November 18, 2014, 11:00 - 12:00 EST / 16:00 - 17:00 GMT

Optimise Clinical Development with Adaptive Trials

Introductions

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Reinhard Eisebitt, Executive Vice President, ICON Adaptive Trials Innovation Centre

Reinhard oversees a team of US and EU based adaptive design experts, who work closely with clients to design and conduct adaptive trials, and with industry partners to develop new and innovative methods in drug development. Prior to joining ICON, he was managing director of ClinResearch GmbH, a full service CRO specialising in the planning, execution and reporting of adaptive clinical trials. He is also co- founder of ADDPLAN GmbH, the first commercial software package for the design, simulation, and analysis of adaptive trials.

Introductions

Andy Grieve, PhD, Senior Vice President, Clinical Research Methodology, ICON Adaptive Trials Innovation Centre

Andy Grieve is a member of the ICON Adaptive Trials Innovation Center. He is a former professor of medical statistics at King’s College London, and brings over 30 years of pharmaceutical industry experience. He was also executive director and worldwide head of the Statistica lResearch and Consultancy Center at Pfizer, and held senior positions at ICI pharmaceuticals and at CIBA-GEIGY Pharmaceuticals. Andy is a fellow and chartered statistician and former president of the Royal Statistical Society. He is a fellow of the American Statistical Association and a member of Statisticians in the Pharmaceutical Industry, of which he is a past chairman and founder-member.

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Agenda

• What ICON does to improve adaptive learning

• Combine exploratory & confirmatory trials

• Software requirements

• Case studies

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A recording of this Webinar is also available

To view click on the link below http://www.iconplc.com/webinar/14/OptimizeClinicalDevelopmentwithAdaptiveTrials.wmv

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Introduction

Reinhard Eisebitt, Executive Vice President,

ICON Adaptive Trials Innovation Centre

Regulatory Guidance

“…comprehensive and prospective, written standard operating procedures (SOPs) that define who will implement the interim analysis and adaptation plan…”

(FDA Guidance For Industry, line 1685)

“…Many CROs do not have long histories of carrying out these responsibilities. Study sponsors should have assurance that the personnel performing these roles have appropriate expertise…”

(FDA Guidance For Industry, line 1725)

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Methodology and Software

• Adaptive studies require substantial investment in advanced methods, technology, and infrastructure for trial execution

• Adaptive study design and analysis is new and complicated. It therefore requires to use a validated software

• The development of adaptive methods goes hand in hand with statistical software development

• ICON invests in both a team of leading methodologists and enabling technology

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The first ever adaptive design software

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The ADDPLAN® Software

• ADDPLAN was first released in 2002 for the purpose of planning, simulating, and conducting adaptive trials.

• ADDPLAN is professionally validated and 21 CFR Part 11 compliant. • ADDPLAN is available in the following modules:

- BASE (adaptive one- and two-sample studies)- MC (multiple comparisons in multi-arm trials)- PE (population enrichment designs)- DF (dose-finding designs)

www.addplan.com

EMA Certificate for ADDPLAN DF Methodology

First ever qualification opinion on a statistical methodology The EMA has releasedqualification opinion on a statistical and modelling methodologyMCP-Mod facilitates the selection of the best dose of a new medicine during the clinical development program

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The ADDPLAN DF Consortium

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• Established in 2013 to further the development of ADDPLAN DF and its applications

• Current members

ICON Innovation Centre

The ICON Innovation Centre comprises leading experts in adaptive trial design and execution. Through this global team ICON’s customers have access to first class expertise to ensure that adaptive trials are designed and implemented professionally and successfully.

Innovation Centre experts can help clients with the• Design, simulation, and implementation of adaptive trials across all

phases of drug development• Development of innovative trial methodologies and software• Customised training in adaptive trials• Advice and guidance on the logistical and operational requirements

for successful adaptive trial execution

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Optimise Clinical Development with Adaptive Design

Andy Grieve, PhD, Senior Vice President, Clinical Research Methodology,

ICON Adaptive Trials Innovation Centre

Why are Adaptive Clinical Trials Essential for Development of New Products?

