Lydia Pan, PhD
Director, Worldwide Policy, Pfizer Inc.
Presentation to the Cancer Action Coalition of Virginia
September 12, 2013
Innovating to Better Care:
How personalized medicine is changing the
biopharmaceutical marketplace
Personalized Medicine: Towards a Definition
“Personalized medicine” refers to the tailoring of medical treatment to the
individual characteristics of each patient. It does not literally mean the creation of
drugs or medical devices that are unique to a patient, but rather the ability to
classify individuals into subpopulations that differ in their susceptibility to a
particular disease or their response to a specific treatment. Preventive or
therapeutic interventions can then be concentrated on those who will benefit,
sparing expense and side effects for those who will not
.
Report of the President’s Council of Advisors on Science and Technology,
September 2008
The right
drug
…for
the right person
…in
the right
dose
…at the right time
1
Older Drugs were Developed Empirically
–
Source of data: Brian B. Spear, Margo Heath
-
Chiozzi
, Jeffery Huff, “Clinical
Trends in Molecular Medicine,” Volume 7, Issue 5, 1 May 2001, Pages 201
-
204.
2
Today’s Medicines are Developed with
More Precision
Medicines targeting
patient segments that
will have an
optimal
response
to therapy
Building disease
understanding to
identify the right
pathways and targets
Linking disease
understanding
and clinical
outcomes
Precision Medicine
Segmented (not personalized)
3
Recognition of Leukemia and Lymphoma
Sub
-
types has Improved Outcomes
100
years ago
Disease of the blood
80
years ago
Leukemia or lymphoma
60
years ago
Chronic leukemia
Acute leukemia
Preleukemia
Indolent lymphoma
Aggressive lymphoma
Today
~38 leukemia types identified:
•
Acute myeloid leukemia (~12 types)
•
Acute lymphoblastic leukemia (2 types)
•
Acute promyelocytic leukemia (2 types)
•
Acute monocytic leukemia (2 types)
•
Acute erythroid leukemia (2 types)
•
Acute megakaryoblastic leukemia
•
Acute myelomoncytic leukemia (2 types)
•
Chronic myeloid leukemia
•
Chronic myeloproliferative disorders (5 types)
•
Myelodysplastic syndromes (6 types)
•
Mixed myeloproliferative/myelodysplastic
syndromes (3 types)
51 lymphomas identified:
•
Mature B
-
cell lymphomas (~14 types)
•
Mature T
-
cell lymphomas (15 types)
•
Plasma cell neoplasm (3 types)
•
Immature (precursor) lymphomas
(2 types)
•
Hodgkin’s lymphoma (5 types)
•
Immunodeficiency
-
associated
lymphomas ~ 5 types)
•
Other hematolymphoid neoplasm's
(~7 types)
5
-
Yr
Survival
~0
%
70%
Source:
Malorye
, Allison. “Is Personalized Medicine Finally Arriving?”
Nature Biotechnology
,
May 2008
4
The Human Genome: A Great Opportunity
for Drug Discovery?
Biopharmaceutical R&D Investment and
New Medicines Approved
Sources:
Paraxel's
Pharmaceutical R&D Statistical Sourcebook 2005/2006; FDA;
PhRMA
6
12
22
30
20
21
20
23
23
30
26
25
22
28
53
39
30
25
27
24
17
21
36
20
22
18
24
26
21
30
39
3.2
3.6
4.1
4.8
5.7
6.5
7.3
8.4
9.6
11.6
12.7
13.4
15.2
16.9
19.0
21.1
22.7
26.0
29.8
31.0
34.5
37.0
39.9
43.4
47.9
47.4
46.4
50.7
48.6
48.5
$ Billiions
Number of Products
Year
Genomic
-
based Research Enables
Precision Medicine
Right Target
Right Patient
Goal to
improve survival
7
Drug targeted to specific
oncogene or aberrant
pathway driving the
specific tumor
Patient identified through
molecular profiling of
their tumor
Ultimate objective is to
improve survival
New treatment
Comparator
0
6
12
18
24
30
36
0
0.2
0.4
0.6
0.8
1.0
Overall Survival Probability
Months of survival
Phase 1
Phase 2
Phase 3
Clinical Development
Challenges for Coordination of
Rx/
Dx
Co
-
development
PMA (CDRH)
CDER/CBER
The FDA prefers to review both Rx &
Dx
applications concurrently.
