The „hope vs. hype dilemma“ of the „Personalized Medicine“ claim
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Transcript of The „hope vs. hype dilemma“ of the „Personalized Medicine“ claim
The Feasibility of Personalized Medicine
Steffen Stürzebecher, M.D., Ph.D. Global PGx, Biomarker Development and Non-Clinical Statistics
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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, regulatory agencies will put more pressure on the development of predictive markersand you should have a 15 minutes training unit with the remote control
before you give your talk
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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
stratification marker (moderate)used and confirmed;
effect in „non-carriers“ = minimal phase III
better
worse
post approval
since minimal response in „non-carriers“, marker
becomes „test“ and label requires prior testing accordingly
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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
• 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:
Initial study n=78
Test +
(pos. progn.)
Test –
(neg. progn.)
Disease
free > 5 yrs.26 18
Disease
dist. met. < 5yrs.
3 31
Sensitivity Specificity PPV NPV
Initial study
N=78
59% 91% 89% 63%
Confirmatory
Study N=295
52% 93% 96% 38%
• a Biomarker short of fulfilling the criteria of becoming a Surrogate marker can still contribute a lot to increase certainty in treatment option selection and treatment montitoring as compared to established prognostic criteria and scores (NIH etc.)
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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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•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“
A (few) word(s) on Incentives and Hurdles
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•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“)
A (few) word(s) on Incentives and Hurdles
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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 Pharmacogenetics
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Disease geneticsPharmacogenetics
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Disease geneticsPharmacogenetics Expression profiling,
Proteomics, somatic mutations..
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•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
A (few) word(s) on Incentives and Hurdles
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•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
A (few) word(s) on Incentives and Hurdles
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•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
A (few) word(s) on Incentives and Hurdles
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Schering’s approach exemplified:
overall: 1900 RNA samples
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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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•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
A (few) word(s) on Incentives and Hurdles
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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“
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
Classical Biomarker application - Physician‘s Eye & Ear
clinical symptoms / scores & their change under therapy
assessing the response to / outcome of 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
“weak“ compound (phase I)
BM detects no / weak biological drug effect related to desired MoA @ MTD in phase I
BM detects sub-maximal biological drug effect @ MTD in phase I
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)
“bad compound“ signal by early markers of toxicity / lack of tolerance (preclinical / phase I)
Lack of Tolerance (Marker) / SAEs
Dose
Type I BM
MTD
Dose
MTD
Dose
Optimal Dose
ClinicalReponse(Marker)
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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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„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“)
PM needs and demands in drug development:
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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 “by-product“
PM needs and demands in drug development:
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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)