Your SlideShare is downloading. ×
Nick Dracopoli Shanghai Bioforum 2012-05-11
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Nick Dracopoli Shanghai Bioforum 2012-05-11

340
views

Published on

Nic Dracopoli, May 11, 2012. Shanghai Bioforum Translational Medicine, Session S4, Shanghai, China

Nic Dracopoli, May 11, 2012. Shanghai Bioforum Translational Medicine, Session S4, Shanghai, China

Published in: Health & Medicine

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
340
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Biomarkers  and  Companion  Diagnos1c  Applica1ons   in  Oncology  Drug  Development   Nicholas  C.  Dracopoli,  Ph.D.   Vice  President,  Head  Oncology  Biomarkers   Janssen  R&D   Johnson  &  Johnson   Shanghai,  China   May  10,  2012  
  • 2. Empirical  Drug  Development  Strategies  are   Unsustainable    •  Overall  aLri1on  rates  are  too  high  during  development:   –  Poor  in  vivo  and  in  vitro  disease  models  lead  to  failure  early  in   development   –  Too  many  compound  fail  for  lack  of  efficacy  late  in  development  •  Disease  heterogeneity  means  too  few  pa1ents  respond  to   any  one  therapeu1c  approach:   –  Need  beLer  markers  to  monitor  status  of  the  drug  target  and   cognate  pathway    •  Development  costs  for  novel  drugs  with  low  response  rates   are  too  high:   –  Large  Phase  III  trials  required  to  demonstrate  clinical  benefit   –  High  risk  of  registra1onal  failure   –  Length  of  1me  required  to  show  overall  survival  benefit  
  • 3. New  Drug  Approvals  in  US:  1996-­‐2010   Mullard, A. (2011) 2010 FDA drug approvals, Nature Reviews Drug Discovery 10:82-85
  • 4. ALri1on  in  Drug  Development:  2009  •   Overall  clinical  success  (Phase  I  entry   to  approval)  has  risen:   •  2004  es1mate:  11%   •  2009  es1mate:  18%  •   Companion  diagnos1cs  have  impacted   approval  of  some  kinase  inhibitors:   –  cKIT  for  ima1nib  (GIST)   –  KRAS  for  panitumumab  (colorectal  cancer)   –  HER2  for  lapa1nib  (breast  cancer)  •  Clinical  success  for  kinase  inhibitors  is   ~2.5-­‐fold  higher  than  the  overall   average:   •  How  much  of  this  is  due  to  undifferen1ated   fast  follow  on  compounds?   •  Has  the  transi1on  from  cytotoxic  to  targeted   therapies  reduced  overall  aLri1on?   •  How  much  is  this  due  to  precedented   chemistry  and  biology  for  kinase  inhibitors?   Walker & Newell, 2009
  • 5. Biomarkers  in  Drug  Development  Marker Func*on TestPD/MOA •  Determine  whether  a  drug  hits  the  target  and  has   •  Research  test  used  during  drug   impact  on  the  biological  pathway   development   •  Evaluate  mechanism  of  ac1on  (MOA)   •  Not  developed  as  companion   diagnos1c •  PK/PD  correla1ons  and  determine  dose  and  schedule   •  Determine  biologically  effec1ve  dosePredic1ve •  Iden1fy  pa1ents  most  likely  to  respond,  or  are  least   •  Companion  diagnos1c  test  (e.g.   likely  to  suffer  an  adverse  event  when  treated  with  a   hercep1n,  EGFR) drug.Resistance •  Iden1fy  mechanisms  driving  acquired  drug  resistance •  Muta1on  analyses  (e.g.  Bcr-­‐Abl   muta1on  in  ima1nib  treated   CML)Prognos1c •  Predicts  course  of  disease  independent  of  any  specific   •  Approved  tests  (e.g.  CellSearch,   treatment  modality Mammaprint)Surrogate •   Approved  registra1onal  endpoints •  Commercial  diagnos1c  tests  (e.g.   LDL,  HbA1c,  viral  load,  blood   pressure)
  • 6. The  Biomarker  Hypothesis  •  Biomarkers  will:   –  Reduce  development  1me  for  ac1ve  compounds   –  Accelerate  failure  of  unsafe  or  inac1ve  compounds   –  Reduce  average  development  costs  for  approved   compounds   –  Lead  to  beLer  outcomes  for  cancer  pa1ents  •  The  costs  for  biomarker  research  will  be  more  than   compensated  by  increased  efficiency  of  the  drug   development  process:   –  Early  at-­‐risk  investment  in  biomarkers  leads  to  more   approved  compounds  with  beLer  pa1ent  outcomes  and   stronger  cases  for  reimbursement  
