Transcript Wyeth Research All-Hands

Strategic Challenges in
Neuroprotective Drug
Development
Christopher C. Gallen, M.D., Ph.D.
Vice President, Wyeth Research
March 15, 2003
Washington, D.C.
The Current World of Pharma

The Big Picture Challenge for R&D-Driven
Pharmaceutical Companies

The Challenges of CNS R&D

Meeting the Challenge

Changing the Model

A Strategy Going Forward
Health Care Costs
As a Percentage of Gross Domestic Product in Major
Industrialized Countries, 1997
15%
14.0%
12%
9.3%
9.9%
10.4%
8.5%
9%
6.7%
7.3%
7.6%
6%
3%
0%
U.K.
Japan*
Italy Netherlands Canada France Germany
*1997 data
Source: OECD-OECD Health Data, 1998.
U.S.
Pharmaceutical Costs
1.8%
1.7%
1.6%
1.4%
1.2%
1.0%
1.2%
1.2%
1.5%
1.5%
Italy
Japan*
1.3%
1.1%
0.9%
0.8%
0.6%
0.4%
0.2%
0.0%
Netherlands U.K.
U.S.
*1997 data
Source: OECD-OECD Health Data, 1998.
Canada Germany
France
What Happens When a Patent Expires?
Prozac Total Prescriptions Per Month
2,500,000
1,500,000
1,000,000
Prozac
Generic
500,000
15
13
11
9
7
5
3
1
-2
-4
-6
-8
-10
-12
0
Total Rx
2,000,000
The Patent Expiration
Challenge
AstraZeneca 50%
Schering-Plough 41%
Eli-Lilly 36%
Merck 27%
Bristol-Myers 26%
Roche 24%
Aventis 22%
GSK 18%
Pfizer 14%
Nov
8%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
% of Total Sales to 2005 Vulnerable to Patent Expiration
Over $100B of Products Face Generic Competition by 2005
Source: Cap Gemini Ernst and Young, 2002. Global Market Research & Analysis
Pharmaceutical R&D
Investment is High
Research-based Pharmaceutical Companies1
17.0%
Domestic R&D
Global R&D
15.6%
Industrial Sector Comparison:
Drugs & Medicine
12.8%
Computer Software & Services
10.5%
Electrical & Electronics
8.4%
Office Equipment & Services
7.8%
Telecommunications
5.3%
Leisure Time Products
4.7%
Automotive
3.9%
Aerospace & Defense
3.8%
Metals & Mining
Paper & Forest Products
All Industries
1.2%
0.73%
3.9%
Source: PhRMA, 2001, Based on Data from PhRMA Annual Survey and Standard & Poor’s
Compustat, a Division of McGraw-Hill
Percentage of companies active in each
therapeutic area
Nervous system
Nervous system
Cancer
Cancer
Cardiovascular
Cardiovascular
Alimentary & Metabolism
Alimentary & Metabolism
Antiinfectives
Antiinfectives
Musculo-skeletal
Musculo-skeletal
GU & Sex hormones
GU & Sex hormones
Respiratory
Respiratory
Blood
Blood
Dermatological
Dermatological
Major companies
(n=14)
Hormones
Sensory organs
Other companies
(n=24)
Hormones
Sensory organs
0%
20%
40%
60%
80% 100%
Percentage of companies
0%
20%
80% 100%
Percentage of companies
Therapeutic area ordered by decreasing number of
NASs in development on December 31st, 2001
Source: Institute for Regulatory Science
40% 60%
IO0-10099
24/05/02
Nervous system NASs dominate the
development pipeline
Nervous System
Nervous System
Alimentary &
Metabolism
Cancer
Alimentary &
Metabolism
Cancer
Cardiovascular
Antiinfectives
Antiinfectives
Cardiovascular
Musculoskeletal
Musculoskeletal
Respiratory
Respiratory
0
20
40
60
80
100 120
140
Number of NASs in development
Source: Institute for Regulatory Science
0
5
10
15
20
25
Number of NASs first tested in man in 2001
But R&D Productivity is
