Biomarker development for

targeted cancer therapeutics,

a real life story

ODDP 2015, Amsterdam

Prof. Alain van Gool

Professor Personalized Healthcare

Coordinator Radboudumc Technology Centers

Head Radboud Center for Proteomics, Glycomics and Metabolomics

Senior Scientist Integrator Biomarkers

Based on data and slides from projects @Organon, Schering-Plough, MSD

2

8 years academia (NL, UK)

(molecular mechanisms of disease)

13 years pharma (EU, USA, Asia)

(biomarkers, Omics)

4 years applied research institute (NL, EU)

(biomarkers, personalized health)

4 years university medical center (NL)

(personalized healthcare, Omics, biomarkers)

My background

1991-1996

(PhD)

1996-1998

(post-doc)

2009-2012

(visiting prof)

1999-2007 2007-2009 2009-2011

2011-now

(prof)

2011-now

2

3

Agenda

Background

– Personalized medicine

– Need for biomarkers in oncology

Case study

– Biomarkers to support development of BRAF inhibitors for melanoma

4

Translational medicine in pharma

Basic Research

In Vitro Studies

Target Validation

Animal Models

Phase I and Phase II

-PoC- Studies

Phase III Studies

Clinical Research

Forward Translation Forward Translation

Reverse Translation Reverse Translation

(View drug development

as customer)

(Feed back clinical needs

and samples)

[van Gool et al, Drug Disc. Today 2010]

5

Biomarkers

Definition: ‘a characteristic that is objectively measured and evaluated as an

indicator of normal biological processes, pathogenic processes, or

pharmacologic responses to a therapeutic intervention’

Molecular biomarkers can provide a molecular impression of a biological

system (cell, animal, human)

Biomarkers can be various analytes:

PSA protein – blood, indicator of prostate cancer

Cholesterol – blood, risk indicator for coronary and vascular disease

{Biomarkers definition working group, 2001 }

MRI scan – shows abnormal tissue, like brain tumor

6

Biomarker strategy based on key questions

Does the compound get to the site of action?

Does the compound cause its intended pharmacological/

functional effects?

Does the compound have beneficial effects on disease or

clinical pathophysiology?

What is the therapeutic window (how safe is the drug)?

How do sources of variability in drug response in target

population affect efficacy and safety?

Lead

Optimization

Exploratory

Development PoC

Lead

Discovery

Target

Discovery

Exposure ?

Mechanism ?

Efficacy ?

Safety ?

Responders ?

Core of Biomarker Strategy and Development planning

Start in Early Discovery, expand in Lead Optimization, complete in clinical Proof of Concept

{Concept by de Visser and Cohen, CHDR}

{van Gool et al, Drug Disc. Today 2010}

7

Biomarker strategy: Data-driven decisions

To be made during testing of drug in preclinical and clinical disease models:

Target engagement? Effect on disease?

yes yes !

no no

• No need to test current

drug in large clinical trial

• Need to identify a more

potent drug

• Concept may still be

correct

• Concept was not correct

• Abandon approach

• Proof-of-Concept

• Proceed to full

clinical

development

“Stop early, stop cheap”

“More shots on goal”

Include personalized differences at every stage when possible.

8

Rational selection of best targets and drugs

works The 5R’s assessment:

• Right Target

• Right Tissue

• Right Safety

• Right Patients

• Right Commercial Potential

8

9

High attrition in oncology drug development

{Kola & Landis, Nat. Rev. Drug Disc. (2004) 8: 711}

10

Source: Arrowsmith: Nature Reviews Drug Discovery 2011

• Success rates of clinical proof-of-concept have dropped from 28% to 18% • Insufficient efficacy as the most frequent reason • Better therapies following Personalized Medicine strategies are needed • Key to apply translational biomarkers for personalized therapy

Need for Personalized Medicines

Analysis of 108 failures in phase II

Reason for failure Therapeutic area

10

11

Consider individual differences in life science research

12

{Source: Chakma. Journal of Young Investigators. 2009}

Principle of Personalized/Precision/Targeted Medicine

13 13 Alain van Gool, NanoNext.NL, 3 July 2015

Optimal Personalized / Precision / Targeted Medicine

14

Biomarker need in oncology clinical care

Early detection tumor

Determine mechanism of pathophysiology

Determine tumor stage

Early detection benign to malignant tumor progression

Detect residual disease after therapy

Early and sensitive detection metastatic circulating cells

Early detection metastatic tumor

Understand why people respond differently

Main needs:

