An Update on FDA

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An Update on FDAs Critical Path
InitiativeStatistical Contributions

• Robert T. ONeill Ph.D.
• Director , Office of Biostatistics
• Center for Drug Evaluation and Research

Presented at the 2005 FDA/Industry Statistics
Workshop September 14-16, 2005 Marriott Wardman
Park Hotel, Washington, DC
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The Critical Path Initiative

• Refers to the product development path from
  candidate selection to product launch
• Covers drugs, biologics, and medical devices
  but todays talk is mostly about drugs /
  biologics
• Initiative was announced publicly by Dr.
  McClellan Tuesday, March 16, 2004

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What the Critical Path Is

• A serious attempt to bring attention focus to
  the need for more scientific effort and
  publicly-available information on evaluative
  tools
• Evaluative tools The techniques methodologies
  needed to evaluate the safety, efficacy quality
  of pharmaceuticals as they move down the path

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Despite Advances in Science, Success Rate of
Product Development has NOT Improved

• New compounds entering Phase I development today
  have 8 chance of reaching market, vs. 14 chance
  15 years ago.
• Phase III failure rate now reported to be 50,
  vs. 20 in Phase III, 10 years ago.

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Perceived Problem The development process
itself is becoming a serious bottleneck

• Current applied science and infrastructure date
  from last century
• Funding and progress in Development science has
  not kept pace with basic biomedical science.
• Science to evaluate safety and efficacy of
  potential new medical products, and enable
  manufacture, is different from basic discovery
  science.
• Need to fill gap in applied science-- to increase
  productivity and efficiency --to reduce cost of
  development process.

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Stakeholder Input Overwhelming Support

• Overwhelming concurrence with
• recognition of science infrastructure problem
• CP Initiative focus on research and
  collaboration,
• We heard this from drug industry, patient
  groups, device companies and groups, biotech
  companies, others

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This is what we heard !Demand Exceeds Supply

• Docket Demand for FDA Action Exceeds FDA
  Capacity Far more proposed than FDA can
  undertake.
• Principles for setting priorities for FDA
  actions are on Science Board agenda.

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Overriding Concerns

• Clinical Trials
• Biomarkers and Endpoints

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What is the problem

• Phase III trials are failing at a rate that is
  higher than expected - root causes ?
• What is the typical planning process for drug
  development / phase 3 trials
• What can we change what new tools can we use,
  and what can we do better in the future to
  improve Phase III success and efficiency of drug
  development

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Possible solutions / strategies Can
statisticians help ?

• Are new study designs needed
• Impetus for Adaptive designs, two stage designs,
  enriched target population designs
• Are we planning correctly - Rethink how the study
  planning process occurs
• Its the dose
• Its the scenario needing better planning - or
  analysis methods
• Bring consensus / closure to most pressing
  statistical issues at the core of decision making
• Get involved in new emerging subject matter areas
  and impact them -genomics, proteonomics,
  nanotechnology
• Broaden the multi-disciplinary roles, in
  industry, academia and regulatory bodies -
  internationally

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Our Proposal for the Critical Path

• Conduct Research , Gain Consensus, and Develop
  Guidance to Remove Obstacles to Efficient Drug
  Development and Enhance Success Rates of Clinical
  Trials
• Improve the Processes and Approaches to
  Quantitative Analysis of Clinical Safety Data
  from Clinical Trials to Enhance Risk Assessment
  and Management Initiatives
• Improve the Statistical Understanding and
  Application of Modern Statistical Approaches to
  Product Testing and Process Control

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Clinical Trial Proposals for the Critical Path

• Missing data due to patient withdrawals and
  dropouts in clinical trials
• Flexible / adaptive clinical trial designs to
  improve the information and success rate of
  trials
• Non-inferiority active control studies when
  placebos can't be used - getting to consensus on
  appropriate methods for margin setting, data
  analysis and interpretation for various data rich
  and data poor scenarios
• Development of consensus on the statistical
  handling of multiple endpoints in clinical
  trials.
• Clinical trial modeling and simulation as a tool
  for better design and interpretation of clinical
  trials
• Application of Bayesian Methods to Enhance the
  Success Rate of Clinical Trials

