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New Approaches to Clinical Trial Design Development of New Drugs


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Title: New Approaches to Clinical Trial Design Development of New Drugs

New Approaches to Clinical Trial Design
Development of New Drugs Predictive Biomarkers
  • Richard Simon, D.Sc.
  • Chief, Biometric Research Branch
  • National Cancer Institute

  • Powerpoint presentations
  • Reprints
  • BRB-ArrayTools software
  • BRB-ArrayTools Data Archive
  • Sample Size Planning
  • Targeted Clinical Trials
  • Phase II Trials
  • Developing Predictive Classifiers

  • Surrogate endpoints
  • Of treatment effect
  • Of patient benefit
  • Prognostic marker
  • Pre-treatment measurement correlated with
    long-term outcome
  • Predictive classifier
  • A measurement made before treatment to predict
    whether a particular treatment is likely to be

Surrogate Endpoints
  • It is extremely difficult to properly validate a
    biomarker as a surrogate for clinical benefit.
  • It requires a series of randomized trials with
    both the candidate biomarker and clinical outcome
  • Biomarkers can be useful in phase I/II studies as
    indicators of treatment effect and need not be
    validated as surrogates for clinical benefit.

Predictive Classifiers
  • Most cancer treatments benefit only a minority of
    patients to whom they are administered
  • Particularly true for molecularly targeted drugs
  • Being able to predict which patients are likely
    to benefit would
  • save patients from unnecessary toxicity, and
    enhance their chance of receiving a drug that
    helps them
  • Help control medical costs
  • Improve the efficiency of clinical drug

If new refrigerators hurt 7 of customers and
failed to work for another one-third of them,
customers would expect refunds.BJ Evans, DA
Flockhart, EM Meslin Nature Med 101289, 2004
Oncology Needs Predictive Markers not Prognostic
Pusztai et al. The Oncologist 8252-8, 2003
  • 939 articles on prognostic markers or
    prognostic factors in breast cancer in past 20
  • ASCO guidelines only recommend routine testing
    for ER, PR and HER-2 in breast cancer
  • With the exception of ER or progesterone
    receptor expression and HER-2 gene amplification,
    there are no clinically useful molecular
    predictors of response to any form of anticancer

Oncology Needs Predictive Markers not Prognostic
  • Many prognostic factor studies use a convenience
    sample of patients for whom tissue is available.
    Generally the patients are too heterogeneous to
    support therapeutically relevant conclusions

  • Targeted clinical trials can be much more
    efficient than untargeted clinical trials, if we
    know who to target

  • In new drug development, the role of a predictive
    classifier is to select a target population for
  • The focus should be on evaluating the new drug in
    a population defined by a predictive classifier,
    not on validating the classifier

Developmental Strategy (I)
  • Develop a diagnostic classifier that identifies
    the patients likely to benefit from the new drug
  • Develop a reproducible assay for the classifier
  • Use the diagnostic to restrict eligibility to a
    prospectively planned evaluation of the new drug
  • Demonstrate that the new drug is effective in the
    prospectively defined set of patients determined
    by the diagnostic

Develop Predictor of Response to New Drug
Using phase II data, develop predictor of
response to new drug
Patient Predicted Responsive
Patient Predicted Non-Responsive
Off Study
New Drug
Applicability of Design I
  • Primarily for settings where there is a
    substantial biological basis for restricting
    development to classifier positive patients
  • eg HER2 expression with Herceptin
  • With substantial biological basis for the
    classifier, it may be ethically unacceptable to
    expose classifier negative patients to the new

Evaluating the Efficiency of Strategy (I)
  • Simon R and Maitnourim A. Evaluating the
    efficiency of targeted designs for randomized
    clinical trials. Clinical Cancer Research
    106759-63, 2004.
  • Maitnourim A and Simon R. On the efficiency of
    targeted clinical trials. Statistics in Medicine
    24329-339, 2005.

  • Efficiency relative to trial of unselected
    patients depends on proportion of patients test
    positive, and effectiveness of drug (compared to
    control) for test negative patients
  • When less than half of patients are test positive
    and the drug has little or no benefit for test
    negative patients, the targeted design requires
    dramatically fewer randomized patients

No treatment Benefit for Assay - Patientsnstd /
Proportion Assay Positive Randomized Screened
0.75 1.78 1.33
0.5 4 2
0.25 16 4
Treatment Benefit for Assay Pts Half that of
Assay Pts nstd / ntargeted
Proportion Assay Positive Randomized Screened
0.75 1.31 0.98
0.5 1.78 0.89
0.25 2.56 0.64
  • Metastatic breast cancer
  • 234 randomized patients per arm
  • 90 power for 13.5 improvement in 1-year
    survival over 67 baseline at 2-sided .05 level
  • If benefit were limited to the 25 assay
    patients, overall improvement in survival would
    have been 3.375
  • 4025 patients/arm would have been required
  • If assay patients benefited half as much, 627
    patients per arm would have been required

Interactive Software for Evaluating a Targeted
  • http//

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Developmental Strategy (II)
Developmental Strategy (II)
  • Do not use the diagnostic to restrict
    eligibility, but to structure a prospective
    analysis plan.
  • Compare the new drug to the control overall for
    all patients ignoring the classifier.
  • If poverall? 0.04 claim effectiveness for the
    eligible population as a whole
  • Otherwise perform a single subset analysis
    evaluating the new drug in the classifier
  • If psubset? 0.01 claim effectiveness for the
    classifier patients.

