The Application of Translational Medicine Andrew … Application of Translational Medicine... · 1 The application of Translational Medicine in clinical development Professor Andrew

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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

2


Phase I


Patients

FTIM

EOP2(Subpart H)

NDA

EOP1


Phase II

Single arm

MTD- Tumour hunting

Phase II

Single arm

MTD- Tumour hunting

Phase II

Single arm

MTD- Tumour hunting


Phase I


Patients

FTIM

EOP2(Subpart H)

NDA

EOP1


Phase II

Single arm

MTD- Tumour hunting

Phase II

Single arm

MTD- Tumour hunting

Phase II

Single arm

MTD- Tumour hunting

3


Phase I


Patients

Phase III

Pivotal registration Trial

Randomised Controlled

FTIM

EOP2(Subpart H)

NDA

EOP1


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


Patients

FTIM

EOP1

Phase IPhase I

Post cycle 4

Baseline Post cycle 2

Post cycle 6

*Molecular Based Therapies/Novel Therapies

4

Today: Targeted Therapies

Phase I


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

5

Lead

Identification

Target

Identification

Hit

Identification

Lead

Optimisation

Discovery

Medicine

Development

for LaunchLaunch


Life Cycle Support

•What dose making biomarker? •What schedule? •What disease type?•Personalised medicine (predictive biomarker)





Pharmacological Biomarkers(Pharmacodynamic biomarkers)

• Proof of Target Effect

• Proof of Phenotype

effect

Growth Factor

Growth FactorReceptor

MEK

Cell Proliferation

TF

ras

raf

ERK

6

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

7

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

8

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


Life Cycle Support



•Personalised medicine





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


In an unselected population, there may be patients who are poor

responders or who suffer adverse events

“Safe Responders” Poor Responders

Adverse events

12


“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

13

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 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


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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


Life Cycle Support








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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


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


Advanced disease n=24

12-52.5mg/m2/day

Senderowicz, 2000

1hr Dx3 q3w iv



50-62 5mg/m2/day

Senderowicz, 2000

24h CI qlw iv



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


Life Cycle Support








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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

22

Lead

Identification

Target

Identification

Hit

Identification

Lead

Optimisation

Discovery

Medicine

Development

for LaunchLaunch


Life Cycle Support








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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Documents

The Application of Translational Medicine Andrew … Application of Translational Medicine... · 1 The application of Translational Medicine in clinical development Professor Andrew