Need for biomarkers to develop more targeted therapies

Need for biomarkers for patient selection

15

Biomarker need in oncology drug development

Determine mechanism of pathophysiology of tumor

Verify published data on drug target

Select and develop a drug with

– Sufficient selectivity

– Highest efficacy Lead Optimisation

– Lowest off-target safety risk

Test exposure, efficacy and safety of drug in preclinic model

Test exposure, efficacy and safety of drug in clinical trials

Test efficacy in stratified patients, selected on mechanism

Monitor drug efficacy and safety post-market introduction

Back-translation of clinical findings to research

Consistent application of translational biomarkers

16

Agenda

Background

– Personalized medicine

– Need for biomarkers in oncology

Case study

– Biomarkers to support development of BRAF inhibitors for melanoma

17

Case study: Development RAF inhibitors for melanoma

{Miller and Mihm,

2006}

18

Mechanism of pathophysiology in BRAF mutated tumors

V600E

Kinase domain

{Roberts and Der, 2007}

B-RAFV600E mutation: constitutively active kinase, oncogenic addiction

Overactivate ERK pathway drives cell proliferation

RAF inhibitors shown to block growth of tumors with B-RAFV600E mutation

Prevalence of B-RAFV600E

– Melanoma (60%), colon (15%), ovarian (30%), thyroid (30%) cancer

19

19

{Source: Prof Khusru Asadullah, Head of Global Biomarkers Bayer Healthcare}

20

Cellular efficacy by selective B-RAF inhibition by siRNA

Wild

type

Moc

k

BRAF

1

BRAF

5

CRAF

1

CRAF

3

ARAF

4

ARAF

8

siCont

rol

GFP

0

25

50

75

100

125

150

Percen

tag

e

Inhibition of RAF-MEK-ERK

pathway and induction of

apoptosis by siRNA (shown effect in A375 cells)

Inhibition of cell proliferation

by siRNA (shown effect in A375 cells)

G2/MS

G1

A0

A0 : 38 %

G1 : 42 %

S : 6 %

G2/M : 5 %

G2/M

G1

S

G1 : 65 %

S : 17 %

G2/M : 15 %

G1 : 56 %

S : 16 %

G2/M : 17 %

SG2

G1

G2S

G1

G1 : 65 %

S : 17 %

G2/M : 12 %

B-RAF C-RAF

A-RAF GFP

G2/MS

G1

A0

A0 : 38 %

G1 : 42 %

S : 6 %

G2/M : 5 %

G2/MS

G1

A0

A0 : 38 %

G1 : 42 %

S : 6 %

G2/M : 5 %

G2/M

G1

S

G1 : 65 %

S : 17 %

G2/M : 15 %

G2/M

G1

S

G1 : 65 %

S : 17 %

G2/M : 15 %

G1 : 56 %

S : 16 %

G2/M : 17 %

SG2

G1

G1 : 56 %

S : 16 %

G2/M : 17 %

SG2

G1

SG2

G1

G2S

G1

G1 : 65 %

S : 17 %

G2/M : 12 %

G2S

G1

G1 : 65 %

S : 17 %

G2/M : 12 %

B-RAF C-RAF

A-RAF GFP

Induction of apoptosis

by siRNA

(shown effect in A375 cells)

Key response selection biomarker is B-RAFV600D/E mutation

Key pathway biomarker is phosphorylated ERKSer202/204 = p-ERK

B - RAF

C - RAF

ERK

B - actin

A - RAF

Mock GFP Si -

control C - RAF A - RAF

PARP

B - RAF WT

p-MEK

p-ERK

-

Mock GFP Si -

control C - RAF A - RAF B - RAF WT

21

Cellular efficacy by RAF kinase inhibitor compounds

Inhibition of proliferation (A375, SK-MEL-24, Colo-205)

Inhibition of anchorage-

independent growth

in soft agar (A375)

Inhibition of RAF-MEK-ERK pathway (A375, SK-MEL-24, Colo-205)

Sorafenib

Sorafenib CI1040 Org240390 SB590885 Org245224 Org245108

Solvent No compound

Sorafenib CI1040 Org240390 SB590885 Org245224 Org245108

A375

Cells:

Compounds:

SK-MEL-24

Colo-205

Sorafenib

(multikinase)

CI-1040

SB 590885

Perc

en

tag

e g

row

th

- 10 - 9 - 8 - 7 - 6 - 5 - 4 - 10

0 10 20 30 40 50 60 70 80 90

100 110 120

Log conc. (M)

CI-1040

(MEK)

SB590885

(B-RAF)

