Biomarkers & Tv Humanising Drug Discovery

The Use of Biomarkers and Target Validation
Humanising Drug Discovery

Dr Harsukh Parmar
Executive Director, Global Discovery Medicine,
Respiratory & Inflammation Therapeutic Area
harsukh.parmar@astrazeneca.com

Dr H Parmar
Discovery Medicine, Astrazeneca

Dr H Parmar

R & D Productivity
• What’s Increasing?
• What’s Increasing?
! R&D Cycle times at all
! R&D Cycle times at all
• What’s Decreasing?
phases
phases !
! Success rates at all phases
Success rates at all phases
! Regulatory hurdles
! Regulatory hurdles !
! Product Exclusivity
Product Exclusivity
! Approval times
! Approval times
! Number of clinical
! Number of clinical
trials/NDA
trials/NDA
! Clinical trial size (# of
! Clinical trial size (# of
patients)
patients)
! R&D inflation (> 12 %)
! R&D inflation (> 12 %)
! Drug development costs
! Drug development costs
! R&D spending
! R&D spending The Result:
! Investors’ expectation for
! Investors’ expectation for – R & D productivity is down
– R & D productivity is down
growth
growth across the industry!
across the industry!
! Product liability
! Product liability
! Industry risk
! Industry risk

Dr H Parmar

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

Clinical Safety Toxicology
20.2% 19.4% Clinical
Pharmacokinetics/
Bioavailability
3.1%
Other
6.2% Preclinical efficacy
3.1%

Preclinical
Pharmacokinetcs/
Various Bioavailability
10% 1.6%

Formulation
Portfolio 0.8%
Considerations Patent or Commercial
21.7% Clinical Efficacy Legal
0.8%
22.5%
Regulatory
0.8%
Dr H Parmar

U.S. Drug Industry R&D Expenditures and
Drug Approvals, 1963-2000
60 27

R&D Expenditures

R&D Expenditures
(Billions of 2000$)
NCE Approvals

40 18

NCE Approvals
20 9

0 0
63

65

67

69

71

73

75

77

79

81

83

85

87

89

91

93

95

97

99
19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

19
R&D expenditures adjusted for inflation
Dr H Parmar Source: Tufts CSDD Approved NCE Database, PhRMA

Readouts from the Human Genome Project

Dr H Parmar

What are the Post Genomic Challenges ?
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 Medicine
But we also need more Powerful
•Bioinformatics-Computational Biology
•Multiple Databases to Interrogate & Knowledge Mx
Dr H Parmar

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
Dr H Parmar

Human, Mouse & Primate Genomic Chart

!In these comparative genomic charts, it is easy to see why meaningful
comparisons 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.

Dr H Parmar

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

Drug Targets in the Genome
Assumption: wider phenotypic screening will identify
a greater number of therapeutically-relevant genes?

Therapeutically
Predicted + Human genome relevant genes
assumed
~30,000 ~6000 + 20%
druggable
overlap
targets
~3000
+~3000
= ~6000
Small Mol 6000 Targets for
Drug targets Large Molecule
Therapeutics
~1200
Dr H Parmar

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

!Numerous targets
!Limited target
validation
!Cost of development

expensive
!Regulatory Hurdles

Dr H Parmar
!Increasing

Human TV

Dr H Parmar

Target Validation/PoP/PoC

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 & Researcher
Harvard Medical School
A Revolution in R & D-The Impact of Genetics
The Boston Consulting Group

Dr H Parmar

Some terminology around
biomarkers
Pharmacology Pharmacology exists in “Proof of Mechanism”
e.g. receptor, enzyme man, dose-exposure- (PoM)
inhibition effect relationship on
the target mechanism
Disease relevant Mechanism related to “Proof of Principle”
e.g. CRP, cartilage disease process and (PoP)
breakdown products, alters some key disease
tumour blood flow related parameters
Clinical Endpoint Mechanism will treat “Proof of Concept”
e.g. ACR20,50,70, the disease and alter (PoC)
FEV1, symptom clinically recognised
scores, tumour size, and relevant endpoints
time to progression
Dr H Parmar

