverse
mechanism
of
ac'on.
• 745
approved
drugs
• 420
phase
I-‐III
inves'ga'onal
drugs
• 767
preclinical
molecules
• Diverse
and
redundant
MOAs
represented
AMG-47a
Lck inhibitor
Preclinical
belinostat
HDAC inhibitor
Phase II
Eliprodil
NMDA antagonist
Phase III
JNJ-38877605
HGFR inhibitor
Phase I
JZL-184
MAGL inhibitor
Preclinical
GSK-1995010
FAS inhibitor
Preclinical
6. Combina'on
Screening
Workflow
Run
single
agent
dose
responses
6x6
matrices
for
poten5al
synergies
10x10
for
confirma5on
+
self-‐cross
Acoustic dispense, 15 min
for 1260 wells, 14 min for
1200 wells"
7. Where
Are
We
Now?
• 309
screens
in
total
– 189
screens
against
full
MIPE3
or
MIPE4
• ~
200
cell
lines
– Various
cancers
– Mainly
human
• Combined
with
target
annota'ons
we
can
look
at
combina'on
behavior
as
a
func'on
of
various
factors
0
50
100
150
0 500 1000 1500 2000
Number of combinations
Numberofassays
8. Screening
Challenges
• A
key
challenge
is
automated
quality
control
• Plate
level
data
employs
standard
metrics
focusing
on
control
performance
• Combina'on
level
is
more
challenging
– Single
agent
performance
is
one
approach
– MSR
across
all
combina'on
can
provide
a
high
level
view
– But
how
to
iden'fy
bad
blocks?
9. 0 5 10 15 20
MSR
Compound
10
20
30
40
Freq
QC
Examples
• Single
agents
with
very
high
MSR’s
could
be
used
to
flag
combina'ons
containing
them
• Doesn’t
help
for
compounds
with
only
one
or
two
replicates
• S'll
requires
manual
inspec'on
12. Repor'ng
Combina'on
Results
• These
web
pages
and
matrix
layouts
are
a
useful
first
step
• Does
not
scale
as
we
grow
MIPE
• S'll
need
to
do
a
beMer
job
of
ranking
and
aggrega'ng
combina'on
responses
taking
into
account
– Response
matrix
– Compounds,
targets
and
pathways
15. When
are
Combina'ons
Similar?
• Differences
and
their
aggregates
such
as
RMSD
can
lead
to
degeneracy
• Instead
we’re
interested
in
the
shape
of
the
surface
• How
to
characterize
shape?
– Parametrized
fits
– Distribu'on
of
responses
0.000
0.005
0.010
0 25 50 75 100
0.00
0.02
0.04
0.06
0 25 50 75 100
0.00
0.05
0.10
0.15
0 50 100
D, p value
16. 0.0
2.5
5.0
7.5
10.0
0.00 0.25 0.50 0.75
D
density
Similarity
via
the
Syrjala
Test
• Syrjala
test
used
to
compare
popula'on
distribu'ons
over
a
spa'al
grid
– Invariant
to
grid
orienta'on
– Provides
an
empirical
p-‐value
• Less
degenerate
than
just
considering
1D
distribu'ons
Syrjala,
S.E.,
“A
Sta's'cal
Test
for
a
Difference
between
the
Spa'al
Distribu'ons
of
Two
Popula'ons”,
Ecology,
1996,
77(1),
75-‐80
17. Ibru'nib
Combina'ons
For
DLBCL
• Primary
focus
is
on
inves'ga'ng
combina'ons
with
Ibru'nib
for
treatment
of
DLBCL
– Btk
inhibitor
in
Phase
II
trials
– Experiments
run
in
the
TMD8
cell
line,
tes'ng
for
cell
viability
Mathews-‐Griner,
Guha,
Shinn
et
al.