22

Analysis ERK pathway activity

A375 treated with MEKi #1 A375 treated with RAFi #1

RSK RSK RSK

p-MEK

p-ERK

p-RSK

-10 -8 -6

0

50

100

150

DM

SO

Log [SCH 772984, M]

% o

f E

RK

ph

os

ph

ory

lati

on

-10 -8 -6 0

50

100

150

DM

SO

Log [SCH 772984, M]

% o

f M

EK

ph

os

ph

ory

lati

on

-10 -8 -6

0

50

100

150

DM

SO

Log [SCH 772984, M]

% o

f R

SK

ph

os

ph

ory

lati

on

Log [ , M]

Log [ MEKi #1 , M]

MEKi #1

IC50 = 35.70 nM

IC50 = 14.26 nM

No inhibition

Concentration MEKi #1 Concentration RAFi #1

Immunoassays to monitor phosphorylation biomarkers in ERK pathway

(ELISA, western blotting, mass spectrometry, reverse phase protein arrays)

23

Example flow chart B-RAF Lead Optimisation

IC50 < 20 nM IMAP

Dose-dependent inhibition

IC50 < 100nM in 2 out of 3

B-RAFV600E cell lines

IC50 < 100 nM P-ERK P-ERK cellular

Phase 1a

Phase 1b

Solubility, eLogD,

Pampa B-RAF biochemical

In vitro ADME

Cell proliferation

ADME-PK data in range to

allow 1 or 2x daily dosing

EDC selection

Phase 2a

Phase 2b

Selection phase

PK rodent

Selectivity 20 kinases

In vitro safety

Pilot xenografts Full kinase profiling

PK

dog/monkey

14 day rat tox,

pilot Ames, novascreen,

CV safety, phototox

Pilot CMC

Reduced tumor growth at

equivalent of anticipated

human dose

Efficacy assays

ADME-Tox assays

Decisive path

Safety profile supportive

of therapeutic window

Cell apoptosis

Xenograft mouse models ?

24

A375_P_ERKpEC50

5.5

6

6.5

7

7.5

5.5 6 6.5 7 7.5

Activity B-RAF inhibitors in melanoma cell line

Proliferation (pIC50)

Pathway inhibition (pIC50)

Lead

Competitor compound

Best own compound

Overlapping

two week S&T cycles:

Week 1: Synthesis

Week 2: Testing

Start with 1 lead, and

S&T of up to 1000

derivatives.

25

Structure Activity Relationships

NH

NH

O

O

O

NH

O

CN

N

S

R

N

NS

R1

R2

(lead)

Central phenyl: only m-F allowed

Allosteric backpocket:

• aryl required

• meta subst required

• heteroaromates allowed

• solubilizers allowed

Linker:

• NHCO most active

• CONH, urea are allowed

• alkylated amide not allowed

Linker:

• NHCO most active

• variation allowed but 10-50 fold loss

Lead compound modeled in crystal structure of B-RAF kinase domain

Hinge: • subst of benzoxizanone

optimal for cellular activity

• solubilizers allowed

• other scaffolds allowed:

• substituted thienopyrazines

most optimal

26

Kinase selectivity

Medium screening of >200 kinases using biochemical assays

Read-out = phosphorylation of substrates

Limited translational value but selection of potential off-target hits

Subsequent validation needed on cellular level

% inihibition

-60

-40

-20

0

20

40

60

80

100

120

Example:

Kinase selectivity of 3 compounds tested

under the same assay conditions

% inhibition is shown; each dot is one assay

27

Cancer cell line panel testing ORG RAFi Proliferation EC50 vs. RAF Genotype

1

10

100

1000

10000

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Pro

lif E

C5

0 (

nM

)

Braf V600D Hetero

Braf V600E Homo

Braf V600E Hetero

Braf WT

CHIR 265 Prolif EC50 vs. RAF Genotype

1

10

100

1000

10000

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Pro

lif E

C5

0 (

nM

)

Braf V600D Hetero

Braf V600E Homo

Braf V600E Hetero

Braf WT

AZD6244 Prolif EC50 vs. RAF Genotype

1

10

100

1000

10000

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Pro

lif E

C5

0 (

nM

)

Braf V600D Hetero

Braf V600E Homo

Braf V600E Hetero

Braf WT

772984 Prolif EC50 vs. RAF Genotype

1

10

100

1000

10000

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Pro

lif

EC

50

(n

M)

Braf V600D Hetero

Braf V600E Homo

Braf V600E Hetero

Braf WT

RAFi 1 MEKi

RAFi 2 ERKi

Efficacy compound 1-4 linked to BRAFV600D/E mutational status

Compound 1 limited effect in WT cell lines (less off target effects?)

