The Application of Translational Medicine Andrew … Application of Translational Medicine... · 1...
Transcript of The Application of Translational Medicine Andrew … Application of Translational Medicine... · 1...
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The application of Translational The application of Translational Medicine in clinical developmentMedicine in clinical development
Professor Andrew Hughes MRCP, PhD, FFPM
Worldwide Director Early Oncology Clinical Development, AstraZeneca
Chair of Translational Medicine, University of Manchester
AstraZeneca Pharmaceuticals,
Alderley Park, Macclesfield. SK10 4TG
Tel: 01625 512092
Fax: 01625 585626
e-mail: [email protected]
PIPMG November 25th 2009
Yesterday: Cytotoxic Drug Development
Phase I
Multiple Ascending Dose
Patients
FTIM
EOP2(Subpart H)
NDA
EOP1
Phase IPhase I Phase IIPhase II Phase IIIPhase III
Post cycle 4
Baseline Post cycle 2
Post cycle 6
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Yesterday: Cytotoxic Drug Development
Phase I
Multiple Ascending Dose
Patients
FTIM
EOP2(Subpart H)
NDA
EOP1
Phase IPhase I Phase IIPhase II Phase IIIPhase III
Phase II
Single arm
MTD- Tumour hunting
Phase II
Single arm
MTD- Tumour hunting
Phase II
Single arm
MTD- Tumour hunting
Yesterday: Cytotoxic Drug Development
Phase I
Multiple Ascending Dose
Patients
FTIM
EOP2(Subpart H)
NDA
EOP1
Phase IPhase I Phase IIPhase II Phase IIIPhase III
Phase II
Single arm
MTD- Tumour hunting
Phase II
Single arm
MTD- Tumour hunting
Phase II
Single arm
MTD- Tumour hunting
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Yesterday: Cytotoxic Drug Development
Phase I
Multiple Ascending Dose
Patients
Phase III
Pivotal registration Trial
Randomised Controlled
FTIM
EOP2(Subpart H)
NDA
EOP1
Phase IPhase I Phase IIPhase II Phase IIIPhase III
Phase II
Single arm
MTD- Tumour hunting
Phase II
Single arm
MTD- Tumour hunting
Phase II
Single arm
MTD- Tumour hunting
Median TTP (months)
Anastrozole
(n=305)
6.410.7
Tamoxifen
(n=306)
Anastrozole
Tamoxifen
p=0.022 (2-sided)*
0 6 12 18 24 30 36 42
Time to progression (months)
Perc
en
tag
e n
ot
pro
gre
ss
ed
0
10
20
30
40
50
60
70
80
90
100
Today: Targeted Therapies*
Phase I
Multiple Ascending Dose
Patients
FTIM
EOP1
Phase IPhase I
Post cycle 4
Baseline Post cycle 2
Post cycle 6
*Molecular Based Therapies/Novel Therapies
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Today: Targeted Therapies
Phase I
Multiple Ascending Dose
Patients
FTIM
EOP1
Phase IPhase I
Tumour 160 choices
Dose 3 choices
Schedule 6 choices
~120,000 opportunities ~120,000 opportunities to get it wrongto get it wrong
Combination 20 choices
PatientSelection
2 choices
Lead
Identification
Target
Identification
Hit
Identification
Lead
Optimisation
Discovery
Medicine
Development
for LaunchLaunch
Product Maintenance &
Life Cycle Support
•What dose making biomarker? •What schedule?
•What disease type?
•Personalised medicine (predictive biomarker)
•Combination strategy
Translational Medicine
Understand how drugs work in man
Generating plausible and testable scientific hypothesesto address the perennial development questions
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Lead
Identification
Target
Identification
Hit
Identification
Lead
Optimisation
Discovery
Medicine
Development
for LaunchLaunch
Product Maintenance &
Life Cycle Support
•What dose making biomarker? •What schedule? •What disease type?•Personalised medicine (predictive biomarker)
•Combination strategy
Translational Medicine
Understand how drugs work in man
Generating plausible and testable scientific hypothesesto address the perennial development questions
Pharmacological Biomarkers(Pharmacodynamic biomarkers)
• Proof of Target Effect
• Proof of Phenotype
effect
Growth Factor
Growth FactorReceptor
MEK
Cell Proliferation
TF
ras
raf
ERK
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Pharmacodynamic Biomarkers:
The ho(y)pe
Question Result
Does it hit the
target in man ?
Proof of mechanism (PoM)e.g. enzyme inhibition, receptor blockade
Does it have an
effect on the
disease
phenotype?
