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					Oncology BioMarkers in
Adaptive Clinical Trial Design
Bhardwaj Desai, MD,

Kendle
 Outline


• Why biomarkers?
• Biomarkers and surrogate end points
• Examples
• Challenges




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    Goulart, B. H.L. et al. Clin Cancer Res 2007;13:6719-6726


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The Promises of Biomarkers


• In 2007 more that 34,000 papers dealing with biomarkers have been published
• Biomarkers are a child of the genomics technologies
      – reduce risk in drug development (pharma)
      – improve patient outcomes (healthcare providers)
• Activities
      – earlier diagnosis
      – patient stratification
      – assessment of drug toxicity and efficacy
      – disease staging
      – disease prognosis




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Two types of stratification will entail
different consequences
Patient stratification                                          Disease stratification
• Different dosing based on patient                             • Different drugs given based on patient
  genotype                                                        genotype

• Could increase market size                                    • Would decrease market size for an
                                                                  individual drug
• Change to get into occupied market
                                                                • Emphasis on a group of ‗minibusters‘
• The ‗Blockbuster‘ model of drug
                                                                  rather than one blockbuster
  development would still hold
                                                                • Expanding indications to other diseases
• Expanding the patient subgroup by
                                                                  with same underlying genetic cause of
  growing experience
                                                                  disease
     – Herceptin
                                                                      – Glivec


                                                                              Modified from Shah, Nat Biotech 2003

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 Definitions
 (NIH Definitions Working Group)

• Biomarker
 A characteristic that is measured and evaluated as an indicator of normal biologic
 processes, pathogenic processes, or pharmacologic processes to a therapeutic
 intervention.

• Clinical endpoint
 A characteristic or variable that measures how a patient feels, functions,
 or survives.

• Surrogate endpoint
 A biomarker intended as a substitute for a clinical endpoint.




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Scientific challenges


• Biomarker/transcript profile selection
• Definition of response predictors
• Assay development
      – Platform and reagent standardization
      – Defining sensitivity
      – Minimizing variability
• Pharmacodynamic modeling
• Biomarker validation
• Biomarker ≠ surrogate




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Biomarker Validation


• A biomarker that is measured in an analytical test system with well established
  performance characteristics and for which there is an established scientific
  framework or body of evidence that elucidates the physiologic, toxicological,
  pharmacologic, or clinical significance of the test results.




        From FDA Guidance for Industry: Pharmacogenomic Data Submissions. March 2005.
                        http://www.fda.gov/cber/gdlns/pharmdtasub.pdf

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Surrogate Endpoint Definition


• A laboratory measurement or physical sign that is used in therapeutic trials as
  a substitute for a clinically meaningful endpoint that is a direct measure of how
  a patient feels, functions, or survives and is expected to predict the effects of
  the therapy.




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 Clinical correlates: surrogate endpoint
 biomarkers used for evaluation of oncology
 drugs and biological products
• Objective Response/ Response Rate
• Time to Progression
• Disease free survival or time to recurrence
• Progression-free survival
• Quality of life, symptom improvement, composite endpoints
• Intraephithelial neoplasia
 IEN are precancers that are treated by drug therapy or surgical removal. Regression of
 existing or prevention of new IEN have been considered for supporting approval of
 drugs to prevent cancers or to treat precancers



                                                                                                                       Kelloff, 2005

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Why Use Surrogate Endpoints?


• Faster decisions
• Smaller trials
• Some accepted as predicting a clinical outcome
      – Blood pressure - heart attack and stroke
      – Bone mineral density - risk of osteoporotic fractures
      – Viral loads - progression of HIV


                                      BUT must translate into clinical benefit!




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How are Biomarkers and
Surrogate Endpoints Related?

• Biomarker is a ―candidate‖ surrogate marker

• Biomarker data alone cannot be used to register a product unless it is
  accepted as a surrogate endpoint

                                   All surrogate endpoints are biomarkers

                            but not all biomarkers are surrogate endpoints!!




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Pros/Cons of Biomarkers


• Pros:
      – Objective
      – Change more rapidly than other endpoints
      – Improved efficacy and safety – individualized medicine
      – May reduce drug development costs
      – May speed time to market

• Cons:
      – Validation – complicated and costly
      – Few validated biomarkers known




