1. Pharmacogenomics links patients to targeted therapies using biomarkers to determine the right drug, dose, and disease for the right patient, leading to improved outcomes. 2. Analysis of gene expression data from rheumatoid arthritis patients can identify marker sets to stratify patients for novel drug development and identify pathways involved in disease. 3. Studying epithelial gene expression in COPD patients can define disease progression clusters and identify potential drug targets in relevant biochemical pathways. 4. Several drugs in oncology, HIV, hepatitis C, diabetes, and rheumatoid arthritis have demonstrated or potential for personalized medicine approaches using biomarkers.

1. Application of Pharmacogenomics To
Personalised Medicine and R & D
Dr Harsukh Parmar
Global Discovery Medicine
Respiratory & Inflammation Therapy Area
harsukh.parmar@astrazeneca.com

2. 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
Source: Tufts CSDD Approved NCE Database, PhRMA

3. Main Reasons for Termination of Development
LACK OF EFFICACY & SAFETY !
One Size Does NOT Fit ALL !
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%

5. The co-existence of genetic polymorphisms in drug metabolizing enzymes, targets,
receptors, and transporters, in the context of drug and non-drug influences, may
result in high frequencies of unusual drug reaction phenotypes.

6. Current Treatment is Population Based

7. What is Personalised Medicine?
Personalised Medicine links the patient to a disease (segment or
part of the disease) to a drug using a diagnostic or biomarker or
clinical test that:
• Defines the disease and/or
• Predicts response and risk and/or
• Determines dose
Leading to improved patient outcomes, targeted therapies and new
commercial opportunities. Personalised Medicine involves testing
patients prior to treatment to enable clinicians to prescribe:
• The Right Drug
• At the Right Dose
• For the Right Disease
• To the Right Patient

8. Pharmacogenomics –Making Personalised Medicines

9. Patient Segmentation is Not New
•Historically we have always done this using
Clinical and Biochemical features:
!Inclusion/Exclusion Criteria in Clinical
Trials
!Regulatory Approved Data sheets often
define the approved indications and
subset of patients suitable for the
approved therapy

10. So What Has Changed ?
•The vast array of technology to define patient subgroups
•These range from biochemical, immunocytochemistry,
genetics, proteomics, to new evolving technology such as
real time chemotaxis assays
•Molecular re-classification of disease through genotype
•Better understanding & use of biomarkers for patient
stratification
•Better understanding & use of biomarkers for patient
segmentation & enriched clinical trials
•Greater societal expectation on efficacy and safety
•Increasing costs leading to better targeted therapies

12. Pharmacogenomics Promise
Individualized Medicine
•New diagnostic procedures
(pharmacogenomic tests)
•Better matches between
patient, disease, therapy and
outcome
•Impact on R&D as well as
Sales and Marketing

14. Importance is clear and growing
• BMS - Taxol: first cancer NSCLC treatment with
blockbuster, now facing generic TAXOL
39
competition 40 Taxol Response rate
• Novel taxane about to enter (%)
Median survival
market 30 (months)
• Beta-tubulin gene contains 20
mutations that predict for 10
patterns of response and 10
0 2
resistance 0
• Beta-tubulin pharmacogenomic Wild-
Type
Mutated
N=16
N=33
test for differential prescription: Genotype
Taxol or taxane

15. Discovery Medicine
Utilize and Integrate Human
Pathophysiology and Disease Models
ProteinDomain
COPD2
Target Validation
COPD0
COPD1
Clinical Data
NS
Platforms
Cytoband
HS
Deliverables
NA
•Genetics
•Genomics
GO
•Proteomics 15 19 18 9 16 2 •Validated targets
•Metabonomics •Pathophysiological
•Lipidomics understanding
•Glycomics •Biological Mechanism
•Imaging •Disease stratification
Annots
•Epidemiology •Biomarkers
•Physiology •Patient segmentation
20/04/2005
Bioinformatics and Informatics
15

17. Rheumatoid Arthritis

18. GENE EXPRESSION ANALYSIS USING GENELOGIC DATA

19. GenelogicTM Expression Data
!Pathways that are significant to the pathophysiology of
Rheumatoid Arthritis and Anti-TNF treatments have been
highlighted in the table.
!Knowledge of immune response genes can potentially be
useful for identification of surrogate markers of clinical endpoint
or disease/treatment/response markers according to the project
needs.

20. Overview of Analysis
• Gene expression data from three types of sample
populations analyzed:
! WBC samples from Normal individuals
! WBC samples from Rheumatoid Arthritis patients.
! WBC samples from RA patients, 6 weeks after
Remicade Infusion.
• Set of 25 genes were identified as a marker set for
patient stratification in future novel NME target
discovery and development.

