1 The MSD’s Translational Medicine Research Centre in Singapore: a global approach from bench to...

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The MSD’s Translational Medicine Research Centre The MSD’s Translational Medicine Research Centre in Singapore:in Singapore:a global approach from bench to bedside … and back !a global approach from bench to bedside … and back !

Prof. Alain van Gool, Head Molecular Profiling, TMRC Singapore, Merck Research Laboratories, MSD

First International Conference on Translational MedicineCanberra, 1 November 2010

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TopicsTopics

Biomarkers in pharmaceutical drug development

Singapore

MSD’s Translational Medicine Research Centre Singapore

Trends

Challenges

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Drug R&D process: drivers for changeDrug R&D process: drivers for change

Compounds optimized(efficacy and safety) in model systems

MarketResearch Development

Failure rate over 80-90%

Does the compound work in man?

preclinical

clinical

Model systems in Discovery Research are insufficiently predictive forefficacy and safety in man

Extremely high and static attrition rates

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HistoryHistory

Nov 2007

Nov 2009

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Variable attrition in therapeutic areasVariable attrition in therapeutic areas

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

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Biopsies

Tissues

Need for translational medicine in pharmaNeed for translational medicine in pharma

Issue: frequent crossing of systems barriers

High need for translational models and tools (= biomarkers) to determine drug exposure, efficacy and safety !!

etc

Monkey

Pig

etc

RabbitMice

Ex vivo

Rodents

DogTissues

Cell lines

Primary cells

Diseased human

Healthy human

RatCellsHTS

(solution) assays

Cell lines

Target discovery

Lead discovery

Lead optimization

Pre clinical

Phase I

PhaseII(1)

Phase III

Registration Launch Market

Research Development Marketing

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Limited view from the outside Limited view from the outside

© Gary Larson

Animal models Patient-related outcome

Source: National University Hospital Singapore

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Translational medicine in pharmaTranslational 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 TranslationForward TranslationForward Translation

Reverse TranslationReverse TranslationReverse Translation

(View drug developmentas customer)

(Feed back clinical needs and samples)

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

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Question-based Drug DevelopmentQuestion-based Drug Development

In Translational Medicine Question-based Drug Development is key:

We ask the right Questions

We get the right Answers

We take the right Decisions

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Biomarker strategy: QuestionsBiomarker strategy: 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

ExploratoryDevelopment PoCLead

DiscoveryTargetDiscovery

Exposure ?

Mechanism ?

Efficacy ?

Safety ?

Responders ?

Core of Biomarker Strategy and Development planningStart in Early Discovery, expand in Lead Optimization, complete in clinical Proof of Concept

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

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Biomarker strategy: AnswersBiomarker strategy: Answers

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’

Biomarkers can provide a molecular impression of a biological system (cell, animal, human)

Biomarkers can give the answers to the 5 translational questions

Biomarkers can be various analytes:

PSA – protein in blood, increased in prostate cancer

Cholesterol – metabolite in blood, increased in heart disease

MRI scan – shows abnormal tissue, like brain tumor

{Biomarkers definition working group, 2001 }

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Biomarker strategy: (data-driven) DecisionsBiomarker strategy: (data-driven) Decisions

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”

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Biomarker R&DBiomarker R&D

Biomarker discovery

Apply validated translational biomarkers across R&D

Analytical validationIn vitro, in vivo, in silico

Clinical qualificationExperimental medicine

Fit-for-purpose biomarker assay development

Biomarker Strategy and Development planning

Candidate biomarkers available? Candidate biomarkers not available?

