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When	
  the	
  whole	
  is	
  be-er	
  than	
  the	
  parts	
  
Analy'cs	
  for	
  high	
  throughput	
  	
  
combina'on	
...
Outline	
  
hMp://origin.arstechnica.com/news.media/pills-­‐4.jpg	
  
Why	
  combine?	
  
Physical	
  infrastructure	
  &	...
Screening	
  for	
  Novel	
  Drug	
  
Combina'ons	
  
•  Increased	
  efficacy	
  
•  Delay	
  resistance	
  
•  AMenuate	
 ...
How	
  to	
  Test	
  Combina'ons	
  
•  Many	
  procedures	
  described	
  in	
  the	
  literature	
  
– Fixed	
  dose	
  ...
Mechanism	
  Interroga'on	
  PlateE	
  •  Collec'on	
  of	
  ~	
  2000	
  small	
  molecules	
  of	
  diverse	
  
mechanis...
Combina'on	
  Screening	
  Workflow	
  
Run	
  single	
  agent	
  dose	
  responses	
  
6x6	
  matrices	
  for	
  	
  
pote...
Where	
  Are	
  We	
  Now?	
  
•  309	
  screens	
  in	
  total	
  
– 189	
  screens	
  against	
  full	
  	
  
MIPE3	
  o...
Screening	
  Challenges	
  
•  A	
  key	
  challenge	
  is	
  automated	
  quality	
  control	
  
•  Plate	
  level	
  dat...
0 5 10 15 20
MSR
Compound
10
20
30
40
Freq
QC	
  Examples	
  
•  Single	
  agents	
  with	
  very	
  high	
  MSR’s	
  coul...
Repor'ng	
  Combina'on	
  Results	
  
h-ps://tripod.nih.gov/matrix-­‐client	
  
Repor'ng	
  Combina'on	
  Results	
  
Repor'ng	
  Combina'on	
  Results	
  
•  These	
  web	
  pages	
  and	
  matrix	
  layouts	
  are	
  a	
  
useful	
  first	...
Combina'ons	
  as	
  Networks	
  
•  Combina'on	
  screens	
  lend	
  themselves	
  
naturally	
  to	
  network	
  represe...
Iden'fying	
  the	
  Most	
  Synergis'c	
  Pairs	
  
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●...
When	
  are	
  Combina'ons	
  Similar?	
  
•  Differences	
  and	
  their	
  
aggregates	
  such	
  as	
  RMSD	
  
can	
  l...
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...
Ibru'nib	
  Combina'ons	
  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...
Combina'ons	
  across	
  Cell	
  Lines	
  
•  Cellular	
  background	
  affects	
  responses	
  
•  Can	
  we	
  group	
  c...
Working	
  in	
  Combina'on	
  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	
  
	
  
	
...
Exploi'ng	
  Polypharmacology	
  
•  PD-­‐166285	
  is	
  a	
  SRC	
  &	
  
FGFR	
  inhibitor	
  
•  Lestaurnib	
  has	
  ...
Conclusions	
  
•  Use	
  response	
  surfaces	
  as	
  first	
  class	
  descriptors	
  of	
  
drug	
  combina'ons	
  
–  ...
Acknowledgements	
  
•  Craig	
  Thomas,	
  Marc	
  Ferrar,	
  Lesley	
  Mathews,	
  
Paul	
  Shin,	
  Sam	
  Michaels,	
 ...
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Transcript of "When the whole is better than the parts"

  1. 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  
  2. 2. Outline   hMp://origin.arstechnica.com/news.media/pills-­‐4.jpg   Why  combine?   Physical  infrastructure  &  workflow   Summarizing  &   exploring  the  data   ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.00.20.40.60.8
  3. 3. Screening  for  Novel  Drug   Combina'ons   •  Increased  efficacy   •  Delay  resistance   •  AMenuate  toxicity   •  Inform  signaling  pathway   connec'vity   •  Iden'fy  synthe'c  lethality   •  Highlight   polypharmacology   Transla'onal  Interest   Basic  Interest  
  4. 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. 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. 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. 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. 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. 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  
  10. 10. Repor'ng  Combina'on  Results   h-ps://tripod.nih.gov/matrix-­‐client  
  11. 11. Repor'ng  Combina'on  Results  
  12. 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  
  13. 13. Combina'ons  as  Networks   •  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
  14. 14. Iden'fying  the  Most  Synergis'c  Pairs   ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
  15. 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. 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. 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
  18. 18. Clustering  Response  Surfaces  0.00.20.40.60.8 C1  (24)   C2(47)   C3(35)   C4(24)  
  19. 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. 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. 21. Combina'ons  across  Cell  Lines   •  Cellular  background  affects  responses   •  Can  we  group  cell  lines  based  on  combina'on   response?  
  22. 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}) ,   ,   ,   ,   ,  
  23. 23. 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
  24. 24. •  Vargatef  exhibited  anomalous  matrix   response  compared  to  other  VEGFR  inhibitors             Exploi'ng  Polypharmacology   Vargatef   Linifanib Axitinib Sorafenib Vatalanib Motesanib Tivozanib Brivanib Telatinib Cabozantinib Cediranib BMS-794833 Lenvatinib OSI-632 Foretinib Regorafenib
  25. 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. 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. 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  
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