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 Types of Biomarkers


• Translation Biomarker:               a biomarker that can be applied in both a
                                       preclinical and clinical setting.
• Disease Biomarker:                   a biomarker that relates to a clinical
                                       outcome or measure of disease.
• Efficacy Biomarker:                  a biomarker that reflects beneficial
                                       effect of a given treatment.
• Staging Biomarker:                   a biomarker that distinguishes between
                                       different stages of a chronic disorder.
• Surrogate Biomarker:                 a biomarker that is regarded as a valid
                                       substitute for a clinical outcomes measure.
• Toxicity Biomarker:                  a biomarker that reports a toxicological
                                       effect of a drug on an in vitro or in vivo
                                       system.
• Mechanism Biomarker: a biomarker that reports a downstream
                                       effect of a drug.
• Target Biomarker:                    a biomarker that reports interaction of
                                       the drug with its target.
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     Predictive biomarkers used in oncology
     drug development
Name                                Definition                                   Examples
Drug effect/ pharmacodynamic        Biological effects produced by a drug        Effect on molecular target (e.g., EGFR inhibition, RAS
markers                             that may or not be directly related to       farnesylation inhibition)
                                    neoplastic process
                                                                                 Induction of enzyme activity relevant to drug toxicity
                                                                                 (e.g., CYP1A1, CYP1A2)
                                                                                 Functional (and molecular) imaging of drug interaction
                                                                                 at target tissue



Cellular, histopathological, and    Biological effects occurring during          Quantitative pathology or cytology of cancers,
imaging biomarkers                  neoplastic progression                       precancers, high-risk tissue




                                                                                 Functional imaging (e.g., FDG-PET)

                                                                                 Genomic and proteomic expression profiles

                                                                                 Proliferation biomarkers (e.g., PCNA, Ki-67)

                                                                                  Apoptosis biomarkers (e.g., BCL-2 expression,)



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Monitoring Tumor Response to
Treatment In Vivo

• FDG-PET as an early indicator of response to chemotherapy or
  radiation therapy in some cancers

• PET imaging with radiotracers could be employed as a surrogate marker

• Trial endpoint both in phase 3 studies and at the "go/no go" decision point
  in phase 2 clinical trials




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 There are already several tumor associated
 Markers with (proven?) predictive value

• ß-HCG (Choriocarcinoma)

• ß-HCG (Testicular Tumors)

• AFP (Testicular Tumors)

• AFP (Hepatocellular Carcinoma)

• Calcitonin (Medullary Thyroid Carcinoma)

• Thyroglobulin (Differentiated Thyroid Cancer)

• PSA (Prostate Cancer)

• Monoclonal protein (multiple myeloma)


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Biomarker driven development/ Predictive medicine
Why will it start in oncology?
Clinics
• Cancer is a family of complex and heterogeneous diseases

• Awareness of new technologies (eg. Genotyping)

• Oncology deliver clear quality of life benefits & survival periods

• Efficacy and safety of established therapies is low (20-40%)

• Narrow therapeutic index of conventional drugs

Market
• Subsets of cancer patients are small, new Rx aimed for them would not threat
  the blockbusters
• High competitive pressure (several drugs in several pipelines)
• Reimbursement easier for Rx with clear cost-benefit ratios (pricing)
• High public awareness that cancer is an increasing disease
• Possibility for pharma companies becoming a niche leader
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Use of Biomarker in clinical practice


• Herceptin (Trastezumab) is a monoclonal Antibody against the her2/neu receptor

• HER-2 is over expressed or amplified in 25-30% of all women with breast cancer

• Herceptin is efficacious in ~20% of HER-2 positive patients

• The overall response rate in total target population is about 5%
      – Three diagnostic tests FDA approved (costs < $100)

      – Screening valuable until > 1.5% responders (est. treatment costs are $7000 per patient)




Adrian Towse, Office of Health Economics




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One more


WALL STREET JOURNAL. , May 5, 2005. CANCER DRUG DEEMED
FAILURE, HELPS ASIANS
“Iressa as proved effective at treating lung cancer in Asian patients, even as it flopped
in helping Caucasians, Blacks and just about everyone else…through a curious quirk in
medicine. Asians respond well to therapy because they have a certain genetic mutation
in their cancer cells that Iressa is good at targeting...”



“…As a result, Astra-Zeneca which initially planned big sales of Iressa in the US, is now
adjusting its marketing plan to focus on Japan, China and other Asian markets.”




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Irinotecan (Camptosar®)



• Irinotecan ~ proven 1st (5-FU and leucovorin) and 2nd line prodrug therapy
  for metastatic colon/rectal cancer

• Providers/patients face a clinical predicament ~ what is the optimal dose?
     – Incidence of grade 3-4 neutropenia is 35%

     – Nearly 70% of patients need dose reduction

     – Toxicity associated with active drug exposure


                                  “…causes severe myelosuppression…”
                     “...death due to sepsis following myelosuppression…”
                             “...adjust doses based on neutrophil count…”


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Problem: accumulation of SN-38


• Exposure dependent on metabolism of camptosar by UGT1A1
• Prodrug (irinotecan) metabolized to SN-38 (active drug)
• Rate-limiting metabolic enzyme encoded by UGT1A1
     – Wide interpatient variability in UGT1A1 activity

     – Patients with *28 variant (7 TA repeats) have reduced enzyme activity

     – Homozygous deficient (7/7 genotype) patients have the greatest risk of neutropenia

     – Neutropenia matters to patients

• Original label was silent on UGT information; approved dose not optimized




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Camptosar Label Revised and FDA Approved
UGT Test