21. Micro-array Analysis in RA-Treated with Steroids
• Analysis of covariance. The
distribution of p-values
allowed identification of
genes with altered gene
expression on steroids.
CD68 Immunohistochemistry
pre post
prednisolone
placebo
CD68
100x
Dr H Parmar
Experimental Medicine

22. Biomarkers of disease
progression
COPD

23. Analysis of Epithelial Gene Expression in COPD
Smokers
with/without Brushings (bronchial epithelial cells) Primary cell-based model
COPD
Non-smokers Define the biochemical
Microarrays
Microarrays pathways initiated by
COPD related stresses
Bioinformatics
Clinical data •Smoke (CSE)
&
Statistical analysis
G a n a ig
en n n
ca
e ota (G
FEV1/FVC ratio
Bronchial biopsies
on ti O
m
p
to on AC
Identify differentially
Identify differentially
lo
gy
expressed genes
expressed genes
)
Confirm expression in in
Confirm expression in in
disease tissue
disease tissue Generate hypotheses,
Generate hypotheses,
identify targetable
identify targetable
molecules in pathways
molecules in pathways
IHC/in situ Functional assays
Cytokine production
Differentiation, Proliferation
Secretion, Motility
Candidate Targets

24. Disease progression cluster (Gene Expresion)
(GOLD 0,1 &2; decreasing FEV1) NAME
nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/
tubulin, gamma 1
carboxylesterase 1 (monocyte/macrophage serine esterase 1)
ProteinDomain
carboxyl ester lipase (bile salt-stimulated lipase)
COPD2
COPD0
COPD1
cholesterol 25-hydroxylase
SPARC-like 1 (mast9, hevin)
low density lipoprotein receptor (familial hypercholesterolemia)
Cytoband
NS
HS
prostate stem cell antigen
NA
carboxypeptidase E
gastrin-releasing peptide
fer-1-like 3, myoferlin (C. elegans)
killer cell lectin-like receptor subfamily C, member 3
DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide, Y chromosome
GO
ribosomal protein S4, Y-linked
15 19 18 9 16 2 killer cell lectin-like receptor subfamily C, member 3
small inducible cytokine A5 (RANTES)
small inducible cytokine A5 (RANTES)
secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymp
secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymp
Cluster Incl. AF070536:Homo sapiens clone 24566 mRNA sequence /cd
S100 calcium binding protein A10 (annexin II ligand, calpactin I, light poly
mucin 1, transmembrane
aldehyde dehydrogenase 1 family, member A3
cytochrome P450, subfamily I (aromatic compound-inducible), polypeptid
Annots cytochrome P450, subfamily I (dioxin-inducible), polypeptide 1 (glaucoma
cytochrome P450, subfamily I (dioxin-inducible), polypeptide 1 (glaucoma
annexin A3
transmembrane 4 superfamily member 1
transcobalamin I (vitamin B12 binding protein, R binder family)
cystatin A (stefin A)
uroplakin 1B
S100 calcium binding protein P
claudin 10
carcinoembryonic antigen-related cell adhesion molecule 5
carcinoembryonic antigen-related cell adhesion molecule 6 (non-specific
• Hierarchical clustering of genes carbonyl reductase 1
UDP glycosyltransferase 2 family, polypeptide B
ubiquitin carboxyl-terminal esterase L1 (ubiquitin thiolesterase)
hypothetical protein MGC13523
• Subjects ordered in disease progression Pirin
aldo-keto reductase family 1, member B10 (aldose reductase)
malic enzyme 1, NADP(+)-dependent, cytosolic
• N=79, Expression data from U133A&B glutathione peroxidase 2 (gastrointestinal)
phosphogluconate dehydrogenase
thioredoxin
aldo-keto reductase family 1, member C1 (dihydrodiol dehydrogenase 1;
alcohol dehydrogenase 7 (class IV), mu or sigma polypeptide
transaldolase 1
aldo-keto reductase family 1, member C3 (3-alpha hydroxysteroid dehyd
NAD(P)H dehydrogenase, quinone 1

25. Human Knock-Out Initiated Projects, Entered Phase III

30. Disease reclassification at the
molecular level

31. Molecular classification of Acute Leukaemia
Golub TR et al. Science 1999; 286: 531
!Genes distinguishing ALL
from AML The 50 genes that
correlate most highly between
ALL and AML are shown.
!The top panel shows genes
that are highly expressed in
ALL, whereas the bottom panel
shows genes more highly
expressed in AML.
!While as a group, these genes
are correlated with pathologic
class, no single gene is
uniformly expressed across the
class, illustrating the value of
whole-genome expression
analysis in class prediction

40. Nanosphere, Inc - Novel technology detects human DNA mutations

43. Speed and Simplicity Verigene Mobile
Since it is based on direct
genomic detection and not target !The next generation Verigene Mobile
will transfer the power and accuracy
amplification, ClearRead makes
of the Verigene AutoLab to an
molecular testing faster and affordable, hand-held device.
simpler. Current methods require
highly specialized scientists and !Its portability will make it ubiquitous
lab technicians for processing and at point-of-care settings such as
interpretation, while ClearRead doctor's offices, hospital bedsides and
assays are easy to perform and even in patients' homes.
produce definitive results.

46. Herceptin

54. Drugs with Personalised Medicine Properties/Potential
•Herceptin in Oncology
•Protease Inhibitors in HIV
•Protease Inhibitors in HCV
•Diabetic Treatment & Monitoring
•Neuroamidase Inhibitors in Influenza e.g. Tamiflu, Relenza
•Rituximab, Anti-CD20 in NHL, RA etc
•Xolair, Anti-IgE in asthma
•Anti-TNF’s & Anti-IL1 in RA
•Campostar in Oncology
•Xeloda, Gemcitabine, Velcade in Oncology
•Taxol & Taxanes in Oncology
•UDF in Oncology
•EGFR Antibodies & TK inhibitors e.g. Tarceva, Iressa
•Potentially VEGF Antibodies (Avastin) and TK inhibitors
•Various Monoclonal Antibody Targets