Developmental Strategy (IIb)
  • Do not use the diagnostic to restrict
    eligibility, but to structure a prospective
    analysis plan.
  • Compare the new drug to the control for
    classifier positive patients
  • If pgt0.05 make no claim of effectiveness
  • If p? 0.05 claim effectiveness for the
    classifier positive patients and
  • Continue accrual of classifier negative patients
    and eventually test treatment effect at 0.05

Key Features of Design (II)
  • The purpose of the RCT is to evaluate treatment T
    vs C overall and for the pre-defined subset not
    to re-evaluate the components of the classifier,
    or to modify or refine the classifier

The Roadmap
  1. Develop a completely specified genomic classifier
    of the patients likely to benefit from a new drug
  2. Establish reproducibility of measurement of the
  3. Use the completely specified classifier to design
    and analyze a new clinical trial to evaluate
    effectiveness of the new treatment with a
    pre-defined analysis plan.

Guiding Principle
  • The data used to develop the classifier must be
    distinct from the data used to test hypotheses
    about treatment effect in subsets determined by
    the classifier
  • Developmental studies are exploratory
  • Studies on which treatment effectiveness claims
    are to be based should be definitive studies that
    test a treatment hypothesis in a patient
    population completely pre-specified by the

Use of Archived Samples
  • From a non-targeted negative clinical trial to
    develop a binary classifier of a subset thought
    to benefit from treatment
  • Test that subset hypothesis in a separate
    clinical trial
  • Prospective targeted type (I) trial
  • Using archived specimens from a second previously
    conducted clinical trial

Development of Genomic Classifiers
  • Single gene or protein based on knowledge of
    therapeutic target
  • Single gene or protein culled from set of
    candidate genes identified based on imperfect
    knowledge of therapeutic target
  • Empirically determined based on correlating gene
    expression to patient outcome after treatment

Use of DNA Microarray Expression Profiling
  • For settings where you dont know how to identify
    the patients likely to be responsive to the new
    treatment based on its mechanism of action
  • Only pre-treatment specimens are needed

Development of Genomic Classifiers
  • During phase II development or
  • After failed phase III trial using archived
  • Adaptively during early portion of phase III

Development of Empirical Gene Expression Based
  • 20-30 phase II responders are needed to compare
    to non-responders in order to develop signature
    for predicting response
  • Dobbin KK, Simon RM. Sample size planning for
    developing classifiers using high dimensional DNA
    microarray data, Biostatistics 8101-117,2007

Adaptive Signature Design An adaptive design for
generating and prospectively testing a gene
expression signature for sensitive patients
  • Boris Freidlin and Richard Simon
  • Clinical Cancer Research 117872-8, 2005

Adaptive Signature DesignEnd of Trial Analysis
  • Compare E to C for all patients at significance
    level 0.04
  • If overall H0 is rejected, then claim
    effectiveness of E for eligible patients
  • Otherwise

  • Otherwise
  • Using only the first half of patients accrued
    during the trial, develop a binary classifier
    that predicts the subset of patients most likely
    to benefit from the new treatment E compared to
    control C
  • Compare E to C for patients accrued in second
    stage who are predicted responsive to E based on
  • Perform test at significance level 0.01
  • If H0 is rejected, claim effectiveness of E for
    subset defined by classifier

Biomarker Adaptive Threshold Design
  • Wenyu Jiang, Boris Freidlin Richard Simon
  • JNCI 991036-43, 2007

Biomarker Adaptive Threshold Design
  • Randomized phase III trial comparing new
    treatment E to control C
  • Survival or DFS endpoint

Biomarker Adaptive Threshold Design
  • Have identified a predictive index B thought to
    be predictive of patients likely to benefit from
    E relative to C
  • Eligibility not restricted by biomarker
  • No threshold for biomarker determined

  • S(b)log likelihood ratio statistic for treatment
    versus control comparison in subset of patients
    with B?b
  • Compute S(b) for all possible threshold values
  • Determine TmaxS(b)
  • Compute null distribution of T by permuting
    treatment labels
  • Permute the labels of which patients are in which
    treatment group
  • Re-analyze to determine T for permuted data
  • Repeat for 10,000 permutations

