Biomarkers & Tv Humanising Drug Discovery

Dr H ParmarDiscovery Medicine, Astrazeneca

The Use of Biomarkers and Target ValidationHumanising Drug Discovery

Dr Harsukh ParmarExecutive Director, Global Discovery Medicine,Respiratory & Inflammation Therapeutic Area

[email protected]



R & D Productivity• What’s Increasing?

! R&D Cycle times at all phases

! Regulatory hurdles! Approval times! Number of clinical

trials/NDA! Clinical trial size (# of

patients)! R&D inflation (> 12 %)! Drug development costs! R&D spending! Investors’ expectation for

growth! Product liability! Industry risk

• What’s Increasing?! R&D Cycle times at all

phases! Regulatory hurdles! Approval times! Number of clinical

trials/NDA! Clinical trial size (# of

patients)! R&D inflation (> 12 %)! Drug development costs! R&D spending! Investors’ expectation for

growth! Product liability! Industry risk

• What’s Decreasing?! Success rates at all phases! Product Exclusivity

• What’s Decreasing?! Success rates at all phases! Product Exclusivity

The Result:– R & D productivity is down

across the industry!

The Result:– R & D productivity is down

across the industry!


Main Reasons for Termination of Development for “Opportunity Cost” is LACK OF EFFICACY!

Toxicology19.4%

Other6.2%

Various10%

Clinical Efficacy22.5%

PortfolioConsiderations

21.7%

Clinical Safety20.2% Clinical

Pharmacokinetics/Bioavailability

3.1%

Preclinical efficacy3.1%

PreclinicalPharmacokinetcs/

Bioavailability1.6%

Formulation0.8%

Patent or CommercialLegal0.8%

Regulatory0.8%



U.S. Drug Industry R&D Expenditures and Drug Approvals, 1963-2000

U.S. Drug Industry R&D Expenditures and Drug Approvals, 1963-2000

0

20

40

60

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

NC

E A

ppro

vals

0

9

18

27

R&

D Expenditures

(Billions of 2000$)

Source: Tufts CSDD Approved NCE Database, PhRMAR&D expenditures adjusted for inflation

R&D Expenditures

NCE Approvals


Readouts from the Human Genome Project


High-throughput technologies are being applied and needed to identify and validate molecular targets from the human genome. We have and need better:•Target Discovery & Disease Linkage•Biomarkers, Surrogates & Human Target Validation•Diagnostics-Pharmacogenetics-Personalised MedicineBut we also need more Powerful•Bioinformatics-Computational Biology•Multiple Databases to Interrogate & Knowledge Mx

What are the Post Genomic Challenges ?


How can we use HT technologies to address productivity around Pharma R & D?

• Addressing traditional bottlenecks in drug discovery » Making new chemical compounds » Screening the right mechanism, polymorphism etc» Identifying better targets, disease linkage & biomarkers

• Changing the paradigm for drug discovery » High Content Biology allows Richer Human Integration-

”Humanizing Drug Discovery”» Greater throughput and more efficiency » More parallel, rather than linear drug discovery/development » Greater emphasis on molecular-mechanism-based targets

(“treat the cause and not just the symptom”)» Reliance on Bioinformatics & Informatics as a partner


!In these comparative genomic charts, it is easy to see why meaningfulcomparisons between humans and other species is difficult.

!The pink areas represent regions of high conservation!The blue areas represent the positions of protein-coding regions and!The purple areas represent the non-protein coding parts of a gene.

Human, Mouse & Primate Genomic Chart


The comparison between Targets in the years 2001 and 2005.Shows the influence of Genomics on drug discovery.


Drug Targets in the Genome

Human genome

~30,000

Assumption: wider phenotypic screening will identify a greater number of therapeutically-relevant genes?

Small Mol Drug targets

~1200

Therapeutically relevant genes

~6000 + 20% overlap

Predicted + assumed

druggable targets

~3000 +~3000

= ~6000

6000 Targets for Large Molecule Therapeutics


Why do we need to make better decisions faster in R&D?

!Numerous targets!Limited target

validation!Cost of development

expensive!Regulatory Hurdles!Increasing


Human TV


It would not be possible to overstate the value of in-vivo human validation. Most of what

passes for target validation today is largely conjectural in relation to the disease in question.