Proof of Principle (PoP)
e.g.Increased cell death markers
(apoptotic markers- eg. TuNeL),
Does this result
in a beneficial
clinical effect?
Proof of Concept (PoC)e.g. Tumour size reduction,
Go/
No go
Go/
No Go
Go/
No Go
Progressive reduction of
uncertainty about effects
Increasing level of confidence
about outcomes
No guarantee ofSuccess: rather
Staged risk management
A real example
Drug target-pERK
Tumour growth-Ki67 proliferation
Response Stable Disease Progression
Pre-dose Post-dose
B.Vose: Analyst Briefing 2006
A. Adjei: NCI-EORTC-AACR 2006
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Dose response- a real example
A B C D ED EA C
B. Vose: Annual Business Review Day: 2006
ASCO Plenary 2007
A
B
C
B
A
E
C
D
H
F
GI
Losing a loser(AZD5438; 2005)
Maximum Tolerated Single doseIn volunteers
Maximum Tolerated Repeat doseIn patients
Convincing biological activity
Pre-dose
Post-dose
Kill
B. Vose: Annual Business Review Day: 2004, 2006
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Business model to “qualify” a biomarker in Oncology
Identify potential biomarker(s) (3y prior to clinic)MS3)
A. Assay development in human tumour and/or non-tumour tissue
(Feasibility Study)
Biomarker with clinical utility
Set Go/No Go Hurdles
Lock preferred method
B. Variability in intendedtumour and/or non-tumour tissue
(Reproducibility Study)
D. Clinical sensitivity / positive control study in man IF possible
C. Preclinical sensitivity testing with Candidate Drug
(Positive control/PK-PD)
Definitions
� Biomarker: A characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.
� Clinical endpoint: A characteristic or variable that reflects how a patient feels or functions, or how long a patient survives.
� Surrogate endpoint: A biomarker intended to substitute for a clinical endpoint.
(NIH recommended definitions, 2001; Controlled Clinical Trials 22:485–502 (2001))
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Uses of Pharmacodynamic Biomarkers
• Better guide to duration of action & thus dosing interval
than ‘surrogate’ pharmacokinetics
• Reduce clinical development costs by decision making
early in development.
• Fixes the bottom of the dose response curve for phase II
by eliminating those doses without any biological activity
• Demonstrates evidence of desired effects for commercial
message and support of further discovery effort
• Facilitate investigator, patient and company
interest/recruitment for patient trials
Dangers of pharmacodynamic biomarkersDangers of pharmacodynamic biomarkers
• Some can only be ‘validated’ by demonstrating their predictivity
to disease only by completion of phase III trials- ie. with novel
compounds higher inherent risk.
• Scepticism by internal management and external investigators
but reduced if biomarker is used in animals and man
– Understanding the link (poor qualitative correlate)
– Dose extrapolation (poor quantitative correlate)
• Implementation time ` - feasibility trial
- variability trial
- positive control trial
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Lead
Identification
Target
Identification
Hit
Identification
Lead
Optimisation
Discovery
Medicine
Development
for LaunchLaunch
Product Maintenance &
Life Cycle Support
•What dose making biomarker? •What schedule?
•What disease type?
•Personalised medicine
•Combination strategy
Translational Medicine
Understand how drugs work in man
Generating plausible and testable scientific hypothesesto address the perennial development questions
A reminder on terminology
Biomarkers
Predictive markersDetermines likelihood of
Response to therapyTomorrow’s lecture
Measured prior
to therapy
PD biomarkersChanging in response
to therapy
Response after
receiving therapy
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What is Personalised Medicine?
• Personalised Medicine involves testing
patients before prescription
• To enable clinicians to prescribe
– The right drug
– At the first time
– For the right disease
– To the right patient
What is Personalised Medicine?
In an unselected population, there may be patients who are poor
responders or who suffer adverse events
“Safe Responders” Poor Responders
Adverse events
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What is Personalised Medicine?
