Exploring	
  Compound	
  Combina1ons	
  in	
  
High	
  Throughput	
  Se9ngs	
  	
  
Going	
  Beyond	
  1D	
  Metrics	
  
R...
Background	
  
•  Cheminforma:cs	
  methods	
  
–  QSAR,	
  diversity	
  analysis,	
  virtual	
  screening,	
  	
  
fragme...
Outline	
  
hUp://origin.arstechnica.com/news.media/pills-­‐4.jpg	
  
Why	
  combine?	
  
Physical	
  infrastructure	
  &	...
Screening	
  for	
  Novel	
  Drug	
  Combina1ons	
  
•  Increased	
  efficacy	
  
•  Delay	
  resistance	
  
•  AUenuate	
  ...
How	
  to	
  Test	
  Combina1ons	
  
•  Many	
  procedures	
  described	
  in	
  the	
  literature	
  
– Fixed	
  dose	
  ...
Mechanism	
  Interroga1on	
  PlateE	
  
•  Collec:on	
  of	
  ~	
  2000	
  small	
  molecules	
  of	
  diverse	
  
mechani...
Development VEGF signaling and activation
Translation Non-genomic (rapid) action of Androgen Receptor
Transcription PPAR P...
Combina1on	
  Screening	
  Workflow	
  
Run	
  single	
  agent	
  dose	
  responses	
  
6x6	
  matrices	
  for	
  	
  
pote...
Where	
  Are	
  We	
  Now?	
  
•  382	
  screens	
  in	
  total	
  
– 65,960	
  combina:ons	
  
– 3,024,224	
  wells	
  
•...
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	
...
0 5 10 15 20
MSR
Compound
10
20
30
40
Freq
QC	
  Examples	
  
•  Single	
  agents	
  with	
  very	
  high	
  MSR’s	
  coul...
QC	
  Score	
  
A	
  heuris:c	
  score	
  that	
  can	
  be	
  
used	
  to	
  focus	
  on	
  good	
  quality	
  
combina:o...
QC	
  Score	
  
QCS	
  =	
  0	
  
QCS	
  =	
  13	
  QCS	
  =	
  2	
  
•  Depends	
  on	
  mul:ple	
  
subjec:ve	
  thresho...
Repor1ng	
  Combina1on	
  Results	
  
Repor1ng	
  Combina1on	
  Results	
  
Repor1ng	
  Combina1on	
  Results	
  
•  These	
  web	
  pages	
  and	
  matrix	
  layouts	
  are	
  a	
  
useful	
  first	...
Network	
  Representa1ons	
  
Combina:on	
  screens	
  lend	
  themselves	
  naturally	
  
to	
  network	
  representa:ons...
Network	
  Representa1ons	
  
•  Things	
  get	
  more	
  	
  
interes:ng	
  when	
  
we	
  have	
  n	
  	
  	
  	
  	
  m...
Comparing	
  Neighborhoods	
  
Combina:ons	
  that	
  have	
  DBSumNeg	
  <	
  1st	
  quar:le	
  value	
  for	
  
that	
  ...
Comparing	
  Neighborhoods	
  
Alterna:vely,	
  consider	
  all	
  tested	
  combina:ons,	
  
highligh:ng	
  distribu:on	
...
Iden1fying	
  the	
  Most	
  Synergis1c	
  Pairs	
  
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●...
When	
  are	
  Combina1ons	
  Similar?	
  
•  Differences	
  and	
  their	
  
aggregates	
  such	
  as	
  RMSD	
  
can	
  l...
0
3
6
9
0.00 0.25 0.50 0.75 1.00
D
density
Similarity	
  via	
  the	
  KS	
  Test	
  
•  Quan:fy	
  distance	
  between	
 ...
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	
  u...
Ibru1nib	
  Combina1ons	
  For	
  DLBCL	
  
•  Primary	
  focus	
  is	
  on	
  inves:ga:ng	
  combina:ons	
  
with	
  Ibru...
Clustering	
  Response	
  Surfaces	
  0.00.20.40.60.8
C1	
  (24)	
  
C2(47)	
  
C3(35)	
  
C4(24)	
  
response to stress
peptidyl-tyrosine phosphorylation
cell cycle checkpoint
interphase
peptidyl-amino acid modification
neg...
Cluster	
  C4	
  
•  Focus	
  on	
  sugar	
  
metabolism	
  	
  
•  Ruboxistaurin,	
  
cycloheximide,	
  2-­‐
methoxyestra...
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...
Many	
  Choices	
  to	
  Make	
  