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Prioritize Efforts - Three separate yet related
approaches

• Guidance Development
• Multiple endpoints
• Non-inferiority
• Topics of high interest
• Adaptive / Flexible designs
• Modeling / simulation / planning/Phase 2a
• Other Critical Path needs safety , product
  quality

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Safety and Quantitative Risk AssessmentClinical
Trials - Pre-Marketing

• Methods of application
• Planning, data collection, statistical analysis
  plan
• Process
• Newly formed statistical safety team for more
  concentrated and focused advice
• Earlier planning, modeling and simulation

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FDA Risk Management GuidancesLife cycle of a drug

• Premarketing Risk Assessment (Premarketing
  Guidance)
• Development and Use of Risk Minimization Action
  Plans (RiskMAP Guidance)
• Good Pharmacovigilance Practices and
  Pharmacoepidemiology Assessment
  (Pharmacovigilance Guidance)

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Enhancing Product Quality

• Modern in process testing raises the possibility
  that alternatives to product quality should be
  considered
• There have also been advances in Process
  Analytical Technology (PAT) which depends on in
  process assessment of product quality all along
  the drug manufacturing process

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The Non -Inferiority ProblemCurrent guidance is
inadequate and the issues are poorly understood -
must be fixed

• Term introduced in ICH E9 Statistical Principles
  for Clinical Trials
• Some issues described in ICH E10 Choice of
  Control Groups
• A study design that provides an indirect measure
  of evidence of efficacy / safety

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What are the various objectives of the
non-inferiority design

• To prove efficacy of test treatment by indirect
  inference from the active control treatment
• To establish a similarity of effect to a known
  very effective therapy - e.g. anti-infectives
• To infer that the test treatment would have been
  superior to an imputed placebo ie. had a
  placebo group been included for comparison in the
  current trial. - a new and controversial area -
  choice of margin is the key
• To preserve a specified effect of the AC

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How is the margin ? chosen based upon prior
study data

• For a large treatment effect, it is easier - a
  clinical decision of how similar a response rate
  is needed to justify efficacy of a test treatment
  - e.g. anti-infectives is an example.
• For modest and variable effects, it is more
  difficult and some approaches suggest margin
  selection based upon several objectives.

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Complexities in choosing the margin (how much of
the control treatment effect to give up)

• Margins can be chosen depending upon which of
  these questions is addressed
• how much of the treatment effect of the
  comparator can be preserved in order to
  indirectly conclude the test treatment is
  effective - a clinical decision for very large
  effects a statistical problem for small and
  modest effects
• how much of a treatment effect would one require
  for the test treatment to be superior to placebo,
  had a placebo been used in the current active
  control study - a lesser standard than the above

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How convincing is the prior evidence of a
treatment effect ?

• Do clinical trials of the comparator treatment
  consistently and reliably demonstrate a treatment
  effect - when they do not, what is the reason ?
• Study is too small to detect the effect - under
  powered for a modest effect size
• The treatment effect is variable, and the
  estimate of the magnitude will vary from study to
  study, sometimes with NO effect in a given study
  - a BIG problem for active controlled studies
  (Sensitivity to drug effect)

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Importance of the assumption of constancy of the
active control treatment effect derived from
historical studies

• It is relevant to the design and sample size of
  the current study, to the choice of the margin,
  to the amount of bias built into the comparisons,
  to the amount of effect size one can preserve
  (both of these are likely confounded), and to the
  statistical uncertainty of the conclusion.
• Before one can decide on how much of the effect
  to preserve, one should estimate an effect size
  for which there is evidence of a consistent
  demonstration that effect size exists.

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Four approaches to the problem

• The simple case specify a delta - not estimated
• Indirect confidence interval comparisons (ICIC)
  (CBER/FDA type method, etc.)
• - thrombolytic agents in the treatment of acute
  MI
• Virtual method (Hasselblad Kong, Fisher, etc.)
  
