0830 St rzebecher
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Transcript 0830 St rzebecher
The Feasibility of Personalized
Medicine
Steffen Stürzebecher, M.D., Ph.D.
Global PGx, Biomarker Development and NonClinical Statistics
ACCP and AGAH 2006 joint meeting
The „hope vs. hype dilemma“ of the „Personalized
Medicine“ claim
One has to admit that the early promises have been as much an
overestimation as the recent warnings appear to be overly
cautious, that a relevant contribution of predictive personalized
medicine will take another one or two decades to materialize...
...with regard to a broad use of tools to improve the
personalized use of drugs, we face, however, more than only
the challenges of “omics” tools, biomarker validation and proof
of utility, i.e. the whole spectrum of societal aspects from cost
utility of “response testing” to orphanized disease sub-entities
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What have we learned yesterday already?
• There are quite a number of different meanings of BM
and their respective purpose
BM may be useful in drug development at different stages
without delivering tools for personalized medicine in clinical
practice (more the rule than the exception)
even BM that had been around for a long time, e.g. PSA or
Prolactin do not correlate (good enough) with treatment outcome
only very few BMs are considered (true) surrogate markers
a growing number of companies have adopted policies that
require BM strategies to be in place before a drug is further
promoted through the development pipeline (e.g. Pfizer)
with regard to risk reduction and improved benefit/risk profiles,
and you
should have
a 15will
minutes
training
unit with
the development
remote control
regulatory
agencies
put more
pressure
on the
before you give your talk
of predictive markers
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not yet
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Two „extremes“ of personalized medicine (PM)
• Clinical Development approach: Better stratification or
selection of patients in clinical trials = better prediction for
a GROUP
e.g. by sub-entity of disease,
polymorphisms of target,
polymorphisms in ADME
• can also be applied if selected group has only gradually
better benefit than „all-comers“
• Clinical practice: improve the use of the existing
armamentarium of therapeutic and preventive drugs =
better prediction for an individual patient
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More than two options: The potential bridge
between clin. dev‘t and medical practice:
stratification marker
with moderate/marked increase
of probability to respond
phase II
better
stratification marker (moderate)
used and confirmed;
effect in „non-carriers“ = minimal
worse
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phase III
post approval
since minimal response in
„non-carriers“, marker
becomes „test“ and label requires
prior testing accordingly
ACCP and AGAH 2006 joint meeting
Current and future contribution of „omics“ to personalized
medicine
•
The new „omics“ tools certainly broaden the scope and
increase the chances (beyond their research and dev. use)
of re-defining disease subentities
discovering new prognostic markers of the disease
finding prediction markers of treatment outcome and tolerability
improving treatment monitoring
guiding escalation therapy approaches and drug combinations
by BM monitoring
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The idea of PM is not new in principle!
• Drug development as well as medical practice have
always tried to select and „enrich“ patients with regard to
the following aspects:
early in drug development, e.g. more homogenous patient
population in phase II than in phase III
inclusion criteria that give the drug the „best chance to be
effective“
more effective less tolerable drugs to be used later in treatment
schedules
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The new era of „omics“ based search for BMs to
eventually enable PM is different with regard to:
• biostatistical and bioinformatic approach to interpret and
control the data
hypothesis free vs. hypothesis driven approach
multiplicity of data and pathway context of data
• Regulatory environment
• Public perception, e.g. overly optimistic or critical
expectations
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Current and future contribution of „omics“ to personalized
medicine
•
The first examples beyond the „famous“ Herceptin, Gleevec,
Irinotecan, Abacavir, Iressa stories appear to support that there is
„justified hope“
Example of prognostic RNA Expression Profiling Signature in
Breast Cancer (van de Vijver et al.)
Example of RNA Expression Profiling Signature to predict
treatment outcome of Taxane therapy in breast cancer
Example of ovarian cancer diagnostic proteomics signature
Example of DNA Methylation Marker (PIXT 2) in Breast Cancer
prognosis and prediction of treatment outcome
...most of them have still to stand up to independent confirmation
and to proof of clinical validity and utility...
