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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	
  
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	
  
New	
  Drug	
  Approvals	
  in	
  US:	
  1996-­‐2010	
  




       Mullard, A. (2011) 2010 FDA drug approvals, Nature Reviews Drug Discovery 10:82-85
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
Biomarkers	
  in	
  Drug	
  Development	
  
Marker       Func*on                                                                                  Test

PD/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	
  dose
Predic1ve    •  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)
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	
  
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	
  
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
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	
  	
  
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)
Approved	
  Companion	
  Diagnos1cs:	
  2011	
  
Markers        Direct	
  Markers        Secondary	
      Molecular	
  
                                        Markers          Profiles*
Readout        Drug	
  target	
  status Downstream	
     Consolidated	
  
                                        pathway          profiles
Examples       HER2+	
                  KRAS	
  wt
               ER+	
  
               CD20+	
  
               BCR-­‐ABL	
  (Ph+)	
  
               KIT+	
  
               EGFR+	
  
               BRAF	
  
               EML4-­‐ALK	
  
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	
  
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 an
interpretation function to yield a single, patient-specific result that is intended for use
in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment or
prevention of disease and provides a result whose derivation is non-transparent and
cannot 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)
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                >200
Simple       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
Biomarkers	
  for	
  Oncology	
  Targeted	
  
              Therapies	
  
PD/MOA Biomarkers                                                          Predictive Biomarkers
CD3,	
  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,	
  
……………..       	
  
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	
  
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	
  
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
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	
  

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Nick Dracopoli Shanghai Bioforum 2012-05-11

  • 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 Test PD/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  dose Predic1ve •  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 profiles Examples 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 an interpretation function to yield a single, patient-specific result that is intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment or prevention of disease and provides a result whose derivation is non-transparent and cannot 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 >200 Simple 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 Biomarkers CD3,  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