shaping the future of drug development · base - super superiority mams - binomial multi-stage;...

What's New in East 6.5? MCPMod,

Population Enrichment, & Program-Level Design

Pantelis Vlachos, PhD Pantelis.Vlachos@Cytel.com

Charles Liu, PhD

Charles@Cytel.com

Shaping the Future of Drug Development

Agenda

•  East: From 6.4 to 6.5 •  New modules, the methods

•  MCPMod •  Population Enrichment •  Program-Level Design

•  Demo •  Further enhancements in East 6.5 •  Q&A

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BASE

MULTIARM

ENDPOINTS

EXACT

ESCALATE

SEQUENTIAL

ADAPT

SURVIVAL SURVADAPT

PREDICT

MAMS

East 6.4

3

BASE

MULTIARM

ENDPOINTS

EXACT

ESCALATE

SEQUENTIAL

ADAPT

SURVIVAL SURVADAPT

PREDICT

MAMS

East 6.5 (three new modules)

MCPMod Pop Enrichment

Program Design 4

Dose-response studies

Establish Proof-of-Concept (PoC) •  change in dose desirable change

in endpoint of interest

Dose finding step •  Select one (or more) “good” dose levels

for confirmatory Phase III once PoC has been established

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Traditional Approach

Proof-of-Concept: Conducted using (multiple) active arms and placebo

Selection of Target Dose:

1.  statistically significant at the proof-of-concept stage 2.  smallest of statistically significant doses but also

clinically relevant

Dose-Response Modeling: 1.  use data from PoC and earlier trials 2.  find a statistical model capturing the effects of

target dose on dose-response

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Traditional Approach

Straight-forward approach However:

•  focuses on narrow dose range where sponsors can have faith that they will establish a clear dose-signal

•  dose-response model should itself play a greater role in choosing the right dose

•  focuses on modeling at the very end of the process

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MCP vs Mod

MCP: •  Dose is a qualitative factor •  Inference about target dose restricted to the

discrete set of doses used in the trial

Mod: Dose Response (parametric functional relationship) •  Dose is quantitative •  Modeling approach validity depends on pre-

specification of appropriate dose-response model

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MCP + Mod = MCPMod

•  Design stage •  Pre-specification of

candidate dose-response models

•  Analysis stage (MCP-step) •  Statistical test for dose-

response signal. Model selection based on significant dose response models

•  Analysis stage (Mod-step) •  Dose response and

target dose estimation based on dose-response modeling

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At the end

MCP-Mod •  Combines multiple comparison and model

based approaches •  Robust to model misspecification •  Flexible dose estimation

Result •  More informative phase 2 designs, more solid

basis for confirmatory study!

Endorsed by EMA and FDA!

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MCPMod

For analysis, Cytel’s Proc MCPMod already available on SAS platform

East 6.5 module will include analysis and design (sample

size / power, individually for each candidate model, and summarized)

Design can be based on Optimal Allocation Available for Normal, Binary and Count endpoints

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Angiosarcoma is an ultraorphan disease Poorly addressed by current treatments

•  Pazopanib a VEGF inhibitor shows modest benefit •  TRC105 can compliment Pazopanib by inhibiting

endoglin, a different angiogenic target Adaptive trial considered optimal due to:

•  Small population (1800 cases/year) •  Limited prior data •  Subgroup interaction. Greater benefit possible

with TRC105 for cutaneous vs visceral tumors

Adaptive population enrichment: Motivating example

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p1: p-value for data from cohort 1 p2: p-value for data from cohort 2

2-Stage Design with SSR and Enrichment

All

Comers

Interi

m

Analysis

Favorable: Continue as planned

Promising: Increase sample size

Unfavorable Continue as planned

Enrich with cutaneous subgroup

TRC105 +Pazopanib

Pazopanib

Stop for futility

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In case of no enrichment, declare significance on Full population if 𝑤↓1 Φ↑−1 (𝑝↓1↑𝐹𝑆 )+ 𝑤↓2 Φ↑−1 (𝑝↓2↑𝐹𝑆 )> 𝑧↓𝛼  𝑤↓1 Φ↑−1 (𝑝↓1↑𝐹 )+ 𝑤↓2 Φ↑−1 (𝑝↓2↑𝐹 )> 𝑧↓𝛼 