                                                                ―Individuals who are homozygous for the
                                                                UGT1A1*28 allele are at increased risk for
                                                                neutropenia following initiation of CAMPTOSAR
                                                                treatment. A reduced initial dose should be
                                                                considered for patients known to be
                                                                homozygous for the UGT1A1*28 allele (see
                                                                DOSAGE AND ADMINISTRATION).
                                                                Heterozygous patients (carriers of one variant
                                                                allele and one wild-type allele which results in
                                                                intermediate UGT1A1 activity) may be at
                                                                increased risk for neutropenia; however, clinical
                                                                results have been variable and such patients
                                                                have been shown to tolerate normal starting
                                                                doses.‖




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 EGFR as a therapeutic target


– Epidermal growth factor receptor (EGFR) gene (erbB1) first sequenced in a
  four-member family of structurally related type or subclass 1 receptors known
  as tyrosine kinases.
– Critical for mediating the proliferation and differentiation of normal
  cell growth
– Widely expressed in epithelial, mesenchymal, and neuronal tissues
– Aberrant activation of the kinase activity of these receptors appears to play a
  primary role in solid tumor development and/or progression
– Breast, brain, lung, cervical, bladder, gastrointestinal, renal and head
  and neck squamous cell carcinomas, have demonstrated an over expression of
  EGFR relative to normal tissue, which is associated with
  a poor clinical prognosis




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Erlotinib (Tarceva®)


• Potent EGFR tyrosine kinase inhibitor

• Pre-clinical anti-tumor activity
     – Inhibits tumor cell line growth

     – Activity in mouse xenograft models

• Increased RR, PFS, and OS in Phase 3




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       Erlotinib vs. placebo in NSCLC

                                      1.0
                                      0.9
                                                                                                       HR 0.73*
                                      0.8                                                              P<0.001
                                      0.7
                          Surviving



                                                                                                       Erlotinib N=488
                                      0.6                                                              Median 6.7 mos
                                      0.5
                                      0.4
                                      0.3
                                      0.2       Placebo N=243
                                                Median 4.7 mos
                                      0.1
                                      0.0
                                            0            5             10              15               20              25
*HR is from the Cox regression model with the following covariates: ECOG performance status, number of prior regimens, prior platinum
and best response to prior chemotherapy. P-value is from two-sided Log-Rank test stratified by ECOG performance status,
number of prior regimens, prior platinum and best response to prior chemotherapy.


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Erlotinib in EGFR+ NSCLC


                     1.0
                     0.9
                                                                                   HR 0.65
                     0.8                                                           P=0.03
                     0.7
     Surviving




                                                                                   Erlotinib N=78
                     0.6                                                           Median 10.7 mos
                     0.5
                     0.4
                     0.3
                     0.2            Placebo N=49
                                    Median 3.8 mos
                     0.1
                     0.0
                              0                5               10     15      20                                    25
                                                               SURVIVAL (mos)

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Angiogenesis



• Bevacizumab, Sorafenib, Sunitinib and Temsirolimus, have been approved
  for clinical use on the basis of results from randomized phase III clinical trials
  without significant contributions from biomarkers. No validated biomarkers of
  angiogenesis or antiangiogeneic activity are available for routine clinical use



• Biomarkers of angiogenesis might be useful for monitoring angiogenesis,
  assessing drug activity and distinguishing between active and inactive drugs,
  predicting clinical outcome and response to therapy, defining the optimum
  biological dose, facilitating development of combination therapies, and rapidly
  identifying resistance to treatment



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Current pharmacogenomic examples


• bcr/abl or 9:22 translocation—imatinib mesylate (Gleevec)*
• HER2-neu—trastuzumab (Herceptin)**
• C-kit mutations—imatinib mesylate (Gleevec)**
• Thiopurine S-methyltransferase—mercaptopurine and azathioprine*
• UGT1A1-irinotecan (Camptosar)**
• Cytochrome P-450 (CYP) 2D6—5-HT3 receptor antagonists and
  codeine derivatives*
• *-FDA package insert information
• *-FDA-approved device




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Confounding factors and bias
why biomarker studies fail

• Accuracy of phenotype (disease) is critical
     – All patients must have same disease

     – Several causes lead to the same phenotype

• Inappropriate Dx method
• Inappropriate sample sizes / control groups
• Most diseases are multifactorial by nature (phenotype is affected by variants
  in numerous genes)

• The same biomarker signature can result in different phenotypes due to
  the effects of age, sex, environment, concomitant diseases, nutrition,
  co medication….


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Summary


• Biomarkers hold enormous promise
     – Conventional oncology development - small benefit in a large patient population

     – Targeted drug development – may define large benefit in smaller population

• The devil will be in the details

• Validation is crucial (tools and profiles)

• New development structures must be built
     – Flexible regulatory mechanisms

     – Need for drug-diagnostics co-development paradigm

     – Need for new partnerships between industry, government, academics



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                                                Thank you




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