Per project:

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Translational medicine in a global pharma companyTranslational medicine in a global pharma company

Singapore

Canberra

GMT+08:00GMT+01:00GMT-05:00GMT-08:00MerckResearchLaboratories

- Collaborators- Vendors- Contract laboratories

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Global multidisciplinary teamsGlobal multidisciplinary teams

Technical expertises within own company– Target discovery and validation– Chemistry– Screening and automation– Pharmacology– Formulation– Clinical research– Marketing and strategy– Etc

Global locations– USA– Europe– Asia– Emerging markets

External partners– Academics– Biotech– Clinics– Diagnostics– Funding agencies– Regulators– Vendors– Service providers– Knowledge centers– Etc

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TopicsTopics


Singapore


Trends

Challenges

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SingaporeSingapore

18Source: Singapore Economic Development Board

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MSD’s Translational Medicine Research CentreMSD’s Translational Medicine Research Centre

• Significant investments Schering-Plough and MSD in manufacturing in Singapore

(S-P since 1995; now 7 plants)

• Singapore government greatly stimulates additional investment

• S-P planned new biomarker R&D centre (lab and clinic) in 2006

• Merger Schering-Plough – Organon Nov 2007

• Focus: human biomarker development, discovery (oncology), imaging

• Biomarker laboratory in Biopolis opened Feb 2009, clinic Oct 2009

• Merger Merck/MSD – Schering-Plough Nov 2009

• Focus: non-human primate biomarker lab and disease models

• New investments made

• Expand lab from current 25 FTE (capacity 100 FTE)

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BiopolisBiopolis

Biopolis 1 Biopolis 2 Biopolis 3

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MSD’s Translational Medicine Research Centre SingaporeMSD’s Translational Medicine Research Centre Singapore

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MSD’s Translational Medicine Research Centre SingaporeMSD’s Translational Medicine Research Centre Singapore

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The MSD TMRC crew The MSD TMRC crew (and growing …)(and growing …)

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TMRC Biomarker technology capabilitiesTMRC Biomarker technology capabilities

Italics = planned or outsourced

Analyte class Candidate biomarker(s) Assay format

DNA - DNA mutation- SNP- Insertion/Deletion

- Q-PCR- Quantigene (Affymetrix/Panomics)- Automated PCR systems (ABI)- Next Gen Sequencing - Whole genome microarrays (Affymetrix/Illumina)

RNA - mRNA- ncRNA- Splice variant

- Q-RT-PCR (TaqMan)- Quantigene (Affymetrix/Panomics)- Automated Q-RT-PCR systems (ABI)- mRNA expression microarray (Affymetrix/Illumina)

Protein - Protein- Peptide- Protein isoform- Post-translational modification

- ELISA- Multiplex ELISA (Luminex, Mesoscale)- Clinical chemistry (Roche clinical analyzer)- Reverse protein array (Zeptosens)- MRM LC-MS/MS

Metabolite - Biochemicals - MRM LC-MS/MS, LC-MS, (LC-)NMR- Clinical chemistry (Roche clinical analyzer)

Cell - Cellular read-out- mRNA/protein expression- Post-translational modification- Intracellular protein localization

- FC, FACS- Immunohistochemistry- In situ hybridization

Imaging - Target engagement - Biodistribution- Functional/mechanistic read-outs- Efficacy read-outs

- PET/SPECT tracer evaluation - PET/SPECT radiochemistry- phMRI/fMRI/PET- PET/MRI/CT

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Example: translational oncologyExample: translational oncology

Limited predictive value of cell line xenograft models in prediction clinical efficacy of targeted oncology drugs

Urgent need for better translational models and biomarkers

Promising option through primary tumor xenografts– Human tumor biopsies from cancer patients

– Biopsy fragments transplanted into immunodeficient mice

– Passage tumors to enable parallel testing of dosing groups

Primary xenografts enable testing drugs in stratified tumors– DNA mutations

– mRNA expression

Example: colorectal cancersin collaboration with:

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Analysis of primary colon cancer tumor samplesAnalysis of primary colon cancer tumor samples