Benefit-Risk of Biomarkers in R & D
Benefits Risks

1. For NMEs with a novel mechanism of 1. Biomarkers that are nonspecific and
action, biomarkers are key to do not correlate with clinical outcome
understanding PoM and establishing may lead to incorrect conclusions.
PoP/PoC. 2. Biomarkers associated with only a
2. Biomarkers should help contain the portion of the clinical outcome, may
cost of drug development by allowing not identify all of the relevant effects of
early termination or rapid progression the therapy, including adverse effects.
to Launch. 3. Biomarker analysis can be expensive
3. Biomarkers may help pre-select and time-consuming.
patient populations that are most likely 4. Biomarker-based decisions could
to benefit. become biased unless a priori criteria
4. Biomarkers that predict the course of are set up for decision-making in
disease may serve as a useful tool for addition to biomarker data.
clinicians, health care systems. 5. Patient pre-selection using biomarkers
5. Diagnostic kits could be developed may reduce the potential market size.
where appropriate patient
segmentation may reduce the size of
trials required
Dr H Parmar

PoM: Chemokine Target
ex vivo CD11b upregulation
Median Log DR30 (range) following oral administration of Novel NME
40

35

30 1 h post dose

25

Placebo
100 mg 20

300 mg 4/5 h post dose
400 mg 15

1000 mg
10

42/50 h post dose
5

0

-1 -0.5 0 0.5 1 1.5 2
Log DR30

•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
Dr H Parmar

Discovery Medicine
Utilize and Integrate Human
Pathophysiology and Disease Models

Median Log DR30 (range) following oral administration AZD8309

0
4

5
3

0
3

1 h post dose
5
2

Never smoked Placebo
100 or not 100 mg 0
2

susceptible 300 mg 4/5 h post dose
to smoke 400 mg
F E V (% o f v a lu e a t a g e 2 5 )

5
1

75 1000 mg
0
1

Smoked
regularly and
50 susceptible to Stopped 42/50 h post dose
5

its effects at 45
0

Disability
-1 -0.5 0 0.5 1 1.5 2
1

25
Stopped at 65 Log DR30
Death
† †
0
25 50 75

AGE (YEARS)

Deliverables
Platforms •Validated targets
•Genetics Clinical Data •Pathophysiological
•Genomics
•Proteomics Experimental data understanding
•Biological Mechanism
•Metabonomics Bioinformatics and Informatics •Disease stratification
•Imaging •Biomarkers
•Epidemiology •PoP/PoC Methods
•Physiology
Dr H Parmar
09/08/2005 • Patient segmentation
15

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

From….. Single Variable
To….. Multiple Variables

FUTURE Genomics
INCREASING
Proteomics
FUTURE FOCUS
Metabonomics
FOR BIOMARKER
In Vivo Imaging DISCOVERY
Dr H Parmar
High Content Biology

The Cellomics Concept

DNA Messenger
Genes RNA Protein Whole Cell

Dr H Parmar
Discovery Medicine, Astrazeneca 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

Dr H Parmar

Kinase Cascades

Dr H Parmar

Pathway Analysis

pJun pP38 κ
NFκB pMAPK
(Cellomics) (Cellomics) (Cellomics) (Cellomics)
Compound HeLa/TNF HeLa/TNF HeLa/TNF HeLa/TNF

V A A A A
W A A N/A N/A
X A N/A N/A N/A
Y N/A N/A N/A A
Z N/A N/A A N/A

Dr H Parmar

Disease Process Modelling

Dr H Parmar

Focus on Biopathways
What We Have:
•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
Dr H Parmar

Simulation & Prediction - rapidly emerging
technologies
Discovery PreClinical Clinical Outcomes

Molecular Structure Activity

Subcellular Not
appropriate
Cellular Not currently
addressed
Tissues/Organs Under
Development
Whole Body (animals/humans) Products
Available
Clinical Trials

Clinical Programs

Drug Portfolios

Medical Care Systems
Dr H Parmar
SOURCE: Price Waterhouse Coopers

Modelling of Human Disease
Disease in Whole Human Being
Cell

Nucleus

Chromosome

DNA

From molecules, mRNA
pathways, cells, organs amino acids
to integrated physiology
Protein
in health & disease
Dr H Parmar

Human Tissue in Target Validation
Cross-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

Dr H Parmar

Tools for Human Target Validation
Also Potential Fast Track Therapeutics
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
Dr H Parmar

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)
Dr H Parmar

No Targets Attrition rate (%):
100
initiated •chemical
•biological
annually •selection of target
•efficacy
Target •safety & interactions
•failure to meet target profile
Hit
Antibody
40 (2-3 years)

Target &
concept LC Small molecule
validation (6-8 years)
20
Lead preCD
CD IND
discovery Lead
10 optimization
50% 50% 40% 15% 20%

