Exploring	
  Compound	
  Combina1ons	
  in	
  
High	
  Throughput	
  Se9ngs	
  	
  
Going	
  Beyond	
  1D	
  Metrics	
  

...
Outline	
  
Why	
  combine?	
  

Physical	
  infrastructure	
  &	
  workflow	
  

Summarizing	
  and	
  exploring	
  the	
 ...
Screening	
  for	
  Novel	
  Drug	
  Combina1ons	
  
Transla5onal	
  Interest	
  
•  Increased	
  efficacy	
  
•  Delay	
  r...
How	
  to	
  Test	
  Combina1ons	
  
•  Many	
  procedures	
  described	
  in	
  the	
  literature	
  
–  Fixed	
  dose	
 ...
Scaling	
  Response	
  Surface	
  Screening	
  
5e+07

Combination type

•  Response	
  surfaces	
  	
  
imply	
  a	
  DxD...
Mechanism	
  Interroga1on	
  PlateE	
  
•  Collec[on	
  of	
  ~	
  2000	
  small	
  molecules	
  of	
  diverse	
  
mechani...
Mechanism	
  Interroga1on	
  PlateE	
  
Top	
  10	
  enriched	
  GeneGo	
  pathway	
  maps	
  
Development EGFR signaling ...
Combina1on	
  Screening	
  Workflow	
  
Run	
  single	
  agent	
  dose	
  responses	
  

6x6	
  matrices	
  for	
  	
  
pot...
Where	
  Are	
  We	
  Now?	
  
•  238	
  screens	
  in	
  total	
  
–  30	
  screens	
  against	
  full	
  	
  
MIPE3	
  o...
Screening	
  Challenges	
  
•  A	
  key	
  challenge	
  is	
  automated	
  quality	
  control	
  
•  Plate	
  level	
  dat...
QC	
  Examples	
  
•  Inves[ga[ng	
  an[-­‐malarial	
  combina[ons	
  
•  300	
  10x10	
  combina[ons	
  in	
  duplicate	
...
QC	
  Examples	
  

Compound

•  Single	
  agents	
  with	
  very	
  high	
  MSR’s	
  could	
  be	
  
used	
  to	
  flag	
 ...
Repor1ng	
  Combina1on	
  Results	
  
Repor1ng	
  Combina1on	
  Results	
  
Exploring	
  Combina1on	
  Metrics	
  
•  We	
  implement	
  a	
  variety	
  of	
  metrics	
  to	
  
characterize	
  syner...
8226
AMO-1
ANBL-6
ARP-1
EJM
FR4
INA-6
JJN3
JK-6L
KMS-11
KMS-11LB
KMS-12BM
KMS-12PE
KMS-18
KMS-20
KMS-26
KMS-28BM
KMS-28PE
...
Repor1ng	
  Combina1on	
  Results	
  
•  These	
  web	
  pages	
  and	
  matrix	
  layouts	
  are	
  a	
  
useful	
  first	...
When	
  are	
  Combina1ons	
  Similar?	
  
•  Differences	
  and	
  their	
  
aggregates	
  such	
  as	
  RMSD	
  
can	
  l...
Similarity	
  via	
  the	
  KS	
  Test	
  
•  Quan[fy	
  distance	
  between	
  response	
  
distribu[ons	
  via	
  KS	
  ...
Similarity	
  via	
  the	
  Syrjala	
  Test	
  
•  Syrjala	
  test	
  used	
  to	
  compare	
  
popula[on	
  distribu[ons	...
Ibru1nib	
  Combina1ons	
  For	
  DLBCL	
  
•  Primary	
  focus	
  is	
  on	
  inves[ga[ng	
  combina[ons	
  
with	
  Ibru...
0.8

Clustering	
  Response	
  Surfaces	
  

0.4

0.6

C1	
  (24)	
  

C3(35)	
  
0.2

C2(47)	
  

0.0

C4(24)	
  