Mastertable 20 Colon Cancer Specimens CrownBio CollaborationModel ID Passage # Growth Kinetic Comments Molecular

for Mutation Days to BRAF on Profiling

Analysis/RNA 500 mm3 Exon2 Exon3 EXON15 Exon9 Exon20 Mutation Results

CRF004 P6 41 WT WT1799 T>A Val600Glu

1633G>A, Glu545Lys

WT

CRM010 P3 53 WT WT WT1633G>A, Glu545Lys

WT

CRF012 P5 65 38G>A, Gly13Asp

WT WT WT WT

CRF024 P1 63 (difficult to grow) WT WT WT WT WT

CRM028 P3 64 35G>A, Gly12Asp

WT WT WT WT

CRX231 P3 93 38G>A, Gly13Asp

WT WT WT3140A>G,

His1047Arg

CRX455 P5 32 35G>A, Gly12Asp

WT WT WT3140A>T,

His1047Leu

CRM588 P3 31 38G>A, Gly13Asp

WT WT WT WT

CRF692 P2 NA 35G>A, Gly12Asp

WT WT WT WT

CRX047 P3 55 34G>T,Gly12Cys

WT WT1633G>A, Glu545Lys

WT

CRM245 P3 42 WT WT WT WT WT

CRM205 P5 43 WT WT1781 A>G Asp594Gly

WT3062A>G,

Tyr1021Cys


WT WT WT WT

CRM146 P3 60 WT WT WT1634A>G, Glu545Gly

WT

CRF560 P5 34 WT WT WT WT WT

CRF126 P5 33 35G>T, Gly12Val

WT WT WT WT

CRF029 P5 68 WT WT1799 T>A Val600Glu

WT WT

CRM170 P5 37 WT WT WT WT WT


WT WT WT WT

CRF196 P5 62 WT WT WT WT WT

Fast (<35) Medium (36-60) Slow (>60)

Mutation Analysis CrownBio

KRAS PIK3CA

Heterozygous Homozygous

• 20 colon cancer biopsies with proven response to standard of care treatment (e.g. irinotecan)

• Growth kinetics

• Mutation analysis hotspots– KRASG12, G13, Q61

– BRAFV600

– PI3KCAE542, E545, H1047

→ Data indicate different groups for treatment

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Stratification by mRNA expression profiling Stratification by mRNA expression profiling

Absence of DNA mutations in selected genes does not always mean normal pathway activity

mRNA expression profiling provides alternative way to determine analysis of pathway status

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mRNA expression profiling of primary colorectal tumorsmRNA expression profiling of primary colorectal tumors

BRAF mutantKRAS WT

WILDTYPE

Cut-off : 5 fold/p-value=0.05

BRAF WTKRAS mutant

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Clustering of tumors based on differential genesClustering of tumors based on differential genes

WT=BRAFWT=KRAS

WT=BRAFMUT=KRAS

MUT=BRAFWT=KRAS

Clustering of KRAS wild-type with KRAS mutants

Clustering of KRAS mutant with BRAF mutants

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Summary example translational oncology Summary example translational oncology

Primary xenograft tumors resemble clinical morphology

Stratification of tumors was done based on:– Growth kinetics

– DNA mutations of selected hotspots

– mRNA expression profiling

Data indicate that selection of different tumor groups is possible

Tumor stratification allows smaller sizes of test groups

Tumor groups being scaled up to test effect of Merck’s drugs

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TopicsTopics


Singapore


Trends

Challenges

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Trend to targeted biomarker approaches in pharmaTrend to targeted biomarker approaches in pharma

Non-targeted: Open view, not restricted to pre-defined biomarker (sets) Allow for novel and innovative findings Allows for sample clustering based on multiple analytes Slower, requires strong technical infrastructure

More an academic view

Targeted: Focuses on panels of known interesting biomarkers Allows pathway probing Allows better analysis of low abundant biomarkers Faster, can be outsourced as fee-for-service Same analysis can be applied in preclinical and clinical testing

More an industrial view

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Targeted biomarker approachesTargeted biomarker approaches

Biomarker sources: Literature Unpublished biomarkers from collaborations or internal work Technology specific biomarker panels

Technologies for targeted approaches ELISA Luminex Multi-Analyte Profiling Mesoscale immunoarrays Clinical chemistry Q-RT-PCR Resequencing Multiple Reaction Monitoring - mass spectrometry (Reverse-phase) protein arrays Western blotting Etc …

All aimed at cross-species translation

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Approach 1: Targeted mRNA quantitationApproach 1: Targeted mRNA quantitation