• Pharmaceutical industry facing a major pipeline challenge

- Fewer approvals, escalating development costs, tougher regulatory environment, re-imbursement hurdles, expiring patents for existing blockbusters

• Failure rate in Phase III estimated at 40-50%

• Traditional development paradigm is not sustainable

Setting the Scene

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Therapeutic Area

Phase II Phase III

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Phase II Phase III2004

201413

Factors Influencing Failure in Phase III Trials

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Understanding of Exposure/Response

Incomplete/inadequate characterizationExposure may be dose, AUC

Assumptions concerning Treatment Effect

Observed effect sizes smaller than assumedImplications for study size

Experience with Primary Endpoint

Lack of experience with endpointPutative improvements without understanding

Use of Interim Analyses No use of interims despite great uncertainty

Team Issues High turnoverLack of familiarity

All relevant to adaptive modelling and design in Phase II Dose Selection

14Setting the Scene

Terminology

Flexible

Multi-Stage

Response-Driven

Dynamic

Sequential

Self-Designing

Bayesian

Ada

ptiv

e D

esig

ns

15Basic Principles of Adaptive Designs

An Adaptive Trial uses accumulating data to decide how to modify aspects of the study without undermining the validity and integrity of the trial. (PhRMA)

Adaptive Design: Definition

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Validity providing correct statistical 

inference: adjusted p‐values, estimates, 

confidence intervals providing convincing results to a 

broader scientific community minimizing statistical bias

Integrity Pre‐planning based on intended 

adaptations

maintaining confidentiality of data

assuring consistency between different stages of the study 

minimizing operational bias

Basic Principles of Adaptive Designs

• An adaptive design may have one or more rules:

Allocation Rule: how subjects will be allocated to available arms

Sampling Rule: how many subjects will be sampled at next stage

Stopping Rule: when to stop the trial (for efficacy, harm, futility)

Decision Rule: the terminal decision rule and interim decisions pertaining to design change not covered by the previous three rules

An adaptive design requires the trial to be conducted in several stages with access to the accumulated data

At any stage the subsequent stages can be redesigned taking account of all available data

• Number of Subjects• Study Duration• Endpoint Selection• Treatment Duration• Patient Population• Number of Treatments• Number of Interim Analyses• Hypotheses

General Structure – Dragalin (DIJ, 2006)

17Basic Principles of Adaptive Designs

• Aspects of the Study Modifiable:

The Role Of Adaptive Trials in the Drug Development Process

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NewProduct

Standard Development Process

Phase 1                   Phase 2                                 Phase 3

Adaptive Development Process

Option to:• Verify trial assumptions• Explore additional doses• Stop for futility early

Option to:• Select best dose• Submit application early• Stop for futility

Long periods of information “blackout”No opportunity to: 

‐ verify trial assumptions‐ adjust dosing‐make minor adjustments to trial design‐ stop for futility

Basic Principles of Adaptive Designs

Types of Adaptive Design: Learn

• Single ascending dose escalation designs • Up-and-Down and CRM to find MTD• Establish Proof-of-Mechanism or Proof-of-Target Modulation

First-in Human

• Two-stage adaptive approach in patients• 1st stage – identify MTD• 2nd stage – select dose and exposure levels (necessary cond.)

MAD and PoC

• Start with the highest feasible tolerated dose and placebo• If a pre-specified futility condition is satisfied => stop • Otherwise, open enrollment to lower doses

PoC and ADRS

• SAD or MAD combined with Biomarker-based Efficacy• To identify the Optimal Safe Dose

Seamless Phase I/II Design

• Finding a target dose (MED, EDp)• Response Adaptive Allocation• Covariate Adjusted Response Adaptive Allocation

Adaptive Dose Ranging Design

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CRM: Continual Reassessment Method; MTD: Maximum Tolerated Dose; MAD: Multiple Ascending Dose; SAD: Single Ascending Dose; MED: Minimum Effective Dose; EDp: Dose achieving 100p% of maximum effect

Basic Principles of Adaptive Designs

Types of Adaptive Design: Confirm

• Sample size adjustment based on blinded or unblinded data:• Using nuisance parameter estimate• Using treatment effect estimate

Sample Size Reassessment

• Early stopping for efficacy, futility, harm or safety• Adjusting the number and/or timing of interim analyses• Increasing the maximum sample size