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...
52

136

150

184

217

322

339

384

165

163

371

139

116

145

194

241

327

125

82

143

164

215

254

361

0.00...
Combina1ons	
  across	
  Cell	
  Lines	
  
•  Cellular	
  background	
  affects	
  responses	
  
•  Can	
  we	
  group	
  c...
Working	
  in	
  Combina1on	
  Space	
  
•  Each	
  cell	
  line	
  is	
  represented	
  as	
  a	
  vector	
  of	
  
respo...
L363
XG-2

0.8

1.0

0.20

0.25

1.2

0

KMS-20

XG-2

OPM-1

L363

0.0

MM-MM1

SKMM-1

KMS-11LB

U266

EJM

8226

XG-1

...
Exploi1ng	
  Polypharmacology	
  
•  Vargatef	
  exhibited	
  anomalous	
  matrix	
  
response	
  compared	
  to	
  other	...
Exploi1ng	
  Polypharmacology	
  
DCC-2036

PD-166285

GDC-0941

PI-103

GDC-0980

Bardoxolone methyl

AT-7519
AT7519

SNS...
Predic1ng	
  Synergies	
  
•  Related	
  to	
  response	
  surface	
  methodologies	
  
•  LiKle	
  work	
  on	
  predic[n...
Structural	
  Similarity	
  vs	
  Synergy	
  
beta

gamma
0.4

0.3

0.3

0.2

0.2

0.1

0.1

Similarity

0.4

0.85

0.90

...
Predic1on	
  Strategy	
  
•  Don’t	
  directly	
  predict	
  synergy	
  
•  Use	
  single	
  agent	
  data	
  to	
  genera...
Conclusions	
  
•  Use	
  response	
  surfaces	
  as	
  first	
  class	
  descriptors	
  of	
  
drug	
  combina[ons	
  
–  ...
Acknowledgements	
  
•  Lou	
  Staudt	
  
•  Beverly	
  Mock,	
  John	
  Simmons	
  
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Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D Metrics

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Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D Metrics