“Safe Responders” Poor Responders
Adverse events
The concept of Personalised Medicine is that these patients can be
screened out prior to treatment leaving only patients with good safety
and efficacy
The Old Paradigm:
Reactive Medical Care
Select Drug
Diagnosis
Switch Drug
Switch Drug Again
Diagnose Disease; Treat Symptoms; Costly, Trial and Error Treatment
Dis
ea
se
Se
ve
rity
Time
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Personalised Medicine Paradigm:
Efficient Medical Care
Health Management; Molecular Screening; Early Detection; Rapid Effective Treatment; Improved Quality of Care
Predisposition
“Right” Drug
Diagnosis/Prognosis
Screening
Monitoring
Dis
ea
se
Se
ve
rity
Time
Examples of predictive biomarkers
Type Example Gene/ marker Test
Gene mutation
Gleevec C-kit+ve
GIST
Somatic mutation
Gene mutation
Olaparib BRCA Germline mutation
Gene amplification
Herceptin Her2+ ve
Breast Cancer
FISH
Protein expression of target
Erbitux EGFR IHC
Protein Expression off-target (DNA repair)
Cisplatin ERCC1 IHC
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0
Difference in Median Survival cf BSC (months)
Non responsivepatients (90%)
5
Responsivepatients (10%)
0.5
Failure to correctly select patients will dilute
trial outcomes
All patients(100%)
Amongst patients treated with drug,
biomarker +ve patients do better
than biomarker –ve patients
Time
% s
urv
ivin
g o
r pro
gre
ssio
n-f
ree biomarker +ve, drug
biomarker –ve, drug
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..but the same is true for patients
treated with control, biomarker +ve
patients do better than biomarker
–ve patients
Time
% s
urv
ivin
g o
r pro
gre
ssio
n-f
ree
biomarker +ve, control
biomarker –ve, control
biomarker +ve, drug
biomarker –ve, drug
biomarker+ve patients treated
with drug do better than
biomarker +ve patients treated
with control
Time
% s
urv
ivin
g o
r pro
gre
ssio
n-f
ree
biomarker +ve, control
biomarker –ve, control
biomarker +ve, drug
biomarker –ve, drug
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Oncology PHC Strategic framework
Hypothesis
GenerationMolecular characterisation of sensitive vs resistant cell lines determining molecular basis for different sensitivity
Step Activity
Test
GenerationDevelop reagents & assimilate into a
“Research Use Only” kit for use in early clinical testing
Hypothesis
TestingDeployment & support to Study Teams in Phase I/II studies
to evaluate prediction with response
Development for Launch
Deploy in Phase III studies & partner
with external provider for diagnostic label
Exploratory
PHCCollect pre-dose plasma & serum from all patients enrolled
Into trials enabling additional tests as science evolves
1692
Patients
560
samples
380
evaluable
215 for
DNA anal.
EGFR: 26
No sample
Consent
Pathology
Tracking
Consent
DNA
extraction
DNA
depletion
Available for
FISH &
IHC
Available for
EGFR mutations
A real exampleIressa: Clinical Collection
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Randomised treatment
Gefitinib EGFR M+
Gefitinib EGFR M-
Carboplatin / paclitaxel M+
Carboplatin / paclitaxel M-
0.0
0.2
0.4
0.8
1.0
0.6
0 4 8 12 16 20
Time from randomisation (months)
24
Pro
babili
ty o
f pro
gre
ssio
n-f
ree s
urv
ival
PFS by Mutation Status– Overlaid KM Curves
Lead
Identification
Target
Identification
Hit
Identification
Lead
Optimisation
Discovery
Medicine
Development
for LaunchLaunch
Product Maintenance &
Life Cycle Support
•What dose making biomarker? •What schedule?
•What disease type?