28

Discovery of improved biomarkers for RAF inhibitors

Aim: identify soluble protein biomarker in blood that reflects

inhibition of ERK pathway in tumor with B-RAFV600D/E mutation

(More practical than p-ERK protein analysis in tumor biopsy)

(Enabling personalized medicine)

Pharmacogenomics approach:

– A375 melanoma cells

– Homozygote BRAFV600E mutation

– Robust model system for method development

– Investigate effect of 7 inhibitors

• 4x RAFi

• 2x MEKi

• 1x ERKi

on gene expression, proliferation, apoptosis, etc

29

Pharmacogenomics in A375 melanoma cells

• Efficient approach

• Highly reproducible data with

robust gene modulation

• Identify compound-specific and

common differential transcripts

• Select candidate biomarkers

RAFi #4

MEKi #1MEKi #2

RAFi #3

RAFi #1

RAFi #2

ERKi #1

RAFi #4

MEKi #1MEKi #2

RAFi #3

RAFi #1

RAFi #2

ERKi #1

RAFi #4

MEKi #1MEKi #2

RAFi #3

RAFi #1

RAFi #2

ERKi #1

RA

Fi

#1

RA

Fi

#2

RA

Fi

#3

RA

Fi

#4

ME

Ki

#1

ME

Ki

#2

ER

Ki

#1

RA

Fi

#1

RA

Fi

#2

RA

Fi

#3

RA

Fi

#4

ME

Ki

#1

ME

Ki

#2

ER

Ki

#1

RAFi #1

RAFi #2

RAFi #4

RAFi #1

RAFi #2

RAFi #4

Data for RAFi #4

4x RAFi

2x MEKi

1x ERKi

30

• ~200 genes with >10 fold change.

• Overlap and differences between compound-regulated genes

• Methods applied to select new candidate biomarkers for validation, e.g. as

secreted proteins in plasma

• Selection of ERK pathway responsive transcripts, e.g. IL-8

Selection biomarkers from pharmacogenomics A375 cells

RA

Fi

#4

RA

Fi

#1

RA

Fi

#2

ER

Ki

#1

RA

Fi

#3

ME

Ki #

1

ME

Ki #

2

DM

SO

31

Zoya R. Yurkovetsky, John M. Kirkwood et al. Clin Cancer Res 2007;13(8) April 15, 2007

123 pg/ml

9 pg/ml

p < 0.001

Determination of IL-8 levels (one of 29 serum cytokines analyzed) in

179 melanoma patients (stage II & III) & 379 healthy individuals

Elevated levels of IL-8 in Patients with Melanoma

32

Validation study to confirm IL-8 in melanoma

Tissue Plasma

Normal Healthy Controls 40 50

Stage 1 11 11

Stage 2 11 11

Stage 3, non-metastatic 4 4

Stage 3, metastatic 11 11

Stage 4, non-metastatic 3 3

Stage 4, metastatic 19 19

Stage 1 Stage 2 Stage 3 Stage 4

H&E staining; 20x

Clinical samples used (from two independent commercial biobanks)

33

Validation study to confirm IL-8 in melanoma

Stage 1 Stage 2 Stage 3 Stage 4

H&E staining; 20x

Analysis done:

• Genetic analysis for BRAFV600E/D mutation in genomic DNA from tissue samples

• IL-8 mRNA analysis in tissue samples by in situ hybridisation using bDNA probes

(multiplexing with 12 ERK pathway response transcripts)

• IL-8 protein analysis in tissue samples by immunohistochemistry (in parallel with 4 other

ERK pathway response proteins, Ki67, Tunnel)

• IL-8 protein analysis in matching plasma and serum by IL-8 immunoassay (3 formats:

ELISA, Luminex, Mesoscale; singleplex and multiplex)

• Statistical data analysis

34

Plasma IL-8 levels vs Melanoma Stages

No confirmation of literature: no change in IL-8 protein levels in plasma

samples of melanoma patients. Reason?

35

No change in plasma & serum IL-8 levels in melanoma

Serum IL-8 levels in various Stages of Melanoma

Healthy control (n=10) Melanoma (n=37)

0

20

40

60

80

Me

an

IL

-8 l

ev

els

(p

g/m

l)

Plasma IL-8 levels in various Stages of Melanoma

Healthy control (n=20) Melanoma (n=59)

0

5

10

15

20

Me

an

IL

-8 l

ev

els

(p

g/m

l)

No confirmation of literature: no change in IL-8 protein levels in melanoma

Reason?

