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

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

    • 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  
    • Outline   Why  combine?   Physical  infrastructure  &  workflow   Summarizing  and  exploring  the  data   hKp://origin.arstechnica.com/news.media/pills-­‐4.jpg  
    • 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  
    • 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
    • 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
    • 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
    • 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
    • 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"
    • 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
    • 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?  
    • 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
    • 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
    • Repor1ng  Combina1on  Results  
    • Repor1ng  Combina1on  Results  
    • 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?  
    • 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
    • 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  
    • 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
    • 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
    • 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
    • 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  
    • 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 34 102 286 230 178 179 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
    • 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
    • Combina1ons  across  Cell  Lines   •  Cellular  background  affects  responses   •  Can  we  group  cell  lines  based  on  combina[on   response?  
    • 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  
    • 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
    • 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
    • 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
    • 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  
    • 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
    • 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  
    • 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  
    • Acknowledgements   •  Lou  Staudt   •  Beverly  Mock,  John  Simmons