In case of enrichment, declare

significance on cutaneous subgroup if 𝑤↓1 Φ↑−1 (𝑝↓1↑𝐹𝑆 )+ 𝑤↓2 Φ↑−1 (𝑝↓2↑𝑆 )> 𝑧↓𝛼  𝑤↓1 Φ↑−1 (𝑝↓1↑𝑆 )+ 𝑤↓2 Φ↑−1 (𝑝↓2↑𝑆 )> 𝑧↓𝛼 

Closed testing with combination of p-values

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Independence of p1 and p2 essential for valid level-α test Use of auxiliary data in the censored observations of

cohort 1 that become completers in cohort 2 is forbidden •  ORR data, lab values, toxicities of patients censored for PFS in

cohort 1 can provide insights about their eventual PFS result in cohort 2

But use of auxiliary data to decide on enrichment

destroys independence of p1 and p2

(Jenkins, Stone and Jennison, Pharmaceutical Statistics, 2011)

Special Challenge of Time-to-Event Endpoint

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Do not choose the interim analysis time point to split data into cohort 1 and cohort 2

Instead, pre-specify allocation of events and sample

size to each cohort before taking the interim analysis •  70 patients and 60 events for cohort 1 •  54 patients and 35 events for cohort 2 •  Interim analysis after 40 events have arrived

That will permit full inspection of all cohort 1 data at interim analysis, including censored data

How to permit use of auxiliary data

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In the end…

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Program Design Design and simulate sequence of trials Two types planned for 6.5:

•  Dose Escalation followed by a cohort expansion study o  Stage1: Dose escalation design (3+3, mTPI, CRM, BLRM) o  Stage2: Single-arm cohort expansion o  Frequentist or Bayesian GNG rules

•  Phase 2 oncology trial followed by Group Sequential o  Stage1: Single-arm binomial, Simon’s two-stage, or 2-arm

survival o  Stage2: A group sequential design

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As Sponsor was preparing Early Development Plan, posed these questions:

•  How can the relationship between early biomarkers and clinical endpoints be leveraged to optimize Phase 1 - 2 plans? (improve quality of information or speed development time) o  How can different designs for Ph1b (PoC and Dose-

Finding with biomarkers) improve design of Ph2b? o  Can we use Ph1b data to optimize dose selection

for a Ph2b study, allowing reduced Ph2b sample size and/or improved chance of picking correct Ph3 dose?

Program Design: Motivation

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Initial Ph1b-Ph2b Development Plan

Go

Ph1b Biomarker

Dose- Finding

Ph1b Biomarker

PoC

Go

Ph2b with Clinical

Endpoint

Go

STOP

STOP

STOP

Ph3

Key Objective Design Ph2b trial to maximize probability of Ph3 dose choice

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Define true underlying scenario(s) for endpoint(s), study design(s), decision rule(s)

Generate many repetitions Summarize results Use to choose and justify trial design - Demonstrates design performance for a

span of potential true scenarios Widely used in Drug Development

Clinical Trial Simulation

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Define a sequence of clinical trial simulations and decision rules and design options for moving from one trial to the next

Aim to optimize the sequence of trials for a particular set of drug program objectives

Drug Program Simulation

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In the end…

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Up next: New module examples

Shaping the Future of Drug Development

MCPMod example

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MCPMod example

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MCPMod example

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MCPMod example

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MCPMod example

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MCPMod example

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MCPMod example

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Population Enrichment example

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Population Enrichment example

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Population Enrichment example

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Population Enrichment example

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Population Enrichment example

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Program Design example

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Program Design example

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Program Design example

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Program Design example

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Program Design example

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Program Design example

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Further Enhancements in East 6.5

BASE - Super Superiority MAMS - Binomial multi-stage; Survival p-value combination

SEQUENTIAL – Equivalence Group Sequential

ADAPT/SURVADAPT - SSR for Non-Inferiority designs

PREDICT – Weibull distribution

ESCALATE - mTPI-2

MEP - Mixed endpoint type; gMCP

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Thank-you!

Pantelis.Vlachos@Cytel.com Charles@Cytel.com