Identical Q-RT-PCR laboratory hardware and software across MRL globally59

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TMRC Data


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TMRC Data

SiRNA formulation 1 SiRNA formulation 2 SiRNA formulation 1 SiRNA formulation 2

Replicate protocols and, where applicable, repeat selected sample analysis

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Approach 2: Targeted phosphoprotein analysisApproach 2: Targeted phosphoprotein analysis

Reverse-phase Protein Array technology

Protein lysate samples from cells, tissues, body fluids

Spotting

High density protein microarrays with serial dilutions of samples

Blocking

Blocking of aspecific binding

Perform assay using selected pathway-specific antibodies

Assay

Fluorescence readout

The ZeptoReader detects the fluorescence intensities

Image/Data Analysis

Detection of multiple pathway signaling eventsin semi-quantitative manner

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Approach 3: Targeted mass spectrometryApproach 3: Targeted mass spectrometry

Identical laboratory hardware and software across MRL globally

Replicate quantitative protocols and work flows

E.g. Peptides E.g. Metabolites

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Example: targeted QqQ MS analysis of cyno ApoB48/100Example: targeted QqQ MS analysis of cyno ApoB48/100

ApoB48(intestine)

ApoB100(liver)

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Correlation QqQ mass spectrometry with immunoassayCorrelation QqQ mass spectrometry with immunoassay

ApoB100

ApoB48

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Trend to data-rich biomarker discovery technologiesTrend to data-rich biomarker discovery technologies

Next generation sequencing– Very large level of detail on functional genome level

• DNA, meth-DNA, mRNA, miRNA, etc• Comparative genomics

– Sequencing per patient within reach

– Strong application for patient stratifications

Mass spectrometry– Large level of detail on proteome and metabolome level

• PTMs, isoforms, metabolism

– Allows analysis of acute state of organism

– Strong application for mechanistic studies

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ChallengesChallenges

Limited functional annotation of newly identified biomarkers

– Strong technological advances (e.g. next gen sequencing, mass spectrometry) yield great amount of detailed and personalized data

– Most studies yield correlation to mechanism- and/or disease by only one lab

– Limited qualification by multiple biomarker labs using accepted validated assays

Limited availability of validated translational medicine tools

– Many published biomarkers with false positive/negative clinical qualification

– Lack of robust fit-for-purpose biomarker assays

– Lack of predictive preclinical models

– Lack of well annotated clinical samples

Limited standardized procedures

– To fuse, model and simulate translational data (molecular biomarkers, imaging, PK, pre-clinical phenotypes, clinical scores, etc)

– To qualify candidate biomarkers across multiple testing centers

Only progress through collaborations and partnerships !

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Top Institute Pharma CSF Biomarker ConsortiumTop Institute Pharma CSF Biomarker Consortium

(4 years, 8 FTE, funding industry:academia:government = 1:1:2)

Samples

Proteomics

Metabolomics

Experimental design& Data analysis

Theo Luider

Rainer BischoffThomas Hankemeier

Leon Coulier

Sybren Wijmenga

Lutgarde Buydens

Theo Reijmers

Ge RuigtAlain van Gool

Tinka TuinstraAmos Attali

Objectives:• Develop standard procedures for sample handling, proteomics & metabolomics, and

data analysis within a system biology context• Improve where needed• Apply to translational biomarker studies in Multiple Sclerosis (enlighten the black box)

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Thanks to:Thanks to:

Biomarker strategies CollaboratorsMembers of:- Organon Biomarker Platform- Schering-Plough Biomarker Group- Merck/MSD Biomarker Committee

Translational Medicine Research Centre SingaporeColleagues, particularly:- Sabry Hamza, Nicole Boo (genomics)- Rachel Ball, Meenakshi Krishnamoorthi, Aveline Neo (mass spectrometry proteomics)- Kian Chung Lee, Vidya Anandalakshmi (reverse protein array proteomics) - Hannes Hentze (pharmacology)

MSD/MerckColleagues, 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

Top Institute Pharma CSF consortiumRadboud University Nijmegen

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