  • 7. The  Biomarker  Paradox      There  are  11,166  biomarkers  listed  in  GOBIOM  database   (01/31/2011)     -­‐  BUT  -­‐   only  32  valid  genomic  biomarkers  in  FDA    approved  drug   labels   -­‐  AND  -­‐  0  are  mul1plex  IVD’s    based  on  proteomic  or  genomic  profiles  
  • 8. Protein  Kinase  Inhibitors:  A  Model  for  Biomarker  Development  in  Oncology  •  216*  protein  kinase  drugs  in  Phase  II  or  III  for  cancer  indica1ons  (23%):   –  Most  common  cancer  drugs  in  oncology  development  (23%*)   –  2nd  most  common  drug  class  aker  G-­‐protein  coupled  receptors  (GPCR)  in  all  indica1ons  •  12  drugs  approved  by  FDA  for  cancer  indica1ons  that  target  receptor   tyrosine  kinases  (RTK):   –  7  have  predic1ve  markers  in  the  drug  label   –  No  other  cancer  drug  classes  have  predic1ve  markers  in  their  labels  when  launched  •  Biomarkers  are  required  for  RTK  drug  development  to:   –  Predict  dependency  on  specific  signaling  pathways   –  Screen  for  acquired  drug  resistance   –  Monitor  pathological  changes  during  disease  progression   *The Beacon Group, 2010
  • 9. Targeted  Therapy  with  Tyrosine  Kinase  Inhibitors    Mul1ple  druggable   approaches  to  inhibi1ng   protein  kinase  signaling:   –  Reduce  ligand  –  bevacizumab   (Avas1n)  binds  VEGF  and  reduces   ligand-­‐dependant  receptor   ac1va1on   –  Block  receptor  –  cetuximab   (Erbitux)  blocks  EGFR  and   prevents  ligand-­‐induced  receptor   ac1va1on   –  Inhibit  intracellular  kinase  –   erlo1nib  (Tarceva)  inhibits  the   intracellular  phosphoryla1on  of   Ciardiello & Tortora, New Engl. J. Med. 358:1160, 2008 EGFR  kinase    
  • 10. Signal  Transduc1on  Pathways  are  Ini1ated   by  Mul1ple  Pathological  Events     A: Normal signal B: Activate intracellular Transduction Kinase (mutation or translocation) C: Mutate intermediate D: Receptor gene pathway member amplification (e.g. KRAS) E: Increase ligand F: Utilize alternative expression Receptor (e.g. MET)
  • 11. Approved  Companion  Diagnos1cs:  2011  Markers Direct  Markers Secondary   Molecular   Markers Profiles*Readout Drug  target  status Downstream   Consolidated   pathway profilesExamples HER2+   KRAS  wt ER+   CD20+   BCR-­‐ABL  (Ph+)   KIT+   EGFR+   BRAF   EML4-­‐ALK  
  • 12. Companion  diagnos1cs:  KRAS  in   colorectal  cancer   Karapetis et al., 2008    Predic1ve  values  of  KRAS  muta1ons  in   colorectal  cancer  (Raponi  et  al.,  2008)  :   –   35%  PPV   –  97%  NPV  
  • 13. No  IVDMIA  Tests  Approved  as   Companion  Diagnos1cs  Test   Company   Companion   Prognos*c   Diagnos*c   Test  Mammaprint   Agendia   No   Yes  Tumor  of   Pathwork   No   Yes  Unknown  Origin   Diagnos1cs  Allomap   XDx   No   Yes  An IVDMIA is a device that combines the values of multiple variables using aninterpretation function to yield a single, patient-specific result that is intended for usein the diagnosis of disease or other conditions, or in the cure, mitigation, treatment orprevention of disease and provides a result whose derivation is non-transparent andcannot be independently derived or verified by the end user.Draft Guidance for Industry, Clinical Laboratories, and FDA staff – Multivariate Index Assays (Rockville, MD:FDA, Center for Devices and Radiologic Health, 2007)
  • 14. Efficacy  Biomarker  Discovery  &   Valida1on   Phase  I   Phase  I   Pre-­‐ Post-­‐ Dose   Extension   Phase  II   Phase  III   Clinical   Launch   Escala1on   at  MTD   N 0 0 >30 >80 >200Simple in  vivo  &   2nd  Biomarker 1st     1st   Valida1on   in  vitro  (e.g. BRAF Training    Valida1on   &   models   Registra1on  V600E) 2nd   in  vivo  &  Molecular 1st   1st   1st   Valida1on   in  vitro   Training   Training   Valida1on   &    Profile models   Registra1on   N: # patients treated at or above biological effective dose
