Characteriza*on	  and	  visualiza*on	  of	  compound	  combina*on	  responses	  in	       a	  high	  throughout	  se8ng	  ...
Outline	  Why	  combine?	  Physical	  infrastructure	  &	  workflow	  Summarizing	  and	  exploring	  the	  data	          ...
Screening	  for	  Novel	  Drug	                    Combina*ons	  •  Drug	  combina*ons	  offer	  advantages	  for	  both	  ...
How	  to	  Test	  Combina*ons	  •  Many	  procedures	  described	  in	  the	  literature	     –  Fixed	  dose	  ra*o	  (ak...
Scaling	  Response	  Surface	  Screening	                                                               5e+07             ...
Mechanism	  Interroga*on	  PlateE	  Top	  10	  Panther	  gene	  classes	          Top 10 Panther gene classes             ...
Combina*on	  Screening	  Workflow	  Run	  single	  agent	  dose	  responses	                                               ...
Repor*ng	  Combina*on	  Results	  
Repor*ng	  Combina*on	  Results	  
Repor*ng	  Combina*on	  Results	  •  These	  web	  pages	  and	  matrix	  layouts	  are	  a	     useful	  first	  step	  • ...
A	  Simpler	  Visual	  Summary	  •  Convert	  mul*ple	  individual	  	           1   7    13   19    25    31   heatmaps,	...
A	  Simpler	  Visual	  Summary	  1   2   3   4   5   6   7   8   9   10   11   12   13   14   15   16   17   18   19   20 ...
When	  are	  Combina*ons	  Similar?	  •  Differences	  and	  their	     aggregates	  such	  as	  RMSD	     can	  lead	  to	...
Similarity	  via	  the	  KS	  Test	  •  Quan*fy	  distance	  between	  response	     distribu*ons	  via	  KS	  test	     –...
Similarity	  via	  the	  Syrjala	  Test	            •  Syrjala	  test	  used	  to	  compare	                              ...
Datasets	  •  Primary	  focus	  is	  on	  inves*ga*ng	  combina*ons	     with	  Ibru*nib	  for	  treatment	     of	  DLBCL...
0.8             Clustering	  Response	  Surfaces	                                                               C1	  (24)	...
Cluster	  C3	  0.300.250.200.150.100.050.00           302           281           128           174           285         ...
0.080.060.040.020.00           361                                                                                        ...
Combina*ons	  across	  Cell	  Lines	  •  Cellular	  background	  affects	  responses	  •  Can	  we	  group	  cell	  lines	 ...
Working	  in	  Combina*on	  Space	  •  Each	  cell	  line	  is	  represented	  as	  a	  vector	  of	     response	  matric...
0.00    0.05     0.10      0.15   0.20     0.25              0         1         2       3          4   INA-6             ...
Exploi*ng	  Polypharmacology	  •  Vargatef	  exhibited	  anomalous	  matrix	     response	  compared	  to	  other	  VEGFR	...
Exploi*ng	  Polypharmacology	                                                                                             ...
Predic*ng	  Synergies	  •  Related	  to	  response	  surface	  methodologies	  •  LiRle	  work	  on	  predic*ng	  drug	  r...
Structural	  Similarity	  vs	  Synergy	                                           beta                                    ...
Predic*on	  Strategy	  •  Don’t	  directly	  predict	  synergy	  •  Use	  single	  agent	  data	  to	  generate	  a	  mode...
Conclusions	  •  Use	  response	  surfaces	  as	  first	  class	  descriptors	  of	     drug	  combina*ons	      –  Surroga...