No 1 2 Year 4 4.5 5.5
CD prenomination

Dr H Parmar

Current cumulative success rates to market by product type

100%

90%
NCEs
80% Biotech/Gene therapy
70%
Success rate

60%

50%

40%

30%

20%

10%

0%
First human dose to market First patient dose to market First pivotal dose to market Submission to market

Source: CMR International

Dr H Parmar
CONFIDENTIAL

Number of Clinical Studies for Approved
Biopharmaceuticals and NMEs
40
37
Biopharmaceuticals (1994-2000, n=12)
NMEs (1995-2000, n=23)
MEAN NUMBER

21

11.8
10

5.1 5.2 6

1.3
0
Phase I Phase II Phase III Total

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

Number of Subjects for Approved
Biopharmaceuticals and NMEs
4800 4478
Biopharmaceuticals (1994-2000, n=12)
NMEs (1995-2000, n=23)

3350
MEAN NUMBER

1014
696 598
441
307
107
0
Phase I Phase II Phase III Total

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

Recombinant Proteins

Dr H Parmar

Vascular Endothelial Growth Factor - 2 (VEGF-2)

Dr H Parmar

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.

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

Human Skin
As a Tool to Study Inflammation

Dr H Parmar

Urate Crystal Skin Inflammation
• Need safe and malleable in Biopsies Clinical
vivo inflammation models •
for early PoP for novel
Histology • Laser doppler
inflammation targets • ICC • Systemic inflammatory
Skin Chamber fluid markers (blood)
• Skin is visible and safely
accessible • Cell counts • Subjects assessment of
discomfort
• Monosodium urate crystals • Cell characterisation
are a potent inflammatory • Soluble mediators
• Investigators assessment of
stimulus (gout) inflammation
• Safety

Dr H Parmar

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
750
Control 300 Control 1.25mg 2.5mg
Total cell count (103 )

UAX 1.25mg
UAX 2.5mg

IL-8 pg/ml
500
200

250
100

0
2 4 6 8 0
time (hr)
2hr 4hr 6hr 8hr
Neutrophil exudate, #7 Luminex (IL-8), #7
Dr H Parmar

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

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

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

Dr H Parmar

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 1. Human Stop/Go PoP data
• Steroid >>Calcipitrol>>P2Y2=Placebo 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
Dr H Parmar

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.

Dr H Parmar

Dermal Microdialysis & Microdosing

•Single Dose and/or
•Mutiple Dose including Dose Ranging Possible
In the same subject

Dr H Parmar

Asthma & COPD

Dr H Parmar

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
Shape CD11b
550 10

525
9

500

8
475

450
7

Donor 3 425

6
400

375
5

350

4
325

300 3
-12 -11 -10 -9 -8 -7 -6 -11 -10 -9 -8 -7 -6 -5

550 10

525
9

500

8
475

450
7
Donor 5 425

6
400

375
5

350

4
325

300 3
-12 -11 -10 -9 -8 -7 -6 -11 -10 -9 -8 -7 -6 -5

Dr H Parmar

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

ASTHMA NORMAL
35 35
30
30
NO ppb

25 PLACEBO 25
20 20
15 15
10 10 PLACEBO
SD3651 (L-NILTA)
5 5
0 SD3651
0
0 2 4 6 8 12 24 48 72 0 2 4 6 8 12 24 48 72
Hours
Dr H Parmar

COPD PoP: Biomarker Discovery
In Vivo In Vitro

Elastin breakdown
COPD
Control specific peptides

Differential Comparison
Lung destruction Fraction 38, Mass 1286

Elastin degradation

500
products

400
Peptide Index

Intensity

300
200
Exhaled breath
condensate

100
Sputum

0
Control COPD

Control COPD
Blood
Urine

Dr H Parmar
Discovery Medicine, Astrazeneca Size of peptide

Novel Markers-Proteomic analysis of plasma
Stable and Acute Exacerbation, COPD
% relative expression of total pooled sample

Protein 1 Protein 2

100 100
80 80
60 60
40 40
20 20
0 0
Control Stable COPD AE Control Stable COPD AE

Protein 3 Protein 4

100 100
80 80
60 60
40 40
20 20
0 0
Control Stable COPD AE Control Stable COPD AE

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

Dr H Parmar

Core Problem:
The migration of raw data into useable knowledge
Current State of
BioPharma Industry Knowledge

User-Integrated Information
• Predictive Modeling:
Information – Disease progression models
– Toxicity Models
–Efficacy Models

Data

Raw Data: Integrated and
• DNA Array Contextualized Data:
• Sequence Data
• Toxicity Data

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

Dr H Parmar

Biomarkers & Tv Humanising Drug Discovery

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