Decreasing
60
35
50
30
40
25
20
30
15
20
10
10
Source: PhRMA Annual Survey, 2001. U.S. FDA. Global Market Research & Analysis
2002E
2001
2000
1999
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
0
1980
5
0
New Molecular Entities Approved
R&D Expenditures (US$ Billions)
40
Millions of 2000 $
Discovery and Development
Costs are Increasing
900
800
700
600
500
400
300
200
100
0
802
466
336
318
214
84
Preclinical
1970s approvals
54
104
Clinical
1980s approvals
Source: DiMasi et al., Tufts CSDD R&D Cost Study, 2002
138
Total
1990s approvals
Clinical Trial Number Per NDA
is Increasing
Number of Trials
80
68
70
60
60
50
40
36
30
30
1977-80
1981-84
30
20
10
0
1985-88
1989-92
1994-95
Source: Boston Consulting Group, 1993; Peck, C., “Drug Development: Improving the Process,”
Food & Drug Law Journal, Vol. 52, 1997.
Number of Patients Per NDA is
Increasing
Number of Patients
4,500
4,237
4,000
3,567
3,233
3,500
3,000
2,500
2,000
1,500
1,576
1,321
1,000
500
0
1977-80 1981-84 1985-88 1989-92 1994-95
Source: Boston Consulting Group, 1993; Peck, C., “Drug Development: Improving the Process,”
Food Drug Law Journal, Vol. 52, 1997.
Number of patients per phase III study to
support
first
submission
Mean number of patients
1200
1000
800
600
400
200
0
Therapeutic area
Where enrolment completed 1999-01
R&D Cycle Times are Increasing
16
14.8
14.2
Years
14
12
11.6
10
2.1
8
6
4
2
2.8
6.1
8.1
2.4
2.6
5.5
4.1
2.5
6.1
5.1
5.9
1970s
1980s
3.2
0
1960s
Pre-IND Phase
IND Phase
1990s
NDA Phase
Source: Joseph A. DiMasi, “New Drug Development; Cost, Risk and Complexity”, Drug Information
Journal, May 1995. (From R&D Directions, 1996)
Mean Approval Time (Months)
Drug Approval Times are
Increasing Again
42
36
30.3 29.9
30
26.5
24
19.7 19.2
17.8
18
17.6 16.4
16.2
11.7 12.6
12
6 30
26
25
22
28
53
39
30
35
27
24
92
93
94
95
96
97
98
99
00
01
0
91
Calendar Year
Source: U.S. Food and Drug Administration
Total Number
of New Drugs
Approved in
Each Year
Time to termination by therapeutic area
(for NASs terminated 1999-2001)
Nervous system Anti-infectives Cardiovascular
Percentage of NASs terminated
100
75
50
25
0
0.0
2.0
4.0
6.0
8.0
10.0
Time from first human dose (years)
Source: CMR International
12.0
14.0
Breakdown of reasons for termination
(for NASs terminated 1999-2001)
Percentage of terminations
50%
45%
Nervous system (n=127 )
40%
All therapy areas excluding nervous system (n=360)
35%
30%
25%
20%
15%
10%
5%
0%
Clinical efficacy
Source: CMR International
Clinical
Pk/bioavailability
Clinical safety
Portfolio
considerations
Toxicology
Various
Attractiveness profile of industry’s late
stage pipeline
High success rate,
fast cycle time
100
90
39
Anti-infectives
Musculo-skeletal
80
42
18
70
Alimentary/
metabolism
25
3.4
2.9
19
2.4 60
CVS
Respiratory
43
Oncology
Nervous system
51
Low success rate,
slow cycle time
1.9
1.4
50
Success rate: phase III to submission
High success rate,
slow cycle time
40
30
Fast cycle time,
low success rate
Average duration of phase III (decreasing left to right)
Bubble size = current market size (IMS); number in bubble = number of NASs in phase II/III development
Why are Success Rates
Declining?

