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

AGHA_February 200620.02.062

Global Pharmacogenomics, Schering AG

ACCP and AGAH 2006 joint meeting

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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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• 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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• 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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• 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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• 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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... 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“)





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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





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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





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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





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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





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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)

the „hope vs. hype dilemma“ of the „personalized medicine“ claim

Documents

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