Sponsor must coordinate between different FDA Centers
FDA has multiple programs to expedite drug/biologic development and review:
Fast Track, Accelerated Approval, Breakthrough Therapy, Priority Review
CDRH does not have similar mechanisms to accelerate diagnostic approval.
Diagnostic
Therapeutic
Drugs Labels with Genomic Biomarker
Information
Testing required
–
Trastazumab
/ breast cancer
FISH/IHC HER2
–
Panitumumab
/ colon cancer
KRAS
wildtype
–
V
emurafenib
/ melanoma
BRAF V600E
–
Crizotinib
/ NSCLC
ALK gene rearrangements
Ivacaftor
/ cystic fibrosis
CFTR G551D
T
esting
recommended
–
Abacavir
/ HIV AIDS
HLA
-
B 5701 variant
–
Irinotecan
/ colon cancer
UGT1A1 variant
–
Azathioprine
/ autoimmune
Thiopurine
methyltransferase
–
Warfarin
/ thrombosis, CV prophylaxis
CYP2C9, VKORC
Informational tests
–
Fluoxetine
/ depression
CYP2D6
–
Codeine / analgesia
CYP2D6
–
Clopidogrel
/ CV prophylaxis
CYP2C19
–
Chloroquine
/ malaria
G6PD deficiency
Adapted
from
Nature Biotechnology
, 25, 509
-
517,
2007; Table of
Pharmacogenomic
Biomarkers in Drug Labels
http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm
9
FDA
Framework for
Personalized Medicine
:
A
“
Mosaic
”
of Guidance Documents
Document Type
Title
Date
Concept Paper
Drug
-
Diagnostic Co
-
Development
April 2005
Guidance
Pharmacogenetic
Tests and Genetic Tests for
Heritable Markers
Feb 2006 (draft)
June 2007 (final)
Draft Guidance
Pharmacogenomic
Data Submissions
Aug 2007
Draft Guidance
In Vitro Diagnostic Multivariate Index Assays
Sept 2006 (draft)
Feb 2007 (public meeting)
July 2007 (revised)
2010 (withdrawn)
Draft Guidance
(FAQ)
Commercially Distributed In Vitro Diagnostic
Products Labeled for Research Use Only or
Investigational Use Only
June 2011
Draft Guidance
In Vitro Companion Diagnostic Devices
July 2011
Guidance
Clinical
Pharmacogenomics
: Premarketing
Evaluation in Early Phase Clinical Studies
January 2013
(draft Feb 2011)
Additional guidance documents forthcoming
I
III
II
III
II
CMS
(CLIA)
FDA
510k
PMA
All
Lab
-
Developed Tests
Distinct pathways
for
LDTs and
Dx
test
kits
Certification of laboratory
performance standards
Multiple Ways for Tests to Reach the
Marketplace
Risk
class
Clinical utility
required
Regulator
FDA wants all
CDx
to go
through PMA
Personalized Medicine: Key Components
•
Science & Technology
•
Driving the understanding of disease and the discovery and
development of medicines
•
Regulatory science advances
•
Medical Practice
-
What’s best for the patient?
-
Changes in medical practice
•
Health Care Environment
o
How do we get personalized
o
medicines to patients?
1
2
Understanding of Oncologic Drivers
is
Rapidly Increasing
References: 1.
Massachusetts
General Hospital, data on file
2.
Horn L, Pao W.
