Applying Biomarker Information To Support Drug Discovery ... - IIR

Applying Biomarker Information
To Support Drug Discovery &
Development
Rick Xu, MD, Ph.D.
Department of Clinical Pharmacology
Hoffman-La Roche Inc
September, 2006

Biomarker & Clinical Endpoint
Biomarker:
A characteristic that is objectively
measured & evaluated as an indicator of
normal biologic, pathologic processes,
or pharmacologic responses to a
therapeutic intervention (NIH workshop)
Values:
• understand of mechanism of action
• reduce the sample size and duration
• less variable & reproducible
• help trial design
Challenges:
Results may not be relevant or be able
to predict clinical outcomes
Clinical Endpoint:
A characteristic or variable that
reflects how a patient feels or
functions, or how long a patient
survives (NIH workshop)
Values:
• direct evidence to assess clinical
outcomes
Challenges:
• cost
• duration

Definition:
Surrogate Endpoint
A biomarker intended to substitute for a clinical endpoint. It is expected to
predict clinical benefit, harm, or lack of benefit or harm (NIH work shop)
Values:
• understand of mechanism of action
• reduce the sample size and duration
• less variable & reproducible
• be able to predict clinical outcomes
Challenge:
identify necessary evidence to establish a surrogate endpoint for a particular
treatment clinical endpoint.
Opportunity:
put effort to investigate the high degree surrogacy information √

The Significance of High Degree
Surrogacy Information
Predicted vs Observed Clin. Resp. with PEG-IFN (5KD)
(data based upon 24 week treatment)
ALT Normalization (%)
150
100
50
0
Observation
Prediction
3M 6M 9M 3M 6M
qw qw qw biw tiw
Predicted Clin. Resp. (%)
r 2 = 0.99
p < 0.001
Observed Clinical Response (%)

Develop a New Therapy
• Strong Medical Needs
> 200 million patients
Lack of effective therapy (

PK/PD Criteria for a Target Molecule
IFN 3MIU; tiw
PEG 2-fold half-life
Serum OAS (% of Baseline)
PEG 5-fold half-life
PEG 8-fold half-life
0 800 0 800
Time (hours)

PK Profiles of IFN / PEG-IFN in Rats
1000000
10000
1000000
IFN PEG 1
10000
100
100
Serum Conc. (U/ml)
1
0 6 12 18 24
1000000
*PEG-IFN
10000
100
1
0 10 20 30 40 50
1000000
1
0 6 12 18 24
1000000
**PEG-IFN
10000
100
1
0 10 20 30 40 50
1000000
10000
PEGSYS
10000
***PEG-IFN
100
100
1
0 6 12 18 24
1
0 6 12 18 24
Time (hours)

Immunogenicity of PEG-IFN and IFN in Mice
Antibody Titer a
Treatment Median Range
IFN (monomeric) 120 11 - 427
IFN (aggregates) 2133 400 - 38,400
PEG-IFN 0 0 - 57
a
using cell proliferation assay with 20-fold dilution

Comparison of Pharmacokinetic Profiles
between PEG-IFN and IFN
1000
Serum Conc. (U/ml)
100
10
IFN 3 MIU
PEG-IFN 270 μg
1
0 50 100 150 200
Time (hr)

Pharmacodynamic Response Increased Following
PEG-IFN s.c. Administration
1200
Serum OAS (% of baseline)
600
0
PEG2 135 mcg
PEG2 45 mcg
Rof. 3 MIU
PEG2 270mcg
Rof. 18 MIU
0 50 100 150 200 250 300
Time (hr)

Good Correlation between Predicted and
Observed Trough OAS Level in Human
OAS Trough Level ( % of Roferon 3 MIU)
350
300
250
200
150
100
50
0
Rof 3miu
PEG2 Predicted
PEG2 Observed
45 μg 135 μg 270 μg

D-E-AE Relationship Following S.C. Treatment
In Healthy Subjects
PD / Response Rate (%)
100
75
50
25
0
Phase I
135 μg 180 μg
G-III Neutropenia
100
75
50
25
0
Probability (%)
0 100 200 300
Dose (μg)

Good Correlation between Model Predicted and
Clinically Observed Response Rate
ALT or PCR Normalization (%)
100
75
50
25
0
Predicted
Observed
45 μg 90 μg 180 μg 270 μg

D-E-AE Relationship Following S.C. Treatment
PD / Response Rate (%)
100
75
50
25
0
Phase I
135 μg 180 μg
100
75
Phase II
180 μg
135 μg
50
25
ANC< 750
G-III Neutropenia
0
0 100 200 300 0 100 200 300
Dose (μg)
Probability (%)

Good Correlation between Model Predicted &
Clinically Observed Response Rate
ALT or PCR Normalization (%)
100
75
50
25
0
Predicted
Observed
45 μg 90 μg 180 μg 270 μg

Significance of Critical Reasoning
Effects of rolipram in aged mice
• Improvement in spatial memory
• Improvement in LTP is
• Attenuation of impairment in spatial memory
PKA signaling plays key role in memory process

Findings of PKA Signaling Pathway
Pre-Clinical Information
• High potency in multiple animal
models (all studies were done
in rodents)
• good or acceptable PK
• good or defendable Tox.
package
• No QT issues
• Clean or defendable safety
pharmacology
Human Experiences
• Peripheral indications ( +, ?)
• biomarker
• clinical endpoint
• Central Effect indications (-, ?)
• biomarker
• clinical endpoint
• manageable safety / tolerability
• good or manageable PK

Anatomic Differences in Brain between Rodent & Primate
NA: Nucleus accumbens
(reward-motivated actions)
Amyg: Amygdala
(affective regulation of memory)
HIP: Hippocampus
(memory consolidation)
PFC: prefrontal cortex
(working memory and executive function)
Amy F.T. Arnsten et al. Trends in Mol. Med.

