shaping the future of drug development · base - super superiority mams - binomial multi-stage;...
TRANSCRIPT
What's New in East 6.5? MCPMod,
Population Enrichment, & Program-Level Design
Pantelis Vlachos, PhD [email protected]
Charles Liu, PhD
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
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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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