J Clin Oncol
2009;26:4232
–
5
13
Adenocarcinoma 1999
Histology
-
driven Selection
K
-
RAS
EGFR
B
-
RAF
HER
-
2
PIK3CA
ALK
MET
Unknown
Adenocarcinoma 2011
Targeting Oncogenic
Drivers
1
Evolving Personalized
Paradigm
Metastatic disease (
stage
IIIB/ IV)
Biomarkers can direct treatment
towards targeted therapy or
clinical trials (where
available)
EGFR
K
-
RAS
ERCC1
ALK
TS
B
-
RAF
HER
-
2
Traditional
Paradigm
Non
-
squamous
cell carcinoma
Metastatic disease (stage
IIIB/
IV)
Squamous
cell
carcinoma
Creating a New Paradigm for NSCLC
Treatment
Oncologist
sole treatment
decision maker
Treatment
decisions depend on histology
More
complex decisions involving more stakeholders
beyond oncologist (surgeon, pathologist)
Education required to integrate molecular diagnostics
into treatment decisions
Need for multiple molecular
Dx
creates competition for
available tissue, budget, manpower
Not a “simple” issue of a single drug
-
diagnostic
combination
Multiple test options
Biomarkers Support Expansion of Use
Therapeutic
Biomarker
Indication(s)
GLEEVEC
®
Imatinib
C
-
Kit
Gastrointestinal
stromal
tumors,
aggressive systemic
mastocytosis
Philadelphia
Chromosome
chronic myeloid leukemia, acute
lymphocytic leukemia
PDGFR
myelodysplastic/ myeloproliferative
diseases
FIP1L1
-
PDGFRα
hypereosinophilic
syndrome and/or
chronic
eosinophilic
leukemia
Adapted from GLEEVEC® prescribing information (www.novartis.com)
15
Payers Must Determine How to Pay for
Personalized Medicine
Test Coding:
•
AMA created
new Molecular Diagnostic
CPT codes for 2013
•
Retirement of code
-
stacking
•
Unique tests still not identified (McKesson Z
-
codes)
Coverage and Reimbursement:
•
CMS rolling
out new policies for molecular diagnostics
–
MolDx
test payments being set at local level by gap
-
filling; process
has not been transparent
–
Proposed payment determinations for products paid under the CLFS
included the decision NOT to pay for algorithm portion of multi
-
analyte
tests
•
Increasingly, payers are demanding high levels of clinical evidence to
justify the reimbursement of personalized medicine products
–
Challenges to generating timely evidence without denying or delaying
access to treatment
•
Targeted therapeutics increasingly subject to utilization management tools
16
Challenges to Personalized Medicine in
the Marketplace
•
Precision medicine may drive efficiencies in drug
development but application of technologies isn’t cheap
•
Drug development may or may not be less costly
•
If targeting smaller,
more
defined populations, medicines
should have greater efficacy / safety risk ratios but also
likely be more expensive
•
Diagnostics landscape is rapidly evolving
–
needs
investment to sustain innovation
•
Integrating each new intervention into healthcare
management takes time
•
Growing pressure to show PM improves health outcomes
•
Value loss if access is restricted
17
Rx to Deliver the Pipeline for Personalized
Medicine
•
Aggressive application of science to R&D
–
Informatics tools to analyze
large, multi
-
dimensional data
sets
–
Closer
industry
-
academia collaboration
to drive customized therapy
solutions
–
Novel clinical trial designs
that incorporate new drug development tools
–
Opportunities to
add value to existing and potential medicines
•
Secure systems
that allow
safe sharing of data
between health care providers,
industry and regulators to streamline development and approval processes
•
Collaborative
relationships with regulators
that strengthen patient safety but
also speed the approval of novel biomarker applications
and
Dx
technologies
•
Evidence standards
to demonstrate the effectiveness of diagnostics in
improving patient outcomes
18
Toward a Health Care System that Delivers
the Value of Personalized Medicine
•
Data systems
that assure security and access to the growing body of
patient data
•
Quality standards
to insure data compatibility and comparability
•
Integrated health information
: a complete systems
-
based readout
of the health status of an individual in a given environment
•
Physicians
need easy
-
to
-
interpret results
•
user
-
friendly technological interface
•
data from multiple sources
•
continuously refined algorithms and
database updates
•
Enabling functions
: standards,
infrastructure, systems approach,
sharing mechanisms
•
Education
along the entire health care ecosystem
Policy will determine success or failure of
personalized medicine implementation
19
Thank You!
Questions
???
20
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