  1. 1. Exploring  Compound  Combina1ons  in   High  Throughput  Se9ngs     Going  Beyond  1D  Metrics   Rajarshi  Guha,  Lesley  Mathews,  John   Keller,  Paul  Shinn,  Dongbo  Liu,  Craig   Thomas,  Anton  Simeonov,  Marc  Ferrer   NIH-­‐NCATS     January  2013,  San  Diego  
  2. 2. Outline   Why  combine?   Physical  infrastructure  &  workflow   Summarizing  and  exploring  the  data   hKp://origin.arstechnica.com/news.media/pills-­‐4.jpg  
  3. 3. Screening  for  Novel  Drug  Combina1ons   Transla5onal  Interest   •  Increased  efficacy   •  Delay  resistance   •  AKenuate  toxicity   Basic  Interest   •  Inform  signaling  pathway   connec[vity   •  Iden[fy  synthe[c  lethality   •  Highlight   polypharmacology  
  4. 4. How  to  Test  Combina1ons   •  Many  procedures  described  in  the  literature   –  Fixed  dose  ra[o  (aka  ray)   –  Ray  contour   –  Checkerboard   –  Gene[c  algorithm     C5 C5,D5 C4 C4,D4 C3 C3,D3 C2 C2,D2 C1,D5 C1,D4 C1,D3 C1,D2 C1,D1 D5 D4 D3 D2 D1 C1 0
  5. 5. Scaling  Response  Surface  Screening   5e+07 Combination type •  Response  surfaces     imply  a  DxD  matrix     for  each  combina[on   •  All  pairs  screening  is     imprac[cal  for  more     than  tens  of       compounds   •  Instead  we  consider  N  compounds  versus  a   fixed  size  library     All pairs Fixed library Number of combinations 4e+07 Dose matrix size 4 6 10 3e+07 2e+07 1e+07 0e+00 250 500 750 Number of compounds 1000
  6. 6. Mechanism  Interroga1on  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   belinostat HDAC inhibitor Phase II AMG-47a Lck inhibitor Preclinical GSK-1995010 FAS inhibitor Preclinical JZL-184 MAGL inhibitor Preclinical JNJ-38877605 HGFR inhibitor Phase I Eliprodil NMDA antagonist Phase III
  7. 7. Mechanism  Interroga1on  PlateE   Top  10  enriched  GeneGo  pathway  maps   Development EGFR signaling pathway Some pathways of EMT in cancer cells Development VEGF signaling via VEGFR2 - generic cascades Apoptosis and survival Anti-apoptotic action of Gastrin Cell adhesion Chemokines and adhesion Cytoskeleton remodeling TGF, WNT and cytoskeletal remodeling Regulation of lipid metabolism RXR-dependent regulation of lipid metabolism via PPAR, RAR and VDR Transcription PPAR Pathway Translation Non-genomic (rapid) action of Androgen Receptor Development VEGF signaling and activation 0 5 -log10(pValue) 10 15
  8. 8. Combina1on  Screening  Workflow   Run  single  agent  dose  responses   6x6  matrices  for     poten1al  synergies   10x10  for  confirma1on   +  self-­‐cross   Acoustic dispense, 15 min for 1260 wells, 14 min for 1200 wells"
  9. 9. Where  Are  We  Now?   •  238  screens  in  total   –  30  screens  against  full     MIPE3  or  MIPE4   •  200  cell  lines   –  Various  cancers   –  Mainly  human   Number of assays 150 100 50 0 0 500 1000 1500 Number of combinations •  Combined  with  target  annota[ons  we  can   look  at  combina[on  behavior  as  a  func[on  of   various  factors   2000
  10. 10. 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?  
  11. 11. QC  Examples   •  Inves[ga[ng  an[-­‐malarial  combina[ons   •  300  10x10  combina[ons  in  duplicate   •  15  compounds  included  more  than  ten  [mes   -1.5 log IC50 (uM) -2.0 -2.5 -3.0 -3.5 -4.0 Artemether Artesunate Dihydro artemisinin Halofantrine Lumefantrine
  12. 12. QC  Examples   Compound •  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   Freq 40 30 20 10 0 5 10 MSR 15 20
  13. 13. Repor1ng  Combina1on  Results  
  14. 14. Repor1ng  Combina1on  Results  
  15. 15. Exploring  Combina1on  Metrics   •  We  implement  a  variety  of  metrics  to   characterize  synergy/addi[vity/antagonism   •  Lots  of  possible  ques[ons   –  How  is  a  metric  distributed  in  a  given  assay?   –  How  does  a  metric  vary  with  cell  line?   –  Do  metrics  correlate?   –  How  does  a  certain  combina[on  behave  across   cell  lines?  
  16. 16. 8226 AMO-1 ANBL-6 ARP-1 EJM FR4 INA-6 JJN3 JK-6L KMS-11 KMS-11LB KMS-12BM KMS-12PE KMS-18 KMS-20 KMS-26 KMS-28BM KMS-28PE KMS-34 L363 LP-1 MM-MM1 MM.1.144 MOLP-8 OCI-MY-5 OCI-MY1 OPM-1 OPM-2 RPMI-8226 SACHI SKMM-1 U266 XG-1 XG-2 XG-6 XG-7 Delta Bliss Neg Sum Exploring  Combina1on  Metrics   0 -5 -10
  17. 17. Repor1ng  Combina1on  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  beKer  job  of  ranking  and   aggrega[ng  combina[on  responses  taking   into  account   –  Response  matrix   –  Compounds,  targets  and  pathways  
  18. 18. When  are  Combina1ons  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   0.06 0.010 0.04 0.005 0.02 0.00 0.000 0 25 50 75 100 0 0.15 0.10 0.05 0.00 0 50 100 D, p value 25 50 75 100
  19. 19. Similarity  via  the  KS  Test   •  Quan[fy  distance  between  response   distribu[ons  via  KS  test   –  If  p-­‐value  >  0.05,  we  assume   distance  is  0   9 density •  But  ignores  the  spa1al   distribu[on  of  the  responses   on  the  concentra[on  grid   6 3 0 0.00 0.25 0.50 D 0.75 1.00