01234
KMS-34
INA-6
L363
OPM-1
XG-2
FR4
AMO-1
XG-6
MOLP-8
ANBL-6
KMS-20
XG-7
OCI-MY1
XG-1...
•  Vargatef	
  exhibited	
  anomalous	
  matrix	
  
response	
  compared	
  to	
  other	
  VEGFR	
  inhibitors	
  
	
  
	
...
Exploi1ng	
  Polypharmacology	
  
•  PD-­‐166285	
  is	
  a	
  SRC	
  &	
  
FGFR	
  inhibitor	
  
•  Lestaurnib	
  has	
  ...
Predic1ng	
  Synergies	
  
•  Related	
  to	
  response	
  surface	
  methodologies	
  
•  LiUle	
  work	
  on	
  predic:n...
Structural	
  Similarity	
  vs	
  Synergy	
  
beta gamma
ssnum Win 3x3
0.1
0.2
0.3
0.4
0.1
0.2
0.3
0.4
0.1
0.2
0.3
0.4
0.1...
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	
  
•  Lesley	
  Griner,	
  Craig	
  Thom...
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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   NCATS   June  2014,  Novar:s,  Boston.  
  2. 2. Background   •  Cheminforma:cs  methods   –  QSAR,  diversity  analysis,  virtual  screening,     fragments,  polypharmacology,  networks   •  More  recently   –  RNAi  screening,  high  content  imaging,     combina:on  screening   •  Extensive  use  of  machine  learning   •  All  :ed  together  with  soMware     development   –  User-­‐facing  GUI  tools   –  Low  level  programma:c  libraries,  APIs,     databases     •  Believer  &  prac::oner  of  Open  Source  
  3. 3. Outline   hUp://origin.arstechnica.com/news.media/pills-­‐4.jpg   Why  combine?   Physical  infrastructure  &  workflow   Summarizing  and  exploring  the  data  
  4. 4. Screening  for  Novel  Drug  Combina1ons   •  Increased  efficacy   •  Delay  resistance   •  AUenuate  toxicity   •  Inform  signaling  pathway   connec:vity   •  Iden:fy  synthe:c  lethality   •  Highlight   polypharmacology   Transla5onal  Interest   Basic  Interest  
  5. 5. How  to  Test  Combina1ons   •  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
  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   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
  7. 7. Development VEGF signaling and activation Translation Non-genomic (rapid) action of Androgen Receptor Transcription PPAR Pathway Regulation of lipid metabolism RXR-dependent regulation of lipid metabolism via PPAR, RAR and VDR Cytoskeleton remodeling TGF, WNT and cytoskeletal remodeling Cell adhesion Chemokines and adhesion Apoptosis and survival Anti-apoptotic action of Gastrin Development VEGF signaling via VEGFR2 - generic cascades Some pathways of EMT in cancer cells Development EGFR signaling pathway 0 5 10 15 -log10(pValue) Mechanism  Interroga1on  PlateE   Top  10  enriched  GeneGo  pathway  maps  
  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?   •  382  screens  in  total   – 65,960  combina:ons   – 3,024,224  wells   •  244  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
  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   -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 Artemether Artesunate Dihydro artemisinin Halofantrine Lumefantrine logIC50(uM)
  12. 12. 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  
  13. 13. QC  Score   A  heuris:c  score  that  can  be   used  to  focus  on  good  quality   combina:ons   Acceptable DMSO response Valid single agent curve fit & IC50 Sufficient variance in dose sub-matrix Spatial autocorrelation in dose sub-matrix Acceptable single agent efficacy 0 250 500 750 0 2 3 5 6 7 8 10 11 12 13 15 16 QC Score Frequency Strain 3D7 DD2 HB3
  14. 14. QC  Score   QCS  =  0   QCS  =  13  QCS  =  2   •  Depends  on  mul:ple   subjec:ve  thresholds   •  Passes  some  poor   quality  blocks   •  Quickly  filters  out  very   bad  combina:ons  
  15. 15. Repor1ng  Combina1on  Results  
  16. 16. Repor1ng  Combina1on  Results  
  17. 17. Repor1ng  Combina1on  Results   •  These  web  pages  and  matrix  layouts  are  a   useful  first  step   •  Does  not  scale  as  we  grow  MIPE     •  Need  beUer  ways  of  ranking  and  aggrega:ng   combina:on  responses  taking  into  account   – Response  matrix   – Compounds,  targets  and  pathways   – Clinical  status  and  other  external  informa:on  