Adaptive Group Sequential Design

• Design combining the objectives of Phase II dose ranging study and confirmatory Phase III trial in a single protocol

• Dose selection at the interim analysis

Seamless Phase II/III Design

• Placebo run-in; Active control run-in; Dose titration• Adaptively enrich the population at the interim analysis• Enrich based on biomarker or clinical endpoint response

Population Enrichment Design

• Marker by Treatment Design• Targeted Design• Marker x TRT Design & Response adaptive allocation by strata

Drugs with Companion Diagnostics

20Basic Principles of Adaptive Designs

Phase 2b Dose Selection Design - OsteoarthritisCirca 1993

• More Efficient• wider range of doses, smaller numbers of patients per group• followed by one large parallel group study focusing on the doses

showing promise in exploratory study.

2.5 mgs 10 mgsPlacebo 80mgs 120 mgs 160 mgsDose

40 mgs

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m B

asel

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21Designing Dose-Response/Selection Studies

Guojun Yuan, Cubist & Naitee Ting, Boehringer-Ingelheim

● First Dose Ranging Clinical Trial Design – More Doses? Or a Wider Range? (Chapter in Clinical Trial Biostatistics and Biopharmaceutical Applications, Edited by Walter R Young and Ding-Geng Chen - Dec 2014)

• Conclusions from simulation study:• “we suggest a wider dose range need to be considered first, and then

determine how many doses will be needed to cover this wider dose range.”

• “from a practical point of view, a dose ranging design including four to five test doses (in addition to placebo) which cover a wide dose range may be very useful in designing the first dose ranging clinical trial”

22 22Designing Dose-Response/Selection Studies

Phase II Studies

• Main Goals of Phase II Studies

1. Proof-of-concept (POC) – Does the Drug Work?• In a dose-response setting is there evidence for

dose response2. Dose Selection – Which dose to take into Phase III?

• What is the MED, ED90 or ED50?

MCP – Multiple Comparisons Procedure used for 1.Mod – Modelling of the dose response used for 2.

23 23MCP-Mod - ADDPLAN DF

• Multiple comparison procedures- Allow strong control of FWER

(Family Wise Error Rate)- Easy to implement and interpret- Prior knowledge of dose

response relationship not required: less assumptions

• Dose-Response Modeling- Any dose in the observed range

can be minimum effective dose (MED)

- Provides Confidence intervals on estimated doses

- Allows Better understanding of dose-response relationship for future studies/simulations

Issues:

• Multiple comparison procedures:

- How to improve the statistical power?

• Dose-Response Modeling:- Poor prior knowledge of dose-

response relationship may have a big impact on dose selection

MCP-Mod - Aligned with regulatory requirements

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ICH-E4: Find the smallest dose with a discernible useful effect (MED)

24MCP-Mod - ADDPLAN DF

ADDPLAN DF uses MCP Mod

• Uncertainty about the dose-response structure is taken into account via:

- MCP-Mod approach- Bayesian modeling- Adaptive designed studies

• MCP-Mod is a key part enabling improvement of the 2 main goals:

- Efficiently prove existence of dose response effect- Provide Dose Response-models and improve Dose selection for

Phase III • Novartis: MCP Mod in 30+ studies (standard for Phase IIb)• Statistical methodology is published in scientific magazines• ICON provides software, practical and methodological

knowledge for applying this approach in dose finding studies

25 25MCP-Mod - ADDPLAN DF

MCP-Mod Process

Optimal estimation of a Set of Candidate Models

Establish dose response signal (Proof of Concept)

Type 1 error control (Family Wise Error Rate)

Select single model using max t, AIC, . . .or use model averaging

Estimate Dose Response and select target dose (Minimum Effective Dose, Effective Dose x… )

2626MCP-Mod - ADDPLAN DF

Impact of key parameters on trial success

Delta: the treatment effect – measures the difference between drug group and placebo groupSigma: the standard deviation - measures the dispersion of patient responses around the mean

Power (90%): probability of success

Error (5%): probability of success although no effect (aka type-1 error or false positive)Sample size needed, depends on all the above