  20. 20. Similarity  via  the  Syrjala  Test   •  Syrjala  test  used  to  compare   popula[on  distribu[ons   over  a  spa[al  grid   density –  Invariant  to  grid  orienta[on   –  Provides  an  empirical  p-­‐value   •  Less  degenerate  than  just   considering  1D  distribu[ons   10.0 7.5 5.0 2.5 0.0 0.00 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   0.25 D 0.50 0.75
  21. 21. Ibru1nib  Combina1ons  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     Viable Cells (% DMSO) Ibrutinib MK-2206 Ibrutinib* + MK-2206 Ibrutinib* (nM) MK-2206 (µM) Mathews-­‐Griner,  Guha,  Shinn  et  al.  PNAS,  2014,  in  press  
  22. 22. 0.8 Clustering  Response  Surfaces   0.4 0.6 C1  (24)   C3(35)   0.2 C2(47)   0.0 C4(24)  
  23. 23. 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 0.00 0.05 0.10 0.15 0.20 0.25 0.30 Cluster  C3   macromolecule catabolic process •  Vargatef,  vorinostat,   flavopiridol,  …   •  Not  par[cularly   specific  given  the   range  of  primary   targets   regulation of interferon-gamma-mediated signaling pathway ubiquitin-dependent protein catabolic process cellular process involved in reproduction negative regulation of cell cycle peptidyl-amino acid modification interphase cell cycle checkpoint peptidyl-tyrosine phosphorylation response to stress 0 1 -log10(Pvalue) 2 3
  24. 24. 52 136 150 184 217 322 339 384 165 163 371 139 116 145 194 241 327 125 82 143 164 215 254 361 0.00 0.02 0.04 0.06 0.08 Cluster  C4   cellular carbohydrate biosynthetic process •  Focus  on  sugar   metabolism     •  Ruboxistaurin,   cycloheximide,  2-­‐ methoxyestradiol,  …   •  PI3K/Akt/mTOR   signalling  pathways   regulation of polysaccharide biosynthetic process cellular macromolecule localization peptidyl-serine phosphorylation regulation of generation of precursor metabolites and energy cellular polysaccharide metabolic process glucan metabolic process glucan biosynthetic process regulation of glycogen biosynthetic process glycogen metabolic process 0 1 -log10(Pvalue) 2 3
  25. 25. Combina1ons  across  Cell  Lines   •  Cellular  background  affects  responses   •  Can  we  group  cell  lines  based  on  combina[on   response?  
  26. 26. Working  in  Combina1on  Space   •  Each  cell  line  is  represented  as  a  vector  of   response  matrices   L L •  “Distance”  between  two     ,   cell  lines  is  a  func[on  of  the   ,   distance  between  component   response  matrices   ,     ,   D ( L1, L2 ) = F({d1, d2 ,…, dn })   ,   •  F  can  be  min,  max,  mean,  …     1   2   =  d1   =  d2   =  d3   =  d4   =  d5  
  27. 27. L363 XG-2 0.8 1.0 0.20 0.25 1.2 0 KMS-20 XG-2 OPM-1 L363 0.0 MM-MM1 SKMM-1 KMS-11LB U266 EJM 8226 XG-1 OCI-MY1 XG-7 KMS-20 ANBL-6 MOLP-8 XG-6 AMO-1 FR4 XG-2 OPM-1 L363 INA-6 KMS-34 FR4 8226 XG-7 OCI-MY1 EJM SKMM-1 MM-MM1 ANBL-6 XG-1 KMS-11LB MOLP-8 KMS-20 AMO-1 KMS-34 8226 MOLP-8 AMO-1 XG-6 XG-6 INA-6 U266 EJM U266 FR4 OCI-MY1 SKMM-1 MM-MM1 XG-7 ANBL-6 KMS-11LB XG-1 0.6 0.15 euc INA-6 KMS-34 0.4 0.10 min OPM-1 0.2 0.05 0.1 1 0.2 0.3 2 0.4 3 0.5 4 0.6 sum 0.0 XG-7 MOLP-8 KMS-34 FR4 XG-6 AMO-1 KMS-11LB L363 KMS-20 OCI-MY1 XG-2 OPM-1 EJM SKMM-1 ANBL-6 U266 XG-1 8226 MM-MM1 INA-6 0.00 Many  Choices  to  Make   max
  28. 28. Exploi1ng  Polypharmacology   •  Vargatef  exhibited  anomalous  matrix   response  compared  to  other  VEGFR  inhibitors             Linifanib Sorafenib Vatalanib Motesanib Tivozanib Brivanib Telatinib Cabozantinib Cediranib BMS-794833 Lenvatinib OSI-632 Vargatef   Axitinib Foretinib Regorafenib
  29. 29. Exploi1ng  Polypharmacology   DCC-2036 PD-166285 GDC-0941 PI-103 GDC-0980 Bardoxolone methyl AT-7519 AT7519 SNS-032 NCGC00188382-01 Lestaurtinib CNF-2024 ISOX •  PD-­‐166285  is  a  SRC  &   FGFR  inhibitor   •  Lestaurnib  has     ac[vity  against  FLT3   Vargatef Belinostat PF-477736 AZD-7762 Src Lyn Lck Flt-3 PDGFRb PDGFRa FGFR-4 FGFR-3 Chk1 IC50 = 105 nM FGFR-2 FGFR-1 VEGFR-3 VEGFR-2 VEGFR-1 0 200 Potency (nM) Hilberg,  F.  et  al,  Cancer  Res.,  2008,  68,  4774-­‐4782   400 600
  30. 30. Predic1ng  Synergies   •  Related  to  response  surface  methodologies   •  LiKle  work  on  predic[ng  drug  response  surfaces   –  Peng  et  al,  PLoS  One,  2011   –  Jin  et  al,  Bioinforma1cs,  2011   –  Boik  &  Newman,  BMC  Pharmacology,  2008   –  Lehar  et  al,  Mol  Syst  Bio,  2007   •  But  synergy  is  not  always  objec[ve  and  doesn’t   really  correlate  with  structure  
  31. 31. Structural  Similarity  vs  Synergy   beta gamma 0.4 0.3 0.3 0.2 0.2 0.1 0.1 Similarity 0.4 0.85 0.90 0.95 1.00 ssnum 1.05 1.10 1.15 0.4 -30 -20 1.05 0.2 0.1 0.95 Win 3x3 0.3 0.2 0.85 0.4 0.3 0.75 0.1 0 5 10 15 20 25 -40 Synergy measure -10 0
  32. 32. Predic1on  Strategy   •  Don’t  directly  predict  synergy   •  Use  single  agent  data  to  generate  a  model   surface   •  Predict  combina[on  responses   •  Characterize  synergy  of  predicted  response   with  respect  to  model  surface       •  Reduced  to  a  mixture  predic[on  problem   •  Need  to  incorporate  target  connec[vity  
  33. 33. 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  
  34. 34. Acknowledgements   •  Lou  Staudt   •  Beverly  Mock,  John  Simmons  
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