Acknowledgements	  •  Lou	  Staudt	  •  Beverly	  Mock,	  John	  Simmons	  
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Characterization and visualization of compound combination responses in a high throughout setting

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Characterization and visualization of compound combination responses in a high throughout setting

  1. 1. Characteriza*on  and  visualiza*on  of  compound  combina*on  responses  in   a  high  throughout  se8ng   Rajarshi  Guha,  Lesley  Mathews,  John   Keller,  Paul  Shinn,  Craig  Thomas,  Anton   Simeonov,  Marc  Ferrar   NIH-­‐NCATS     April  7,  2013,  New  Orleans  
  2. 2. Outline  Why  combine?  Physical  infrastructure  &  workflow  Summarizing  and  exploring  the  data   hRp://origin.arstechnica.com/news.media/pills-­‐4.jpg  
  3. 3. Screening  for  Novel  Drug   Combina*ons  •  Drug  combina*ons  offer  advantages  for  both   efficacy  and  poten*al  reduc*on  of  target   related  toxici*es  •  Combina*on  studies  also  offer  insight  into   systems  level  interac*ons  
  4. 4. How  to  Test  Combina*ons  •  Many  procedures  described  in  the  literature   –  Fixed  dose  ra*o  (aka  ray)   –  Ray  contour   C5,D5 C5 –  Checkerboard   C4,D4 C4 –  Gene*c  algorithm   C3,D3 C3   C2,D2 C2 C1,D5 C1,D4 C1,D3 C1,D2 C1,D1 C1 D5 D4 D3 D2 D1 0
  5. 5. Scaling  Response  Surface  Screening   5e+07 Combination type•  Response  surfaces     All pairs Fixed library Dose matrix size 4e+07 imply  a  DxD  matrix     4 Number of combinations 6 10 for  each  combina*on   3e+07•  All  pairs  screening  is     2e+07 imprac*cal  for  more     1e+07 than  tens  of       0e+00 compounds   250 500 750 Number of compounds 1000•  Instead  we  consider  N  compounds  versus  a   fixed  size  library    
  6. 6. Mechanism  Interroga*on  PlateE  Top  10  Panther  gene  classes   Top 10 Panther gene classes 200 kinase nucleic acid binding Number of compounds 150 receptor signaling molecule transferase 100 50Top  10  enriched  GeneGo  pathway  maps   Development EGFR signaling pathway 0 Some pathways of EMT in cancer cells &D I II III ed al t Development VEGF signaling via VEGFR2 - generic cascades d en e ue e ic as e ov R as lim lin as in Ph pr Ph ec nt Ph pp Ap Apoptosis and survival Anti-apoptotic action of Gastrin co Pr Su is D 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 10 15 -log10(pValue)
  7. 7. 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"
  8. 8. Repor*ng  Combina*on  Results  
  9. 9. Repor*ng  Combina*on  Results  
  10. 10. 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  beRer  job  of  ranking  and   aggrega*ng  combina*on  responses  taking   into  account   –  Response  matrix   –  Compounds,  targets  and  pathways  
  11. 11. A  Simpler  Visual  Summary  •  Convert  mul*ple  individual     1 7 13 19 25 31 heatmaps,  to  a  single  heatmap    2 3 8 9 14 15 20 21 26 27 32 33 by  unrolling  response  matrices   4 10 16 22 28 34•  Examine  effects  of  A  at  fixed   5 6 11 12 17 18 23 24 29 30 35 36 concentra*ons,  on  dose  response   of  B   {1, 2, 3, 4, …, 34, 35, 36}•  Zoom  in  on  combina*ons  that  show  extensive   ac*vity  throughout  the  dose  matrix  
  12. 12. A  Simpler  Visual  Summary  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Concentration Combination
  13. 13. When  are  Combina*ons  Similar?  •  Differences  and  their   aggregates  such  as  RMSD   can  lead  to  degeneracy   0.06•  Instead  we’re  interested  in   0.04 0.010 the  shape  of  the  surface   0.005 0.02 0.00 0.000 0 25 50 75 100 0 25 50 75 100•  How  to  characterize  shape?   0.15 –  Parametrized  fits   0.10 0.05 –  Distribu*on  of  responses   0.00 0 50 100 D, p value
  14. 14. Similarity  via  the  KS  Test  •  Quan*fy  distance  between  response   distribu*ons  via  KS  test   –  If  p-­‐value  >  0.05,  we  assume   9 distance  is  0  •  But  ignores  the  spa5al   density 6 distribu*on  of  the  responses   3 on  the  concentra*on  grid   0 0.00 0.25 0.50 0.75 1.00 D
  15. 15. Similarity  via  the  Syrjala  Test   •  Syrjala  test  used  to  compare   10.0 popula*on  distribu*ons   over  a  spa*al  grid   7.5 –  Invariant  to  grid  orienta*on   density 5.0 –  Provides  an  empirical  p-­‐value   2.5 •  Less  degenerate  than  just   considering  1D  distribu*ons   0.0 0.00 0.25 0.50 0.75 DSyrjala,  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  
  16. 16. Datasets  •  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    
  17. 17. 0.8 Clustering  Response  Surfaces   C1  (24)   0.6 0.4 C3(35)   C2(47)   0.2C4(24)   0.0
  18. 18. Cluster  C3  0.300.250.200.150.100.050.00 302 281 128 174 285 153 177 210 144 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 macromolecule catabolic process regulation of interferon-gamma-mediated signaling pathway •  Vargatef,  vorinostat,   ubiquitin-dependent protein catabolic process cellular process involved in reproduction flavopiridol,  …   negative regulation of cell cycle peptidyl-amino acid modification •  Not  par*cularly   interphase specific  given  the   cell cycle checkpoint range  of  primary   peptidyl-tyrosine phosphorylation response to stress targets   0 1 2 3 -log10(Pvalue)
  19. 19. 0.080.060.040.020.00 361 Cluster  C4   254 215 164 143 82 125 327 241 194 145 116 139 371 163 165 384 339 322 217 184 150 52 136 cellular carbohydrate biosynthetic process regulation of polysaccharide biosynthetic process cellular macromolecule localization •  Focus  on  sugar   peptidyl-serine phosphorylation metabolism     regulation of generation of precursor metabolites and energy •  Ruboxistaurin,   cellular polysaccharide metabolic process cycloheximide,  2-­‐ glucan metabolic process methoxyestradiol,  …   glucan biosynthetic process regulation of glycogen biosynthetic process •  PI3K/Akt/mTOR   glycogen metabolic process signalling  pathways   0 1 2 3 -log10(Pvalue)
  20. 20. Combina*ons  across  Cell  Lines  •  Cellular  background  affects  responses  •  Can  we  group  cell  lines  based  on  combina*on   response?  