Diabetes Professor & ResearcherHarvard Medical School

A Revolution in R & D-The Impact of GeneticsThe Boston Consulting Group

Target Validation/PoP/PoC


“Proof of Concept”(PoC)

Mechanism will treat the disease and alter clinically recognised

and relevant endpoints

Clinical Endpointe.g. ACR20,50,70, FEV1, symptom

scores, tumour size, time to progression

“Proof of Principle”(PoP)

Mechanism related to disease process and

alters some key disease related parameters

Disease relevante.g. CRP, cartilage

breakdown products, tumour blood flow

“Proof of Mechanism”(PoM)

Pharmacology exists in man, dose-exposure-effect relationship on the target mechanism

Pharmacologye.g. receptor, enzyme

inhibition

Some terminology around biomarkers


Benefit-Risk of Biomarkers in R & DBenefits Risks

1. For NMEs with a novel mechanism of action, biomarkers are key to understanding PoM and establishing PoP/PoC.

2. Biomarkers should help contain the cost of drug development by allowing early termination or rapid progression to Launch.

3. Biomarkers may help pre-select patient populations that are most likely to benefit.

4. Biomarkers that predict the course of disease may serve as a useful tool for clinicians, health care systems.

5. Diagnostic kits could be developed where appropriate patient segmentation may reduce the size of trials required

1. Biomarkers that are nonspecific and do not correlate with clinical outcome may lead to incorrect conclusions.

2. Biomarkers associated with only a portion of the clinical outcome, may not identify all of the relevant effects of the therapy, including adverse effects.

3. Biomarker analysis can be expensive and time-consuming.

4. Biomarker-based decisions could become biased unless a priori criteria are set up for decision-making in addition to biomarker data.

5. Patient pre-selection using biomarkers may reduce the potential market size.



Median Log DR30 (range) following oral administration of Novel NME

0

5

10

15

20

25

30

35

40

-1 -0.5 0 0.5 1 1.5 2Log DR30

Placebo100 mg300 mg400 mg1000 mg

1 h post dose

42/50 h post dose

4/5 h post dose

•Similar level of inhibition of CD11b for all doses•Evidence of complete reversibility

•PK/PD mismatch. Getting a better PD profile than predicted by PK

PoM: Chemokine Targetex vivo CD11b upregulation

Dr H ParmarDiscovery Medicine, Astrazeneca09/08/2005 15

Discovery MedicineUtilize and Integrate Human

Pathophysiology and Disease Models

Platforms•Genetics•Genomics•Proteomics•Metabonomics•Imaging•Epidemiology•Physiology

Deliverables•Validated targets•Pathophysiologicalunderstanding

•Biological Mechanism•Disease stratification•Biomarkers•PoP/PoC Methods•Patient segmentation

100

75

50

25

025 50 75

Smokedregularly andsusceptible toits effects

Never smokedor notsusceptibleto smoke

Stoppedat 45

Stopped at 65

Disability

Death

AGE (YEARS)

FEV

(% o

f val

ue a

t age

25)

1

† †

Median Log DR30 (range) following oral administration AZD8309

0

5

10

15

20

25

30

35

40

-1 -0.5 0 0.5 1 1.5 2

Log DR30

Placebo100 mg300 mg400 mg1000 mg

1 h post dose

42/50 h post dose

4/5 h post dose

Clinical DataExperimental data

Bioinformatics and Informatics


PRESENT Pharmacologic Effect Physiologic Effect Biochemical Assays Enzymatic Assays In Vivo Challenge Tests Imaging

From….. Single VariableTo….. Multiple Variables

FUTURE Genomics Proteomics Metabonomics In Vivo ImagingHigh Content Biology

Pharmacodynamic Biomarkers

INCREASINGFUTURE FOCUSFOR BIOMARKERDISCOVERY


Whole CellProteinMessenger

RNADNA

Genes

The Cellomics Concept

Image Processing


High Content Screening & Biomarkers

“High Content Screening integrates fluorescence-based assays and novel image processing algorithms for automated analysis of sub-cellular events” Cellomics TM


Kinase Cascades


Pathway Analysis

Compound

pJun (Cellomics)HeLa/TNF

pP38 (Cellomics)HeLa/TNF

NFκκκκB (Cellomics)HeLa/TNF

pMAPK (Cellomics)HeLa/TNF

V A A A AW A A N/A N/AX A N/A N/A N/AY N/A N/A N/A AZ N/A N/A A N/A


Disease Process Modelling


•Maps for a variety of individual biochemical, signaling and gene regulatory pathways

•A few examples of disease process models predicting likely targets and biomarkers

What We Don’t Have:•Good understanding of relationships between individual targets, biomarkers and disease processes

•General framework linking genomics, proteomics, and disease process evolution from biomarker changes to clinical outcomes

What We Have:

Focus on Biopathways


Discovery PreClinical Clinical Outcomes

Molecular Structure Activity

Subcellular

Whole Body (animals/humans)