Conclusion:

Key response selection biomarker is B-RAFV600D/E mutation

Key pathway biomarker is phosphorylated ERKSer202/204 = p-ERK

36

Alignment with: - Experimental medicine

- Competitive intelligence

- Strategy

- Toxicology

- Formulation

- External experts (clinics, academics)

Predict clinical efficacy in oncology

Cells

Cell line xenografts (PoM, PoP)

Healthy subjects (PoM)

Cancer patients (PoM, PoP)

Selected cancer patients (PoC)

PoM – Proof of Mechanism

PoP – Proof of Principle

PoC – Proof of Concept

Primary tumor xenograft models

Genetically engineered mouse models

(PoM, PoP, non-pivotal PoC)

37

Clinical efficacy of Vemurafenib, a novel BRAF inhibitor

Key biomarkers:

Exposure: -

Mechanism: p-ERK, Cyclin-D1

Efficacy: Ki-67, 18FDG-PET, CT

Safety: -

Selection: BRAFV600E mutation

Clinical endpoint: progression-free survival (%)

{Source: Flaherty et al, NEJM 2010} {Source: Chapman et al, NEJM 2011}

38

History of Zelboraf (Vemurafenib)

Davis M J , Schlessinger J J Cell Biol 2012;199:15-19

© 2012 Davis and Schlessinger

39

Clinical effects of Vemurafenib

{Wagle et al, 2011, J Clin Oncol 29:3085}

Before Rx Vemurafenib, 15 weeks Vemurafenib, 23 weeks

• Strong initial effects vemurafenib

• Drug resistancy

• Reccurence of tumors

40

People are complex systems …

41 {Source: Yancovitz, PLoS One 2012}

Tumor tissue heterogeneity

• BRAFV600D/E is the driving

mutation in melanoma

• However, also no BRAFV600D/E

mutation found in parts of a

primary melanoma

• Molecular heterogeneity in

diseased tissue

• Biomarker levels in tissue will

vary

• Biomarker levels in body

fluids will vary

• Major challenge for

(companion) diagnostics

42

Biomarker innovation gaps

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

Gap 3

43

Biomarker innovation gaps: some numbers

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

Gap 3

5 biomarkers/ working day

1 biomarker/ 1-3 years

1 biomarker/ 3-10 years

?

Eg Biomarkers in time: Prostate cancer May 2011: n= 2,231 biomarkers Nov 2012: n= 6,562 biomarkers Oct 2013: n= 8,358 biomarkers Nov 2014: n= 10,350 biomarkers Oct 2015: n = 11,856 biomarkers

44

Reasons for biomarker innovation gap

• Not one integrated pipeline of biomarker R&D

• Publication pressure towards high impact papers

• Lack of interest and funding for confirmatory biomarker

studies

• Hard to organize multi-lab studies

• Biology is complex on organism level

• Data cannot be reproduced

• Bias towards extreme results

• Biomarker variability

• …

{Source: John Ioannidis, JAMA 2011}

{Source: Khusru Asadullah, Nat Rev Drug Disc 2011}

45

Build biomarker validation pipelines

Standardisation, harmonisation, knowledge sharing needed in:

1. Assay development

2. Clinical validation

3. Regulatory acceptance

46

Agenda

Background

– Personalized medicine

– Need for biomarkers in oncology

Case study

– Biomarkers to support development of BRAF inhibitors for melanoma

Take home messages:

Choose and validate your biomarkers wisely

Collaborate

Realize human biology is complex

47

Thanks to:

Biomarker strategies Collaborators

Members of:

- Organon Biomarker Platform

- Schering-Plough Biomarker Group

- Merck Research Labs - Molecular Biomarkers

Translational Medicine Research Centre Singapore

Colleagues, particularly:

Erik Sprengers, Shian-Jiun Shih, Brian Henry, Hannes

Hentze, Zaiqi Wang, Rachel Ball, Meena Krishnamoorthi,

Aveline Neo, Sabry Hamza, Nicole Boo, Lee Kian-Chung,

Vidya Anandalaksmi

MSD/Merck

Colleagues, particularly in:

- Oss (Netherlands)

- Rahway, Kenilworth, Boston (East Coast, USA)

- San Francisco, Palo Alto (West Coast, USA)

Many in Asia, Europe, USA, including:

- Academic

- Consortia

- Contract research organizations

- Vendors

Saco de Visser, Adam Cohen Centre for Human Drug Research, Leiden, NL