PNAS,
2014
Viable
Cells
(% DMSO)
Ibrutinib* (nM)
MK-2206 (µM)
Ibrutinib
MK-2206
Ibrutinib* +
MK-2206
19. response to stress
peptidyl-tyrosine phosphorylation
cell cycle checkpoint
interphase
peptidyl-amino acid modification
negative regulation of cell cycle
cellular process involved in reproduction
ubiquitin-dependent protein catabolic process
regulation of interferon-gamma-mediated signaling pathway
macromolecule catabolic process
0 1 2 3
-log10(Pvalue)
Cluster
C3
• Vargatef,
vorinostat,
flavopiridol,
…
• Not
par'cularly
specific
given
the
range
of
primary
targets
0.000.050.100.150.200.250.30
302
281
128
174
285
153
177
210
144
35
60
457
180
39
111
272
288
166
231
104
106
417
319
44
218
279
219
121
119
34
102
286
230
178
179
20. Cluster
C4
• Focus
on
sugar
metabolism
• Ruboxistaurin,
cycloheximide,
2-‐
methoxyestradiol,
…
• PI3K/Akt/mTOR
signalling
pathways
glycogen metabolic process
regulation of glycogen biosynthetic process
glucan biosynthetic process
glucan metabolic process
cellular polysaccharide metabolic process
regulation of generation of precursor metabolites and energy
peptidyl-serine phosphorylation
cellular macromolecule localization
regulation of polysaccharide biosynthetic process
cellular carbohydrate biosynthetic process
0 1 2 3
-log10(Pvalue)
0.000.020.040.060.08
361
254
215
164
143
82
125
327
241
194
145
116
139
371
163
165
384
339
322
217
184
150
52
136
21. Combina'ons
across
Cell
Lines
• Cellular
background
affects
responses
• Can
we
group
cell
lines
based
on
combina'on
response?
22. Working
in
Combina'on
Space
• Each
cell
line
is
represented
as
a
vector
of
response
matrices
• “Distance”
between
two
cell
lines
is
a
func'on
of
the
distance
between
component
response
matrices
• F
can
be
min,
max,
mean,
…
L1
L2
=
d1
=
d2
=
d3
=
d4
=
d5
D L1, L2( )= F({d1,d2,…,dn})
,
,
,
,
,
25. Exploi'ng
Polypharmacology
• PD-‐166285
is
a
SRC
&
FGFR
inhibitor
• Lestaurnib
has
ac'vity
against
FLT3
Vargatef DCC-2036 PD-166285 GDC-0941
PI-103 GDC-0980 Bardoxolone methyl AT-7519AT7519
SNS-032 NCGC00188382-01 Lestaurtinib CNF-2024
ISOX Belinostat PF-477736 AZD-7762
Chk1 IC50 = 105 nM
VEGFR-1
VEGFR-2
VEGFR-3
FGFR-1
FGFR-2
FGFR-3
FGFR-4
PDGFRa
PDGFRb
Flt-3
Lck
Lyn
Src
0 200 400 600
Potency (nM)
Hilberg,
F.
et
al,
Cancer
Res.,
2008,
68,
4774-‐4782
26. Conclusions
• Use
response
surfaces
as
first
class
descriptors
of
drug
combina'ons
– Surrogate
for
underlying
target
network
connec'vity
(?)
• Response
surface
similarity
based
on
distribu'ons
is
(fundamentally)
non-‐parametric
• Going
from
single
-‐
chemical
space
to
combina'on
space
opens
up
interes'ng
possibili'es
• Manual
inspec'on
is
s'll
a
vital
step
27. Acknowledgements
• Craig
Thomas,
Marc
Ferrar,
Lesley
Mathews,
Paul
Shin,
Sam
Michaels,
John
Keller,
Dongbo
Liu,
Anton
Simeonov,
Bryan
MoM
• Lou
Staudt
• Xinzhuan
Su,
Paul
Roepe,
Rich
Eastwood
• Beverly
Mock,
John
Simmons