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The example of Breast Cancer
• A signature clearly distinguishing risk GROUPS overall
and for LN + and LN- patients could be confirmed in three
consecutive studies (van de Vijver et al., van‘t Veer et al),
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The example of Breast Cancer
• However, when trying to predict
individual patients‘ outcome, the
high odds ratios (13.7-15.3) and
low p-values (p<0.001) don‘t
translate into high accuracy of
individual outcome:
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The example of Breast Cancer
•Initial
a Biomarker
short
study Test
+ of fulfilling
Test – the criteria of becoming a
n=78
•Surrogate
However, when
trying
to predict
marker
can
still
contribute
(pos.
progn.)
(neg.
progn.) a lot to increase
individual
outcome,
the
certaintypatients‘
in 26
treatment
option
Disease
18 selection and treatment
high
odds
ratiosas
(13.7-15.3)
andto established prognostic
free
>
5 yrs.
montitoring
compared
low p-values (p<0.001) don‘t
Disease
criteria into
andhigh
(NIHof31
etc.)
3scores
translate
accuracy
dist.
met. <outcome:
individual
5yrs.
Sensitivity Specificity PPV
NPV
Initial study
N=78
59%
91%
89%
63%
Confirmatory
Study N=295
52%
93%
96%
38%
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Current and future contribution of „omics“ to personalized
medicine
•
Careful bridging from the infancy of a new biomarker era to
a new paradigm in drug development and public health is
necessary
to avoid disappointment of public expectations with the
consequence of e.g. withdrawal of public money, e.g. in the
European FPs, Innovative Medicines Initiative
to avoid overly optimistic expectations within
pharmaceutical companies with the consequence of
reduced budgets for „omics“ and biomarker search in
development projects
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Current and future contribution of „omics“ to personalized
medicine
...
to mitigate the notion that drug development will become
less expensive in the short term
to avoid the notion – or to adequately address- that
diseases will be subsegmented to a point where part of
them will no longer be in the focus of drug development –
orphan indications of the reverse type
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A (few) word(s) on Incentives and Hurdles
•Hurdles:
•(company) internal hurdles:
the usual fear in Marketing of sub-segmenting the market
the extra burden for Clinical Development to safeguard sampling
for PGx (and the extra budget, e.g. for a middle-size phase III trial
~200-300 TEU for sample and save alone)
uncertainty (unjustified) regarding IRBs‘ reaction to supplement
pharmacoGENETIC protocols
the extra „miles“ colleagues in Reg. Affairs and in Project
Management have „to go“
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A (few) word(s) on Incentives and Hurdles
•Hurdles:
•potential IRB/EC and other ethical hurdles:
harmonization of IRBs/ECs with regard to pharmacogenetic
studies still lacking (although not a major issue in our
experience )
once a „probable valid biomarker“ e.g. predicting response and
non-response to a drug has been identified, it may be considered
unethical to still perform studies in all-comers (even if drug were
effective on an all-comers basis but with a strong impact of the
„predictor“)
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„Genetic Exceptionalism“ – a few cautionary remarks
• Altough one can argue for good reasons that genetics
don‘t represent data of different „weight“ and sensitivity as
compared to any other medical data,...
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„Genetic Exceptionalism“
•There is the perception in the public (including IRBs/ECs
and policy/law makers like the Council of Europe) that
genetic data including pharmacogenetic data deserve
special (data) protection
long term storage and use of samples for „genetics“ with large
scale analysis of genes
potential new prognoses/diagnoses, e.g. of course of disease
becoming possible (not revealed by phenotype)
• Industry will have to cope with this perception
• It is all the more important to distinguish
between disease genetics and e.g. pharmacogenetic research
between DNA related tests and dynamic genomic tests
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Disease genetics
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Pharmacogenetics
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Pharmacogenetics
Disease genetics
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Pharmacogenetics
Disease genetics
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Expression profiling,
Proteomics,
somatic mutations..