  21. 21. Working  in  Combina*on  Space  •  Each  cell  line  is  represented  as  a  vector  of   response  matrices   L 1   L2  •  “Distance”  between  two     ,   =  d1   cell  lines  is  a  func*on  of  the   distance  between  component   ,   =  d2   response  matrices   ,   =  d3     D ( L1, L2 ) = F({d1, d2 ,…, dn }) ,   =  d4    •  F  can  be  min,  max,  mean,  …     ,   =  d5  
  22. 22. 0.00 0.05 0.10 0.15 0.20 0.25 0 1 2 3 4 INA-6 KMS-34 MM-MM1 INA-6 min sum 8226 L363 XG-1 OPM-1 U266 XG-2 ANBL-6 FR4 SKMM-1 AMO-1 EJM XG-6 OPM-1 MOLP-8 XG-2 ANBL-6 OCI-MY1 KMS-20 KMS-20 XG-7 L363 OCI-MY1KMS-11LB XG-1 AMO-1 8226 XG-6 EJM FR4 U266 KMS-34 KMS-11LB MOLP-8 SKMM-1 XG-7 MM-MM1 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.1 0.2 0.3 0.4 0.5 0.6 L363 L363 OPM-1 OPM-1 euc max XG-2 XG-2 KMS-34 KMS-20 INA-6 XG-1KMS-11LB XG-7 SKMM-1 ANBL-6 EJM OCI-MY1 U266 U266 MM-MM1 XG-6 FR4 INA-6 AMO-1 MOLP-8 XG-6 AMO-1 Many  Choices  to  Make   8226 KMS-34 MOLP-8 KMS-11LB ANBL-6 SKMM-1 OCI-MY1 MM-MM1 XG-1 EJM KMS-20 FR4 XG-7 8226
  23. 23. Exploi*ng  Polypharmacology  •  Vargatef  exhibited  anomalous  matrix   response  compared  to  other  VEGFR  inhibitors     Linifanib Axitinib Sorafenib Vatalanib     Motesanib Tivozanib Brivanib Telatinib   Cabozantinib Cediranib BMS-794833 Lenvatinib   OSI-632 Foretinib Regorafenib Vargatef  
  24. 24. Exploi*ng  Polypharmacology   Vargatef DCC-2036 PD-166285 GDC-0941 •  PD-­‐166285  is  a  SRC  &   FGFR  inhibitor   PI-103 GDC-0980 Bardoxolone methyl AT-7519 AT7519 •  Lestaurnib  has     ac*vity  against  FLT3   SNS-032 NCGC00188382-01 Lestaurtinib CNF-2024 Src Lyn Lck ISOX Belinostat PF-477736 AZD-7762 Flt-3 PDGFRb PDGFRa FGFR-4 FGFR-3 FGFR-2 Chk1 IC50 = 105 nM FGFR-1 VEGFR-3 VEGFR-2 VEGFR-1 0 200 400 600 Potency (nM)Hilberg,  F.  et  al,  Cancer  Res.,  2008,  68,  4774-­‐4782  
  25. 25. Predic*ng  Synergies  •  Related  to  response  surface  methodologies  •  LiRle  work  on  predic*ng  drug  response  surfaces   –  Peng  et  al,  PLoS  One,  2011   –  Jin  et  al,  Bioinforma5cs,  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  
  26. 26. Structural  Similarity  vs  Synergy   beta gamma 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1Similarity 0.85 0.90 0.95 1.00 1.05 1.10 1.15 0.75 0.85 0.95 1.05 ssnum Win 3x3 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 5 10 15 20 25 -40 -30 -20 -10 0 Synergy measure
  27. 27. Predic*on  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  •  Will  likely  be  beRer  addressed  by  (also)   considering  target  connec*vity    
  28. 28. 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  
  29. 29. Acknowledgements  •  Lou  Staudt  •  Beverly  Mock,  John  Simmons  

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