This document discusses the use of biomarkers and companion diagnostics in oncology drug development. It notes that current drug development strategies have high failure rates. Biomarkers may help increase efficiency by identifying patients most likely to respond to a drug or experience adverse effects. Protein kinase inhibitors are highlighted as a model for biomarker development, as many approved kinase inhibitors require predictive biomarkers. While over 11,000 biomarkers have been identified, only 32 are approved in drug labels, suggesting challenges remain in applying biomarkers.
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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)
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
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