Mechanistic Pharmacology Platform in Support Drug
Candidate Selection – Cognition Improvement (CNS)
• Screen Model testing
NOR-test : rodent
• Agnosia Model Testing
NOR- aged rodent
NOR- scopolamine challenge: rodent.
• Spatial Memory Testing
MWM- aged rodent
MWM- scoploamine challenge: rodent and/or primate
• Executive Function Testing
DMTP- scopolamine challenge (rodent)
DMTP- aged primate: primate

Apply Biomarker Information to Define
Candidate Profiles at Early Stage
Develop Long-acting Therapy
• Business
‣Multi-billion dollar market
‣Current situation
• Medically:
‣May help reduce the infection at
dosing site (s.c. qd)
‣Once/week convenience
• PEGSYS success
Target Profile
• Once weekly dosing for
‣Mylosuppressive Neutropenia
‣PBPC
• Efficacy
Equal or better than Filgrastim
• Safety
Comparable safety

Graphic Illustration -- Mechanism of Action
Stem Cell
Myeloblast
Bone
in circulation
Polymorphonuclear
Neutrophils
Promyelocyte
Stimulation
G-CSF
GM-CSF
Stimulation
Neutrophil
myelocye
Organs & Tissues (extra-vascular site)
Polymorphonuclear
Neutrophils
Polymorphonuclear
Neutrophils

Proposed PK/PD Model of the Effect of
PEG-G / G-CSF on Neutrophil
k a
Plasma PEG-G / G-CSF Conc. & Vd
k e
Stem
Cell
S1
k g
‘E max
‘EC 50
‘γ
M
τ
WBC
(In circulation)
E max
EC 50
γ
k in
k out
S2
WBC
Extra vascular
site

Model Prediction vs Literature Values
60
: Result from clinical study
:
Results of Model predication
30
0
0 5 29 53 77 101 125
Time (hr)

Rationale of Defining Candidate Profiles
Plasma Conc. (ng/ml)
1000
100
10
1
0.1
0.01
EC50
half-Vd double alpha-HL
double beta-HL
0 20 40 60 80
Time (hr)

Effect of PEG-G & Filgrastim on ANC Induction
6 0 0 0 0
30 μg/kg
4 0 0 0 0
2 0 0 0 0
Red Line: PEG-G
single dose
ANC (/mm 3 )
0
6 0 0 0 0
4 0 0 0 0
2 0 0 0 0
0
6 0 0 0 0
0 5 0 1 0 0 1 5 0 2 0 0
60 μg/kg
0 5 0 1 0 0 1 5 0 2 0 0
100 μg/kg
Blue line: Filgrastim
10 μg/kg qd.
Black line: Placebo
4 0 0 0 0
2 0 0 0 0
0
0 5 0 1 0 0 1 5 0 2 0 0
6 0 0 0 0
150 μg/kg
4 0 0 0 0
2 0 0 0 0
0
0 5 0 1 0 0 1 5 0 2 0 0

Mean CFU-GM Colony Formation Profiles Following
PEG-G or Filgrastim S.C in Healthy Subjects
300
300
CFU-GM (10 -6 cells)
30 μg/kg 60 μg/kg
200
100
0
0 50 100 150 200 250 300
300
200
200
200
100
0
0 50 100 150 200 250 300
300
100 μg/kg 150 μg/kg
Red Line: PEG-G, SD
Blue line: F 5 μg/kg, qd
Green line: F 10 μg/kg, qd
100
100
0
0 50 100 150 200 250 300
0
0 50 100 150 200 250 300
Time (hours)

Mean BFU-E Colony Formation Profiles Following PEG-
G or Filgrastim S.C in Healthy Subjects
BFU-E (10 -6 cells)
300
200
100
0
300
200
100
30 μg 60 μg
0 50 100 150 200 250
0 50 100 150 200 250
100 μg
300
200
100
0
300
200
100
150 μg
Red Line: PEG-G, SD
Blue line: placebo
Green line: F 10 μg/kg, qd
0
0 50 100 150 200 250
0
0 50 100 150 200 250
Time (hours)

SUMMARY
• Biomarker: same principles with new name
broader coverage and Sig. impact
• Values of having high degree surrogacy
information
• Benefit of being Critical
• Importance of setting up adequate assumptions
• Tool: Modeling & simulation

Acknowledgment
PEGSYS Project:
PKA signaling pathway
PEG-GM-CSF Project:
Pascal Bailon
Dennis Keith
Mary Graves
Michael Brenda
Alicia Palleroni
Ann Goldstein
Indra Patel
Peter VanBrummelen
Stephane Dickenson
Joseph Hoffman
Simon Pedder
……..
Rudy Schreiber
Robert England
Dennis Deptula
Elizabeth Ashford
Pascal Bailon
Eric Platza
Terry Hays
Indra Patel