ACCP and AGAH 2006 joint meeting
A (few) word(s) on Incentives and Hurdles
•Hurdles:
•regulatory and legal hurdles
even embarking on PGx sampling and associated „claims“ in a
study protocol can constitute the problem of being challenged to
make best use of the samples and findings
Regulatory agencies can re-define the level of impact of PGx data
(regardless of what the sponsor‘s claims are) on a drug‘s dossier
(e.g. FDA‘s GDS guidance)
which is, of course, appropriate given their duties to protect
public health
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A (few) word(s) on Incentives and Hurdles
•Hurdles:
•regulatory and legal hurdles
Push of regulators and/or e.g. third party payers to develop a
validated test to „translate“ PGx findings into a routine testing
tool.
Including the requirement of validation studies for PGx
biomarker development
Non-harmonized legislation e.g. regarding data protection and
biobanking
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A (few) word(s) on Incentives and Hurdles
•Potential Incentives:
•(company) internal
focussed indication rather than no indication at all (salvage of
drugs by PGx based stratification)
increased confidence of doctors and patients in treatment with
perdictive test; improved adherence to treatment
earlier attrition through employing PGx based biomarker related
endpoints in early development
last but not least (rather not in our/Schering‘s case) the additional
commercial value of a Dx
(use) patent extension for Dx/Rx combinations
improved image of pharmaceutical industry re good care for the
patient and for public health
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Schering’s approach exemplified:
250
200
150
100
overall: 1900 RNA samples
50
0
screen
day1
ing
M1
M2
M3
M6
M9
M12
M18
M24
EOS
others.
before
Placebo
3
146
11
147
143
8
3
110
3
74
54
0
Verum
6
243
15
226
225
36
10
206
9
159
59
4
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...this study can be used
• to develop prognostic markers of natural course of
disease
• to discover predictors of treatment outcome
• to search for markers that help monitor disease activity
• to search for markers that help monitor treatment efficacy
• to further investigate the MoA of the drug of interest....
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In case of doubt, treat the patient
•Our favorite scenarios for the performance
characteristics of a test in this case:
high sensitivity - in identifying all responders to drug treatment ...
more important to treat / enhance compliance of the maximum
number of patients ...
minimum number of false negatives, i.e patients falsely
excluded from / taken off drug
... at the price of limited specificity - i.e. rather accepting false
positives
since the drug has a good benefit/risk profile
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A (few) word(s) on Incentives and Hurdles
•Potential Incentives:
•by regulators, legislation, reimbursement policies:
Re-consideration of „patient subgroup based orphan drug status“
Option for patent extension (comparable to pediatric indications)
based on new biomarkers guiding treatment decisions (consider
re-defined biomarker dependent use of a drug as a label
extension)
Special reimbursement policy for drugs with biomarker guided
prescription schemes
Promotion of research and development of new biomarkers for
better and safer treatment by public health policies
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Beyond the development of new medical entities
and research- how do deal with the drugs used
today?
• Consequent use of ADME relevant pharmacogenetics
• Public funds needed to promote the pharmacogenetics
around generic drugs!