  • If the data value of T is significant at 0.05
    level using the permutation null distribution of
    T, then reject null hypothesis that E is
  • Compute point and interval estimates of the
    threshold b

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Validation of Predictive Classifiers for Use with
Available Treatments
  • Should establish that the classifier is
    reproducibly measurable and has clinical utility

Developmental vs Validation Studies
  • Developmental studies should select patients
    sufficiently homogeneous for addressing a
    therapeutically relevant question
  • Developmental studies should develop a completely
    specified classifier
  • Developmental studies should provide an unbiased
    estimate of predictive accuracy

Limitations to Developmental Studies
  • Sample handling and assay conduct are performed
    under controlled conditions that do not
    incorporate real world sources of variability
  • Small study size limits precision of estimates of
    predictive accuracy
  • Predictive accuracy may not reflect clinical

Types of Clinical Utility
  • Identify patients whose prognosis is sufficiently
    good without cytotoxic chemotherapy
  • Identify patients who are likely or unlikely to
    benefit from a specific therapy

Prognosis Good Without Chemotherapy
  • Develop prognostic classifier for patients not
    receiving cytotoxic chemotherapy
  • Identify patients for whom
  • current practice standards imply chemotherapy
  • Classifier indicates very good prognosis without
  • Withhold chemotherapy to test predictions

Prospectively Planned Validation Using Archived
  • Fully specified classifier developed using data
    from NSABP B20 applied prospectively to frozen
    specimens from NSABP B14 patients who received
    Tamoxifen for 5 years
  • Good risk patients had very good relapse-free

B-14 ResultsRelapse-Free Survival
Paik et al, SABCS 2003
Prospective ValidationUS Intergroup Study
  • OncotypeDx risk score lt15
  • Tam alone
  • OncotypeDx risk score gt30
  • Tam Chemo
  • OncotypeDx risk score 15-30
  • Randomize to Tam vs Tam Chemo

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  • Measure classifier for all patients and randomize
    only those for whom classifier determined therapy
    differs from standard of care

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Types of Clinical Utility
  • Identify patients whose prognosis is sufficiently
    good without cytotoxic chemotherapy
  • Identify patients whose prognosis is so good on
    standard therapy S that they do not need
    additional treatment T
  • Identify patients who are likely or unlikely to
    benefit from a specific systemic therapy

Developmental Strategy (II)EGFR Inhibitor in
Validation Study for Identifying Patients Who Do
Not Benefit from Standard Adjuvant Regimen
  • Standard adjuvant treatment S
  • Classifier based on previous data for identifying
    patients who do not benefit from S relative to
    previous control C
  • RCT of S vs C for patients predicted not to
    benefit from S
  • Its difficult to go back
  • Alternatively, RCT comparing S vs new treatment T
    for patients predicted not to benefit from S

  • Contains analysis tools that I have selected as
    valid and useful
  • Targeted to biomedical scientists with analysis
    wizard and numerous help screens
  • Imports data from all platforms and major
  • Extensive built-in gene annotation and linkage to
    gene annotation websites
  • Extensive gene-set enrichment tools for
    integrating gene expression with pathways,
    transcription factor targets, microRNA targets,
    protein domains and other biological information

Predictive Classifiers in BRB-ArrayTools
  • Classifiers
  • Diagonal linear discriminant
  • Compound covariate
  • Bayesian compound covariate
  • Support vector machine with inner product kernel
  • K-nearest neighbor
  • Nearest centroid
  • Shrunken centroid (PAM)
  • Random forrest
  • Tree of binary classifiers for k-classes
  • Survival risk-group
  • Supervised pcs
  • Feature selection options
  • Univariate t/F statistic
  • Hierarchical variance option
  • Restricted by fold effect
  • Univariate classification power
  • Recursive feature elimination
  • Top-scoring pairs
  • Validation methods
  • Split-sample
  • Repeated k-fold CV
  • .632 bootstrap

  • Publicly available for non-commercial use
  • http//

  • New technology makes it increasingly feasible to
    identify which patients are likely or unlikely to
    benefit from a specified treatment
  • Targeting treatment can greatly improve the
    therapeutic ratio of benefit to adverse effects
  • Smaller clinical trials needed
  • Treated patients benefit
  • Economic benefit

  • Some of the conventional wisdom about how to
    develop predictive classifiers and how to use
    them in clinical trial design is flawed
  • Prospectively specified analysis plans for phase
    III studies are essential to achieve reliable
  • Biomarker analysis does not mean exploratory
    analysis except in developmental studies

  • Achieving the potential of new technology
    requires paradigm changes in correlative
  • Effective interdisciplinary research requires
    increased emphasis on cross education of
    laboratory, clinical and statistical/computational

  • Kevin Dobbin
  • Boris Freidlin
  • Wenyu Jiang
  • Aboubakar Maitnourim
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