Clinical Trials

Clinical Programs

Drug Portfolios

Cellular

Tissues/Organs

Medical Care Systems

Not currently addressed

Under DevelopmentProducts Available

Not appropriate

SOURCE: Price Waterhouse Coopers

Simulation & Prediction - rapidly emerging technologies


Modelling of Human Disease

From molecules, From molecules, pathways, cells, organs pathways, cells, organs to integrated physiologyto integrated physiologyin health & diseasein health & disease

Disease in Whole Human BeingDisease in Whole Human BeingCell

Chromosome

DNA

Protein

Nucleus

mRNAamino acids


Human Tissue in Target ValidationCross-functional Inputs

• Tissue acquisition - human tissue sources

• Tissue banking - repository, logging and distribution

• Histopathology - sectioning, staining and morphometry;

diagnostic confirmation

• Immunocytochemistry- bio markers (tagged antibodies, oligos

enzyme markers etc)

• Bio-analysis - mediators, enzymes, cytokines etc

• Molecular biology - gene chip technology (Affymetrix, Taqman etc)

• Bio-informatics - interrogation of integrated data bases


1.Monoclonal antibodies & Nanobodies

2.Antisense & siRNA

3.Viral Vectors

4.Gene therapy & Nucline Gene Silencing

5.Ribozymes & Aptamers

6.Recombinant proteins

7.Zinc Finger Proteins

8.Currently available drugs with multiple mechanisms

9.Lead Compounds in LO phase etc

Tools for Human Target ValidationAlso Potential Fast Track Therapeutics


Target Validation Experiments Already Established in Man

1. HIV (Ribozymes, Antibodies, rHu P)

2. Cancer (A/B’s, Antisense, GeneRx etc)

3. IHD & GI (AntiTNF,GeneRx, Viral Vectors)

4. RA (A/B’s, Cytokines etc)

5. Asthma (Anti IgE A/B, Anti IL-5)

6. Transplantation & Asthma (Zenapax)

7. Multiple Sclerosis (Anti-VLA4, Tysabri)


Target & concept

validation

Year

Lead discovery Lead

optimization

CD prenomination

Attrition rate (%):•chemical•biological•selection of target•efficacy•safety & interactions•failure to meet target profile

Hit

50% 50% 40% 15% 20%

1 2 4.54 5.5

preCD CD

LC

IND

100

40

20

No Targetsinitiated

annually

10

No

Target

Small molecule

Antibody(2-3 years)

(6-8 years)


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

First human dose to market First patient dose to market First pivotal dose to market Submission to market

Succ

ess

rate

NCEsBiotech/Gene therapy

Current cumulative success rates to market by product type

CONFIDENTIAL

Source: CMR International


Number of Clinical Studies for Approved Biopharmaceuticals and NMEs

Number of Clinical Studies for Approved Biopharmaceuticals and NMEs

5.1

21

5.2 6

1.3

1011.8

37

0

40

MEA

N N

UM

BER

Phase I Phase II Phase III Total

Biopharmaceuticals (1994-2000, n=12)NMEs (1995-2000, n=23)

Source: Reichert, Drug Inf J 2001;35:337-346


107441 307

696 598

3350

1014

4478

0

4800

MEA

N N

UM

BER

Phase I Phase II Phase III Total

Biopharmaceuticals (1994-2000, n=12)NMEs (1995-2000, n=23)

Source: Reichert, Drug Inf J 2001;35:337-346

Number of Subjects for Approved Biopharmaceuticals and NMEs

Number of Subjects for Approved Biopharmaceuticals and NMEs


Recombinant Proteins


Vascular Endothelial Growth Factor - 2 (VEGF-2)


Photos showing comparison between clinical condition pre- and post-Rituximab. ( N of 1 Trial)

Cooper, H. L., Healy, E., Theaker, J. M. & Friedmann, P. S.Treatment of resistant pemphigus vulgaris with an anti-CD20 monoclonal antibody (Rituximab).

Clinical & Experimental Dermatology 28 (4), 366-368.


Human SkinAs a Tool to Study Inflammation


Urate Crystal Skin Inflammation• Need safe and malleable in

vivo inflammation models for early PoP for novel inflammation targets

• Skin is visible and safely accessible

• Monosodium urate crystals are a potent inflammatory stimulus (gout)

Biopsies

• Histology

• ICC

Skin Chamber fluid

• Cell counts

• Cell characterisation

• Soluble mediators

Clinical• Laser doppler• Systemic inflammatory

markers (blood)• Subjects assessment of

discomfort• Investigators assessment of

inflammation• Safety


Urate crystals in skin chambers• Chamber applied to de-roofed vacuum blister• GMP crystals applied 2 hours• Fluid for cells and mediators (20-plex Luminex)• Neutrophils, IL-8 and other chemokines

Control

UAX 1.25mg

UAX 2.5mg

2 4 6 80

250

500

750

time (hr)