Discovery issues

Conceptual issues re disease models

Clinical Trial issues
Genomic Targets: Promise and
Concerns


The Promise - improved diagnostics, fundamentally
targeted treatments
Reality: Proliferation of “targets” - but targets with a
limit

Within target heterogeneity

Challenging targets - known models of molecular
dysfunction

Most targets likely “loss of function”

Large market diseases polygenic

Twin concordance rates disturbing
Technological Challenges

Structure-based Drug Design
Match molecules to targets different from in-situ
conformation
Fit for in vitro viral proteins likely > CNS proteins

Combinatorial Chemistry
Existing libraries limited by origins - monoamine
GPCRs, steroid receptors and serine-aspartyl proteases
Why is CNS Particularly
Challenging?

Normal Functioning
Intimate connections, fine timing and pattern code
Parallel paths, multiple systems/step
Instantaneous mutual regulation
Self regulation of the system over time

Antagonists versus agonists

Single target bullets best for probes

Therapies generally multi-target
CNS Disease Animal Models
can be Misleading

Model congruity with disease
Understand the animal model
Understand the human disease
Show them to be congruent in all important respects

Cell Culture
Cell-cell interactions, relation to nutritional systems,
exogenous environment, phospholipid composition all
differ

Mouse Models
Major failures of single genes
Strain differences suggest a cause for concern
Meeting the Challenge:
Clinical Rigor

Success rates are too low to tolerate avoidable
flaws

Animal testing under one set of conditions,
human trials under another

Ignoring the “does it make scientific sense?”
test

Animal models measuring very different
dependent variables

Inadequate determination of dose and duration
Using Technology to do Better
Trials
Key: Near-time trial conduct and analysis
 Scrutinize blinded data to detect poor sites
 Exploratory development - double-blind but
not triple blind
 Exploratory data analysis oriented database
and approach for better programs and
submissions
 Modeling and simulation for better trials
 Adaptive trial designs to optimize doseranging

Experimental Medicine Part of the Solution

Is the compound absorbed?

Does the compound penetrate to the desired
site of action? For appropriate period of time?

Mechanism consistent with hypothesis?

Biological effect?

Free of class-associated limiting toxicities?
Disease Models
Reality is a complex set of interactions
 Each step can be modeled as differential equations

Myriad publications describe individual pieces
Supplemented with research to test the model
Technology allows generation of increasingly
sophisticated disease models
 Stronger model will produce the insights on target
selection and effective therapies
 Core Intellectual Property

Electronic Technologies can
Improve Chemistry

NIH Protein Structure Initiative

Increased supercomputer modeling of protein
folding and interactions

Virtual screening

Virtual combinatorial chemistry

Moving past target to cross-assessing
potential toxic interactions and metabolism
Biological Technologies Have
Great Promise

35% of the 37 NAS launched in 2001

Biologics have important attractions

Typically less toxic, more predictable
Increasingly human derived
Easier to predict distribution, metabolism and
elimination

Faster development

Higher success rates

Huge ability to match potential targets
Changing the Business Model
Historical
Platform oriented
First line treatments, one size fits all, mass population, easy
(oral) treatment, ameliorating chronic disease
One treatment per disease
 Next Generation
Disease focus
Defined populations
Administered by specialists
Targeted treatments
Expand treatments to capture therapeutic subpopulations
Polypharmacy in cases (similar to oncology development)

Pharma and Academicians

Partnership
Intellectual challenge of deciphering targets
Building disease models

Closer ongoing collaborative contact
Remote presence technologies
Secure e-data sharing
Pharma and Regulators

Shifting to a model of early POC studies in
man for both target and molecule validation
calls for earlier consultations

Partnership

Closer ongoing collaborative contact

Rolling dossiers

Marketing rights will change from being oneoff to continuous evaluation
A Strategy Going Forward

Focus on intellect and collaboration

Pharma focus on disease model

Experimental medicine model

Tap the power of the information revolution

Tap the power of biologic-based technologies

Adapt the Business Model