  18. 18. Network  Representa1ons   Combina:on  screens  lend  themselves  naturally   to  network  representa:ons                     ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ∆ Bliss+ −4.3 −3.8 −3.3 −2.9 −2.4 −1.9 −1.4 −1.0 −0.5 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ∆ Bliss+ −3.4 −3.1 −2.7 −2.3 −1.9 −1.5 −1.2 −0.8 −0.4 0.0 immune system process apoptotic process transcription from RNA polymerase II promoter protein phosphorylation cell communication immune response
  19. 19. Network  Representa1ons   •  Things  get  more     interes:ng  when   we  have  n          m   screens   •  Can  be  simplified   using  a  variety  of     methods   – Neighborhoods   – Minimum  Spanning  Tree   ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ×
  20. 20. Comparing  Neighborhoods   Combina:ons  that  have  DBSumNeg  <  1st  quar:le  value  for   that  strain   3D7 DD2 HB3
  21. 21. Comparing  Neighborhoods   Alterna:vely,  consider  all  tested  combina:ons,   highligh:ng  distribu:on  of  synergis:c  and   antagonis:c  combina:ons   3D7 DD2 HB3
  22. 22. Iden1fying  the  Most  Synergis1c  Pairs   ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
  23. 23. 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   0.000 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
  24. 24. 0 3 6 9 0.00 0.25 0.50 0.75 1.00 D density 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   •  But  ignores  the  spa1al   distribu:on  of  the  responses   on  the  concentra:on  grid  
  25. 25. 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  
  26. 26. 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     Mathews-­‐Griner,  Guha,  Shinn  et  al.  PNAS,  2014,  in  press   Viable Cells (% DMSO) Ibrutinib* (nM) MK-2206 (µM) Ibrutinib MK-2206 Ibrutinib* + MK-2206
  27. 27. Clustering  Response  Surfaces  0.00.20.40.60.8 C1  (24)   C2(47)   C3(35)   C4(24)  
  28. 28. 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
  29. 29. 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
  30. 30. Combina1ons  across  Cell  Lines   •  Cellular  background  affects  responses   •  Can  we  group  cell  lines  based  on  combina:on   response?     •  Or  find  “fingerprints”  that  characterize  cell  lines?  
  31. 31. Working  in  Combina1on  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}) ,   ,   ,   ,   ,  
  32. 32. Many  Choices  to  Make   01234 KMS-34 INA-6 L363 OPM-1 XG-2 FR4 AMO-1 XG-6 MOLP-8 ANBL-6 KMS-20 XG-7 OCI-MY1 XG-1 8226 EJM U266 KMS-11LB SKMM-1 MM-MM1 sum 0.00.10.20.30.40.50.6 L363 OPM-1 XG-2 KMS-20 XG-1 XG-7 ANBL-6 OCI-MY1 U266 XG-6 INA-6 MOLP-8 AMO-1 KMS-34 KMS-11LB SKMM-1 MM-MM1 EJM FR4 8226 max 0.000.050.100.150.200.25 INA-6 MM-MM1 8226 XG-1 U266 ANBL-6 SKMM-1 EJM OPM-1 XG-2 OCI-MY1 KMS-20 L363 KMS-11LB AMO-1 XG-6 FR4 KMS-34 MOLP-8 XG-7 min 0.00.20.40.60.81.01.2 L363 OPM-1 XG-2 KMS-34 INA-6 KMS-11LB SKMM-1 EJM U266 MM-MM1 FR4 AMO-1 XG-6 8226 MOLP-8 ANBL-6 OCI-MY1 XG-1 KMS-20 XG-7 euc
  33. 33. •  Vargatef  exhibited  anomalous  matrix   response  compared  to  other  VEGFR  inhibitors             Exploi1ng  Polypharmacology   Vargatef   Linifanib Axitinib Sorafenib Vatalanib Motesanib Tivozanib Brivanib Telatinib Cabozantinib Cediranib BMS-794833 Lenvatinib OSI-632 Foretinib Regorafenib
  34. 34. Exploi1ng  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  
  35. 35. Predic1ng  Synergies   •  Related  to  response  surface  methodologies   •  LiUle  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  &   Yin  et  al,  PLoS  One,  2014   •  But  synergy  is  not  always  objec:ve  and  doesn’t   really  correlate  with  structure  
  36. 36. Structural  Similarity  vs  Synergy   beta gamma ssnum Win 3x3 0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.85 0.90 0.95 1.00 1.05 1.10 1.15 0.75 0.85 0.95 1.05 0 5 10 15 20 25 -40 -30 -20 -10 0 Synergy measure Similarity
  37. 37. 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  
  38. 38. 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  
  39. 39. Acknowledgements   •  Lou  Staudt   •  Beverly  Mock,  John  Simmons   •  Lesley  Griner,  Craig  Thomas,  Marc  Ferrer,   Bryan  MoU,  Paul  Shinn,  Sam  Michaels  

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