• Laboratory, reporting and counseling standards and
qualtity controls have to be established
and different purposes of BM use should be considered with
regard to stringency of requirements for certification
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courtesy Ron Zimmern
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The ethical and societal dimensions of PM:
• Learning more about disease outcome prognostic
markers and treatment outcome predictors will gradually
change the paradigms of medicine again rendering molecular
medicine much more a routine application
may change paradigms of cost-utility analyses and quality
assurance in the public health sector
needs more and better health and disease conscience education
increased need for counselling to enable patients to chose
from options together with their doctors
may lead to re-consideration of private insurance risks
and may need legislation or self-control to avoid „unjustice“
may leave certain subgroups of patients untreated (sub-optimally
treated)
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Old and new tools of „Personalized Medicine“
Classical Biomarker application - Physician‘s Eye & Ear
clinical symptoms / scores & their change under therapy
assessing the response to / outcome of treatment
Anatomical & functional imaging - CT, MRI, PET, SPECT
mechanism of action: PTK-ZK --> early changes in vascular
permeability (DCE-MRI)
hints for clinical efficacy: number / size of MS brain lesions under
treatment
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Old and new tools of „Personalized Medicine“
Molecular Biomarkers - *-omics, biochemical assays,
clinical chemistry
discovery - validation - routine laboratory test
open “*-omics“ analyses - signatures / panels - single analytes
All of these „tools“ have been used to stratify clinical
studies as well as to select optimal treatment for patients
in clinical routine use
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General Definition of Biomarker
Biological marker - Biomarker
A characteristic that is objectively measured and evaluated
as an indicator of normal biologic processes, pathogenic
processes, or pharmacologic responses to a therapeutic
intervention
Biomarkers Definitions Working Group
“Biomarkers and Surrogate Endpoints: Preferred Definitions and Conceptual Framework“
Clinical Pharmacology & Therapeutics 69, 2001
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Early Dis-Proof Scenarios
Lack of Tolerance (Marker) / SAEs
MTD
“bad compound“ signal by early markers of toxicity /
lack of tolerance (preclinical / phase I)
Type I BM
Dose
“weak“ compound (phase I)
BM detects no / weak biological drug effect related to desiredMTD
MoA @
MTD in phase I
BM detects sub-maximal biological drug effect @ MTD in phase I
Dose
good compound & bad target (phase I & phase II)
BM detects strong / maximum biological drug effect (phase I/II)
but: desired MoA not translating into clinical effect (phase II)
Optimal
Dose
Clinical
Reponse
(Marker)
Dose
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Drivers of Personalized Medicine
• In addition to a growing need to better steer and control
health budgets, to avoid unnecessary or harmful drug
treatment,
• the needs and „musts“ in pharmaceutical development
will be a major driver to explore the options of PM
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PM needs and demands in drug development:
„Internal needs“
Speed, early attrition / selection of development candidates,
risk reduction in Clinical Development
External Demands
Ethics (committees) & scientific interest
Regulators
may demand BM development where improvement of Tx risk / benefit
evident or likely
e.g. from own PGx, external PGx data submissions, literature, state of the
art
may not accept “exploratory“ status of submitted data
have established guidelines for biomarker development (GDS
guidance) and offer support and collaboration (e.g. PG Briefing
Meetings with CHMP, FDA; FDA “Critical Path Initiative“)
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PM needs and demands in drug development:
Economics
future reimbursement policies: patient stratification as key to product
sales
new response predictor can support LCM of mature product
earnings from commercialization of BM test as non-strategic “byproduct“
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availability of BMs may reduce duration of phase II
support dose finding in phase I / phase II
focus on biological response rather than on MTD, smaller dose range
shorter exposure to effect by use of early response / tox markers or
surrogate endpoints
less activities in toxicology needed to cover exposure time in patients
option of staggered designs (phase I / IIa): early-into-patients
dose escalation in volunteers narrowly preceding dose escalation in patients
BM in phase IIb and III may allow for development in (highly)
enriched patient populations & may shorten time to first approval
BM may rescue a compound only active in a sub-population of
patients
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The protection of the patient from misuse of
pharmacogenetic data is key to the progress in this field
• As unlikely as it is today that pharmacogenetic testing
may lead to predictors of disease and/or treatment
outcome with high enough probability to draw
consequences regarding insurance coverage, this cannot
be completely ruled out and, therefore, it is of utmost
relevance to the progress in the field - that there are/is:
Moratoria by private health and life insurers to not regard results
of genetic testing in decision on insurance coverage or insurance
premium (e.g. Netherlands, UK, Germany)
Legislation banning the use of genetic test results by employers,
insurers or in any other context of potential discrimination (e.g.
Austria, France, Italy, Netherlands)
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