1. Redefining Disease, New Molecular
Definitions and Personalised
Medicine
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. 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
7. Patient Segmentation is Not New
•Historically we have always done this using
Clinical, Biochemical, Histological 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
8.
9. Pharmacogenomics
Importance is clear and growing
• BMS - Taxol: first cancer NSCLC treatment with
blockbuster, now facing generic TAXOL
39
competition 40 Taxol Response rate
• Novel taxanes have entered (%)
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
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
11. 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
12.
13. 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
to Launch. the therapy, including adverse effects.
3. Biomarkers may help pre-select 3. Biomarker analysis can be expensive
patient populations that are most likely and time-consuming.
to benefit. 4. Biomarker-based decisions could
4. Biomarkers that predict the course of become biased unless a priori criteria
disease may serve as a useful tool for are set up for decision-making in
clinicians, health care systems. addition to biomarker data.
5. Diagnostic kits could be developed 5. Patient pre-selection using biomarkers
where appropriate patient
may reduce the potential market size.
segmentation may reduce the size of
trials required
14.
15. Biomarkers & Clinical Outcomes
•In a 15,000 patient study,
independent drug safety
committee recommended
stopping further development
since mortality was about 60%
(82 versus 51) higher in
Torcetrapib group.
•Biomarkers did not predict.
•However human genetics
(CTEP) in Japanese study did
potentially predict poor
outcome because of ineffective
“HDL” produced by such
inhibition
•Increase in BP may be another
factor for increased mortality
17. 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
27. 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.
28. 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.
29.
30.
31. Speed and Simplicity Verigene Mobile
!The next generation
Since it is based on direct
genomic detection and not target Verigene Mobile will transfer
amplification, ClearRead makes the power and accuracy of the
molecular testing faster and Verigene AutoLab to an
simpler. Current methods require affordable, hand-held device.
highly specialized scientists and
lab technicians for processing and !Its portability will make it
interpretation, while ClearRead ubiquitous at point-of-care
assays are easy to perform and settings such as doctor's
produce definitive results.
offices, hospital bedsides and
even in patients' homes.
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41. Drugs with Personalised Medicine Properties/Potential
•Antibiotics are Personalised Medicines
•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, Erbitux
•Potentially VEGF Antibodies (Avastin) and TK inhibitors
•Various Monoclonal Antibody Targets