• - Clopidogrel, aspirin, placebo
• Bayesian approach (Gould, Simon, etc.)
• - treatment of unstable angina and non-Q wave MI

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Current Guidance on Multiple Endpoints is
inadequateMultiple primary endpointsMultiple
secondary endpointsComposite endpointsMultiple
compositesHierarchiesPatient reported
outcomesDecision Criteria for success

• A collaborative effort PhRMA 2004 meeting on
  co-primary endpoints, manuscript

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Emerging Interest in Adaptive / Flexible Trial
Designs

• Adaptive designs
• Enrichment / pharmacogenomics
• Sample size re-estimation
• Design modification

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New study designsWhy a need for adaptive /
flexible designs ?

• Enriching trials with patients having genomic
  profiles likely to respond or less likely to
  experience toxicity
• Goal of an adaptive / flexible design
• Mid study changes that prospectively plan for
  modifications that preserve Type 1 errors and
  maximize chances for success

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Information adaptive designs / flexible
designsControversialStatististical
Methodology is AvailableWhy and where to use
them?
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Why the need for adaptation? Design
specifications often entail at least partial
knowledge of the values of many planning (primary
or nuisance) parameters that are unknown or at
best might be guessed crudely Sample size
planning entails educated guess of effect
size. Selection of a composite endpoint requires
educated guess of where the potential effects
lie and what noises may be. Others..

Hung
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Addressing a process issue Scenario
PlanningA Tool to Increase the Success Rate of
Phase III trials and to Enhance Drug Development
PlanningIncorporates Several linked linked
study phases - continuumMultiple
endpointsMissing dataUse of all information in
the process Safety PlanningModeling and
simulationFlexible designs / development
sequence / international
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What is Scenario Planning

• Modern approach to protocol planning and choice
  of clinical study designs
• Utilizing models for disease progression and
  endpoint selection
• Utilizing simulation strategies for what if
  scenarios
• Assumes input from other studies and planning
  efforts - planned sequences of studies may matter
• An aid for prospectively planning integrated
  analyses

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Disease Progression Modeling

• Endpoint selection and evaluation
• Trial Duration determination
• Frequency and number of subject measurements
• Tradeoffs between clinical endpoints and patient
  reported outcomes
• Evaluate impact of missing data, informative
  treatment related censoring
• Evaluate multiplicity implications

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What would be observed if subjects had stayed in
trial ? Impute values from subects staying in
longer
Test
Control
Which path do you choose ?
Baseline
1
2
3
4
5
Higher is bad
Visit
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Disease Progression Models and Clinical Outcomes

• What model captures the functional relationship
  of the disease progression and the clinical
  outcome(s) to be used to measure treatment effect
• Can one function capture each of the clinical
  outcomes adequately
• If not are several disease progression models
  used to express response

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Modern Protocol /Development PlanningSensitivity
/ Scenario planning

• Different statistical tools and strategies
• Challenge and explore assumptions
• More multidisciplinary involvement
• It is more than sample size planning
• Structured planning meetings that are different
  that current formal Q As not broad enough
• Links between phase planning and modeling efforts
  currently too limited and stove piped

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Concluding remarksMeeting the Challenges of the
Critical Path will require collaboration and
resource allocation

• Multidisciplinary / collaborative planning and
  evaluation is needed now more than ever because
  issues becoming more complex - guidances cant
  solve this - resources, exposure, experience,
  training will
• Efforts to move available appropriate statistical
  methods and concepts , possibly more complex,
  into the main stream by emphasis on understanding
  by the audience appropriate to the application
• Guidances dont help here - need resources that
  can understand and communicate
• Efforts to maximize contributions of industry,
  academic and regulatory statisticians

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Concluding remark -Priority
setting -

• Choosing the most pressing needs and the chances
  for success - currently being updated
• This is a national effort - not just FDAs
  initiative - it will take a major coordinated
  effort to make progress