Tota

l cel

l cou

nt (1

03)

Neutrophil exudate, #7 Luminex (IL-8), #7

0

100

200

300

2hr 4hr 6hr 8hr

IL-8

pg/

ml

Control 1.25mg 2.5mg


Intradermal urate crystals• Graded doses 0- 2.5mg injected• Quantitate inflammation with laser doppler• Biopsy shows neutrophil, then macrophage infiltrate• Safe, well tolerated, and with no lab changes• Some inter-patient subject variability (timecourse,

intensity)

0 mg

0.63

1.25Same model has been created in animals for full R & D Integration


Psoriasis for assessing therapeutic effects• Cyclosporin A , anti-CD2, CTLA4-Ig and anti-TNFs are

all clinically validated in psoriasis• Accessibility of skin

– Easily monitored clinical response– Sample collection to investigate mechanistic effects easy

Infliximab CTLA4-Ig


Early concept testing in man-P2Y2• Experimental data suggested P2Y2 a good target for

Psoriasis

• Effect on Keratinocytes & Neutrophils demonstrated

• Progress in identifying good compounds (10nM) for the CDTP, however DMPK was still a problem

• A fast track PoP/PoC was negotiated• Only 75 gms of GMP material for Tox, PARD, DMPK and

Clinical PoP was produced.• A very limited Toxicology program agreed with MHRA• Ethics & Regulatory Approval CTX (IND) obtained• 26 patients with Psoriasis treated• Clear outcome, highly significant result• Steroid >>Calcipitrol>>P2Y2=Placebo

1. Human Stop/Go PoP data generated 3-5 years before traditional process

2. Limited cost < £200,000 for all PRD, Safety, DMPK, Clinical etc

3. Introduced the concept of Investigational Tracks to AZ

4. Process repeatable with new eIND and EU guidelines


Development of Concept testing for Inflammation Projects in Humans

Inflammation models in humans• Quantification of inflammatory reactions• Investigation of inflammatory cell recruitment• Mediator analysis and the development of microdosing

approaches to investigate Candidate Drug activity.


Dermal Microdialysis & Microdosing

•Single Dose and/or •Mutiple Dose including Dose Ranging Possible

In the same subject


Asthma & COPD


Whole Blood PoM Markers• The robustness of the CD11b and shape change responses on eosinophils to eotaxin-

2 was assessed in non-atopics• Shape more stable than CD11b in non-atopics

300

325

350

375

400

425

450

475

500

525

550

-12 -11 -10 -9 -8 -7 -6

300

325

350

375

400

425

450

475

500

525

550

-12 -11 -10 -9 -8 -7 -63

4

5

6

7

8

9

10

-11 -10 -9 -8 -7 -6 -5

3

4

5

6

7

8

9

10

-11 -10 -9 -8 -7 -6 -5

Shape CD11b

Donor 3

Donor 5


Biomarkers for iNOS inhibition: exhaled NO(SA Kharitonov, 2001)

NO

pp b

10

15

20

25

30

35

NORMALNORMAL

05

0 2 4 6 8 12 24 48 72

PLACEBO

SD36510

5

10

15

20

25

30

35

Hours

ASTHMAASTHMA

0 2 4 6 8 12 24 48 72

PLACEBO

SD3651 (L-NILTA)


COPDControl

COPD PoP: Biomarker DiscoveryIn Vitro

Urine

Exhaled breathcondensate

Blood

Sputum

Elastin breakdownspecific peptides

Pept

ide

Inde

x

Control COPD

010

020

030

040

050

0

Fraction 38, Mass 1286

Inte

nsity

Control COPD

Differential ComparisonLung destructionElastin degradation products

In Vivo

Size of peptide


Novel Markers-Proteomic analysis of plasmaStable and Acute Exacerbation, COPD

Protein 1

0

20

40

60

80

100

Control Stable COPD AE

Protein 2

0

20

40

60

80

100


Protein 4

0

20

40

60

80

100


Protein 3

0

20

40

60

80

100


% re

lativ

e ex

pres

sion

of t

otal

poo

led

sam

ple

Proteins identified by 2D Gel analysis – work on-going to validate and identify proteins


Core Problem:The migration of raw data into useable knowledge

Integrated and Contextualized Data:

Sequence Data from multiple Databases DNA Array data derived throughout disease progression Integration of Orthogonal data types

DataDataData

InformationInformationInformation

KnowledgeKnowledgeKnowledge

Raw Data: • DNA Array • Sequence Data • Toxicity Data

User-Integrated Information • Predictive Modeling:

– Disease progression models – Toxicity Models–Efficacy Models

Current State of Current State of BioPharmaBioPharma IndustryIndustry

Documents

Biomarkers & Tv Humanising Drug Discovery