•Personalised medicine
•Combination strategy
Translational Medicine
Understand how drugs work in man
Generating plausible and testable scientific hypothesesto address the perennial development questions
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Flavopiridol Monotherapy Development Programme n=271Flavopiridol Monotherapy Development Programme n=271(…or how to loose 5 years on your development plan)(…or how to loose 5 years on your development plan)
72h CI q2w iv
RISING DOSE TOLERABILITYRISING DOSE TOLERABILITY
Advanced Disease n=76
4-98 mg/m2/day
Senderowicz*,1997
72h CI ??q2w iv
RISING DOSE TOLERABILITYRISING DOSE TOLERABILITY
Advanced Disease n=37
8-56mg/m2/day
Thomas,1998
RPD= 50mg/m2/day CI 72h q2w** (500ml)
(40-60) Css=271nM+
40mg/m2/day CI 72hr
METASTATIC CRCMETASTATIC CRC
n=14
Bennett, 1999
METASTATIC NSCLCMETASTATIC NSCLC
n=20
Shapiro*, 1999
METASTATIC GASTRICMETASTATIC GASTRIC
n=16
Schwartz*, 2001
METASTATIC RENALMETASTATIC RENAL
n=35
Stadler* 2000
? Optimal schedule –as monotherapy or combination
1hr Dx5 q3w iv
RISING DOSE TOLERABILITYRISING DOSE TOLERABILITY
Advanced disease n=24
12-52.5mg/m2/day
Senderowicz, 2000
1hr Dx3 q3w iv
RISING DOSE TOLERABILITYRISING DOSE TOLERABILITY
Advanced disease n=12
50-62 5mg/m2/day
Senderowicz, 2000
24h CI qlw iv
RISING DOSE TOLERABILITYRISING DOSE TOLERABILITY
Advanced disease n=20
40-100mg/m2/day
Sasaki, 2002
37.5mg/m2Dx5 q3w
Cmax=1622nM
RPD=50mg/m2/Dx3 q3w
Cmax=4200nM
80mg/m2/day q1w
Cmax=718nM
MELANOMAMELANOMA
n=17
Burdette-Radoux 2002*Journal Article (remainder abstract only)+In vitro IC50 >300nM to induce apoptosis
**”The starting dose of 50mg/m2/day for 3 days proved
intolerable to the majority of patients (Schwartz, 2001)
“Demanding treatment schedule” (Shapiro, 2001)Current view is 8-12h block required (but ongoing clonogenicity
experiments assessing 6, 12, 18 and 24h exposure)
The drivers
Discovery dataDiscovery data Clinical reviewClinical review
Commercial dataCommercial data
RecommendedRecommended
scheduleschedule
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Discovery Data: In vivoSW-620
-0.4
0.1
0.6
1.1
1.6
2.1
2.6
5 10 15 20 25 30 35 40
Day post tumour inoculation
Mean
Tu
mo
ur
Vo
lum
e (
cm
3)
Transient Inhibition of enzyme Activity in xenograft
0
10
20
30
40
50
60
70
80
0 20 40 60 80
Time after last dose (hours)
En
zym
e
acti
vit
y (
% c
on
tro
l)
Lead
Identification
Target
Identification
Hit
Identification
Lead
Optimisation
Discovery
Medicine
Development
for LaunchLaunch
Product Maintenance &
Life Cycle Support
•What dose making biomarker? •What schedule?
•What disease type?
•Personalised medicine
•Combination strategy
Translational Medicine
Understand how drugs work in man
Generating plausible and testable scientific hypothesesto address the perennial development questions
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Therapeutic Therapeutic targetingtargeting
(combination(combinationbased strategy)based strategy)
Oncogenic HypothesisOncogenic HypothesisDiseaseDisease
ExpressionExpressionLinkageLinkage
ToxicologicalToxicologicalTargetingTargeting
CommercialCommercialTargetingTargeting
Market opportunityMarket opportunity Clinical TargetingClinical Targeting
Translational MedicineTranslational Medicine
Serendipity/Serendipity/EmpiricalEmpirical
PhysiochemicalPhysiochemical
ChoiceChoiceof Tumourof Tumour
TargetTarget
Regulatory Regulatory TargetingTargeting
Speed Speed
to Marketto Market
Expression Associated with human disease
Gastric Cancer Pancreatic Cancer Ovarian Cancer Breast Cancer Cervical Cancer
Prostrate Cancer Testicular Cancer Renal Cancer Soft Tissue Cancer Endometrial Cancer
Thyroid Cancer Colon Cancer Malignant Melanoma Liver Cancer Lung Cancer
3+
2+
1+Necrotic areas
Tissue
Missing
TMA Heat Map for p-AKT
21
24
0 20 40 60 80 100
0
10
20
30
40
50
60
70
80
90
100
Proportion
surviving
(%)
Patients
without
activated Src
Patients with
activated Src
Allgayer, 2002
Src < 2.1
Src > 2.1
Months after diagnosis
n=45
Expression Associated with clinical outcome
Expression Associated with in vitro disease models
33
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Lead
Identification
Target
Identification
Hit
Identification
Lead
Optimisation
Discovery
Medicine
Development
for LaunchLaunch
Product Maintenance &
Life Cycle Support
•What dose making biomarker? •What schedule?
•What disease type?
•Personalised medicine
•Combination strategy
Translational Medicine
Understand how drugs work in man
Generating plausible and testable scientific hypothesesto address the perennial development questions
Combinations
Rational
Combination
Pre-clinical
Synergy or
additivity
Non
Overlapping
toxicity
Tumour
Type
Sequencing
knowledge
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Pre-clinical screening
Credible Target
Credible drug
Credible clinical trial
Credible biomarker
Credible Tumour
The Last Word: TRWG Blueprint for Translational Science
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