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SS

SS SS27The Need for Sample Size re-Estimation

observed

design

Impact of Incorrect Assumptions: treatment effect and standard deviation

3.0

2.0

1.0

0.03.02.01.00.0

+ Primary Endpoint met - Primary Endpoint not met

Good news

observed

design

Data from 39 studies (phase 2 and 3) performed in a 2 year period

correct assumption

2828The Need for Sample Size re-Estimation

Uncertainties and Adaptive Insurance Solutions

• Uncertainty about treatment effect or variability of dataEarly stopping for futilitySample size re-estimation

• Uncertainty about dose arm to take forwardDose response adaptiveDose selection (seamless 2/3)

• Uncertainty about (sub)populationPopulation enrichment

• Drug simply doesn’t workEarly stopping for futility

29The Need for Sample Size re-Estimation

Seamless Phase II/III Designs

• Seamless design- A clinical trial design which combines into a single trial objectives

which are traditionally addressed in separate trials (operationally seamless)

• Adaptive Seamless design - A seamless trial in which the final analysis will use data from

patients enrolled before and after the adaptation(inferentially seamless)

• Primary objective – combine “dose selection” and “confirmation” into a single trial

• Key Benefits: Efficiency; faster and more informed decision-making

• Key Challenges: Effective and Efficient Implementation

3030Seamless Phase II/III Designs

Efficiency of Adaptive Seamless Phase II/III Designs

Development Timeline

Inferentially Seamless Phase II/III trials

Dose B

Dose A

Placebo

Dose B

PlaceboPhase II

Operationally Seamless Phase II/III trial

Dose B

Dose A

Placebo

Dose B

PlaceboPhase II

Confirmatory AnalysisConfirmatory Analysis

Confirmatory Analysis

Interim analysis: Trigger for phase III

Dose C

Confirmatory Analysis

Dose C

Separate Phase II and phase III trials

Dose B

Dose A

Placebo

Dose B

Placebo

Phase II End of Phase III

Dose C

Confirmatory AnalysisI.

II.

III.

3131Seamless Phase II/III Designs

Phase II/III Seamless Design – checklist for feasibility

• The hypotheses are pre-defined and will not change• There is positive data from proof-of-concept studies• The remaining uncertainty primarily concerns dose• The primary endpoint for confirmation is pre-specified and will be

measured on all patients• Patient population will stay the same in both phases• The marketing formulation is available• There is sufficient animal data to allow longer drug exposure: Phase

II decision may be based on a biomarker believed to be predictive of the clinical endpoint for confirmation

32Seamless Phase II/III Designs

• Background

- Three doses of a drug with pre-specified effect sizes

- Primary endpoint: Short-term response (0 or 1) (7 days)

- Placebo rate 45% (to be reduced bytreatment)

- One-sided type I error 0.025- Power 80%

• An inferentially seamless adaptive Phase II/III design with stopping after stage 1 only for efficacy using an O’Brien and Fleming boundary. The interim is assumed to be conducted at an information rate of 1/3rd

● An operationally seamless Phase II/III study with the same decision rule, and sample size (in stage 1), as the inferentially seamless design followed by a phase III study powered at 80%

Example: Seamless Phase II/III study in an Orphan Condition (on-going)

3333Seamless Phase II/III Designs

Seamless Phase II/III study in an Orphan Condition

• Inferentially seamless design chosen• Design approved by EMA and FDA• Other case studies have shown similar savings• Orphan status would have been a potential reason for

PMDA to accept the design

34 34Seamless Phase II/III Designs

Sponsors are interested in adaptive designs (AD)

“Why Adaptive Trials?”

35Conclusion

The “Faster, Cheaper, Better” paradigm Shorten trial duration Save patients, save budget Do a better job at dose selection, better

decision making

• Because they anticipate benefits from an AD such as:

• Because all trials have risks

• Uncertainty or variability about : treatment effect; variance of the endpoint; placebo effect; standard of care effect; drop outs; heterogeneous population, dose, treatment duration, high chance of futility

• Most risks lead to lower probability of success, less than 90% power

• Adaptive Trials are used to check trial assumptions through the trial

• Take pre-planned corrective measures to boost back the power of the study

• Stop the trial if it goes in the wrong direction

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