  • 15. Biomarkers  for  Oncology  Targeted   Therapies  PD/MOA Biomarkers Predictive BiomarkersCD3,  CD4,  CD5,  CD8,  CD19,CD20,  CD41,  IgA,  IgM,  IgG,  Estradiol,  Estrone,  Estrone  sulfate,  soluble  HER2,  PET  tratsuzumab,  Testosterone,  Androstenedione,  SHBG,  plasma  HDL,  Albumin,  Treg,  CD8,  CBC,  CD4+,  Caspase  3-­‐9,  Bcl2,  PDGFR,  cKIT,  ER,  PR,  Ki67,  pS2,  IgA,  IgG,  IgM,  IgG,  IgA,  IgM,  20S  proteasome,  EGFR,  pEGFR,  Ki67,p27,   Ph+,  KRAS,  pMAPK,  AKT,  pAKT  ,  kera1n  1,  STAT3,   EGFR,  KIT,  VEGF,  FDG-­‐PET,  CT,  DCE-­‐MRI,  plasma  PLG,   HER2,  CECs,  EGFR,  pEGFR,  Ki67,p27,  TGFalpha  ,   BRAF,  ALK  amphiregulin,  epiregulin,  EGFRvIII,  MEK,  ERK1,  pERK1,  ERK2,  pERK2,  ac1n,  Acetylated  H3,  H4,  HDAC2-­‐6,  TopoIIa,  HP1,    KRAS,  SRC,  pSRC,  pBCR/ABL,  pCRKL,    IGFR1,  pS6,  TGF-­‐alpha,  p95,  4EBP1,  p4E-­‐BP1,  eIF-­‐4G,  S6,  pS6,  IDO,  TNFalpha,  ……………..  
  • 16. Oncology  CoDx:  Nine  Drugs  Against  Six  Targets  Date   Drug   Markers   FDA  Oncology  Approvals   6  1998   trastuzumab   HER2  2007   lapa1nib   HER2,  EGFR   5  2001   ima1nib   BCR-­‐ABL,  KIT   4  2006     dasa1nib   BCR-­‐ABL   3  2007   nilo1nib   BCR-­‐ABL  2004   cetuximab   KRAS   2  2006   panitumumab   KRAS   1  2011   crizo1nib   EML4-­‐ALK   0  2011   vemurafenib   BRAF   No  CoDx   With  CoDx  
  • 17. Oncology  Drug  Approvals:  Room  for  Improvement   Hazard Ratio (HR) in randomized, controlled trial supporting 1st approved indication (data from www.fda.gov)•  >500  targeted  therapies  in  clinical   development   1.00   Marker  +’ve  only   –  <10%  of  therapies  entering  Phase  1  tes1ng   0.90   will  eventually  achieve  regulatory  approval   Allcomers   0.80  •  Most  recently  approved  Oncology   0.70   drugs  have  only  modest   0.60   improvements  in  hazard  ra1os  (HR)   0.50  •  Effec1ve  targe1ng  of  tumors  with   0.40   predic1ve  markers  significantly   0.30   improves  HR  in  defined  subsets:   0.20   –  BRAF  muta1on  in  melanoma   0.10   –  EML4-­‐ALK  transloca1on  in  NSCLC   0.00   Gleevec   Tykerb   Zactema   Sutent   Zelboraf   Votrient   Zy1ga   Erbitux   Provenge   Avas1n   Tarceva   Iressa   Torisel   Hercep1n   Yervoy   Nexavar   Afinitor  
  • 18. Biomarkers  Can  be  the  Difference  in   Eventual  Approval  of  New  Drugs   Probability of Success MOA  poorly   MOA  well   understood   understood   Available   clinical   15%   75%   biomarker   No  clinical   biomarker   5%   35%   Adapted from E. Zerhouni – with permission
  • 19. Conclusion  •  Clinical  innova1on  always  takes  longer  than  expected:   –  Biomarkers  are  no  excep1on!   –  Diseases  are  complex  and  individual  biomarker  effect  sizes  are  oken  too  small  •  Biomarker  science  is  the  major  cause  of  the  delay:   –  When  important  markers  emerge  (e.g.  crizo1nib,  vemurafenib),  regulatory  authori1es  have  adapted   quickly  and  adjusted  previous  requirements  to  include  them  in  the  drug  labels   –  We  have  been  much  more  successful  with  PD/MOA  than  predic1ve  biomarkers   –  To  date,  we  have  largely  failed  to  develop  complex  molecular  profiles  as  useful  predic1ve  markers  •  Companion  diagnos1cs  will  remain  rare  un1l  we  can  develop  more  biomarkers  with:   –  Strong  predic1ve  values   –  Evidence  they  are  predic1ve  not  prognos1c   –  Available  “fit-­‐for-­‐purpose”  assays   –  Ac1onable  data  •  To  be  successful,  we  must  change  the  way  we  implement  biomarker  research  in   pharmaceu1cal  development:   –  Implement  biomarker  work  much  earlier  in  the  development  plan   –  Modify  clinical  trial  design  to  enable  biomarker  discovery  valida1on   –  Demonstrate  that  biomarker  data  improves  the  drug  development  process