Transcript Topics

Adaptive Clinical Trials In the
Real World
Presentation to MBC
23rd April 2008
[email protected]
The questions
3 categories:
1. Should we use adaptive clinical trials
or not?
2. What’s the impact of using them?
3. How do we use them?
To use or not to use
Adaptive Clinical Trials – to
use or not to use?
1. When is it most appropriate to run an adaptive
clinical trial?
2. When in a drug's development is the most
appropriate time to conduct an adaptive clinical
trial?
3. What indications particularly lend themselves to the
use of adaptive clinical trials?
4. What is the value proposition in the use of Adaptive
Clinical Trials?
5. What's driving the increasing use of adaptive
clinical trials?
6. What are the key benefits for utilizing an adaptive
clinical trial design?
1. When is it most appropriate to run
adaptive clinical trial?
 When you have a lot to learn about the drug
and the disease in your target population
 You do not have the time or money to simply
recruit enough subjects in a simple way to
answer you questions
 And there are outcomes early enough in
treatment to adapt to
2. When in a drug's development is the
most appropriate time to conduct an
adaptive clinical trial?
 Any phase where there is significant
uncertainty over the drug behavior
• But Phase 1 is adaptive anyway (could use better
methods and could look at efficacy as well as toxicity)
• Phase 2 (PoC) and Phase 2 (Dose Finding)
• In Phase 3 there are regulatory issues – classical
(frequentist) but not Bayesian statistics? Need for
safety data?
• Phase 4? A lot of scope – but less budget.
3. What indications particularly lend
themselves to the use of adaptive clinical
trials?
Quick response (<25% of recruitment period)
Range of doses available
Subjects are expensive
Don’t want to learn equally about every
treatment regardless of outcome.
Example Cumulative Subjects and Responses
350
300
# Subjects / Responses




250
Monthly recruitment
200
Total Recruitment
150
2 Month Response
100
50
0
1
2
3
4
5
6
7
8
9
Months into trial
10
11
12
13
14
3b. What indications lend themselves to
Adaptive Clinical Trials?
• Migraine, dental pain, post-operative pain,
neuropathic pain
• Stroke, Alzheimer's, Schizophrenia
• Diabetes, cholesterol lowering
• Cancer
• Orphan indications
3c. What indications don’t lend themselves to
the use of adaptive clinical trials?





Very long time to final response
Very swift recruitment
Population change over duration of trial
Subjects are cheap
Want to learn equally about all treatment arms
4. What is the value proposition of using
Adaptive Clinical Trials?
• Save 25-30% over parallel group with interim for
futility.
• Additional investment ~$500,000 but net saving of
$1.5M (400 subject trial) due to early termination for
futility
• Costs:
•
•
•
•
Extra supplies
Additional design
Response collection
Adaptive algorithm
$200,000
$100,000
$100,000
$100,000
4b. What is the value proposition of using
Adaptive Clinical Trials?
• If successful, better characterization of the
efficacy and toxicity of the drug
• More data on the dose of interest
• Less risk of an inconclusive outcome
• Better model of drug effect and disease
progression – more persuasive
• Faster/smarter overall development through
better targeted trials
5. What's driving the increasing use of
adaptive clinical trials?
 Level of failure in Phase 3
• Need better information before Phase 3
• Need better killing of ineffective compounds before
Phase 3
 Time spent in development
• Can we learn faster by combining phases in a
cleverer trial?
• Phase 1 & 2a
• Phase 2a & 2b
• Phase 2b & 3
6. What are the key benefits of adaptive
clinical trials?
 Better Ethics
• Fewer subjects allocated to ineffective or over-toxic
treatment arms
• Fewer subjects used in studies that fail
 Better Science
• Can try more doses (Phase 1 & 2)
• Can try more doses (Phase 3?)
• Explore other dimensions – combinations,
indications, sub-populations
 Better Business
• Swifter curtailment of failing compound
• Better information -> better decisions at the next
phase
6b.The key benefits
 Better definition of trial goal
 Modeling of trial data:
• Borrow ‘strength’ from neighboring points
• Borrow ‘strength’ from other outcomes (biomarkers,
longitudinal, prior data, etc.)
 Optimization of dose allocation:
• Put fewer subjects on treatment arms that are clearly not
working
• Put more subjects on treatments arms that seem to be
showing the desired target effect.
 Result: better characterization of the dose behavior
Example Adaptive Trial
Tom Parke
©2007 Tessella Support Services plc
[email protected]
(44)(0) 1235-555511
Impact
Impact
1.
2.
How do adaptive clinical trials impact the whole
development program?
What are the principle disadvantages (difficulties and
costs) one faces when utilizing this approach?
1. How do adaptive clinical trials impact
the development program?
 More flexibility in design of whole program
• Trials used to have very predefined task
• Now – what are your questions, and lets design a trial
to answer them as efficiently as possible
 Consider trial in whole development program
• What will follow, what will be in parallel, what is the
right order to answer the questions
 Need to think about the next trial earlier and
longer
 Need to integrate the development team
2. What are the difficulties and costs of
implementing adaptive clinical trials?
 Longer Design Time
•
•
•
•
Need to identify candidate trial
Design less “off-the-shelf”
Design needs interaction with clinical team
Design needs simulation and optimization
 More Integrated Trial Management System
• Quick capture of key responses
• Frequent modification of randomization
 Drug Supply
• Need to be able to deliver more doses
• Need to be able to use central randomization
Implementation
Implementation
1.
2.
3.
4.
5.
6.
7.
8.
Can we capture the response data quickly and reliably
Can we calculate and agree the adaptation quickly
How do we implement the adaptation?
How does one effectively manage the clinical drug
supply chain in an adaptive trial?
How do we get all the stakeholders aligned so the trial is
a success?
From a clinical operations perspective, what are the
challenges in managing a complex trial that could have
from 300 to 800 subjects at multiple sites in different
global locations?
How do we go about deploying adaptive clinical trials?
How do you make it mainstream and industrialize the
process?
Example Adaptive Trial Infrastructure
Randomization
Treatment
Pack Data
Drug Supply
Management
Relative %
Randomization
IVRS
Randomization
List
Model
DMC
Report
Data
Monitoring
Committee
Response
Data
Capture
Response
Data
EDC
Lite
Adaptive Trial Infrastructure
Randomization
Treatment
Pack Data
Drug Supply
Management
Relative %
Randomization
IVRS
Randomiszation
List
Model
DMC
Report
Data
Monitoring
Committee
EDC
Response
Data
EDC
Lite
IVRS
• IVRS need modification to allow adaptation:
• To be able to regularly replace the
randomization list
• after interim to drop or add doses
• after model update to adjust relative proportion of
randomization
• Randomize dynamically based on the currently
available arms and/or proportions of
randomization
• Randomize dynamically using a combined
blocking and proportionate randomization
Partial blocking of placebo
ensures % allocated to
placebo and consistent
allocation to placebo
through time.
Random is now:
Dose1: 8%
Dose2: 12%
Dose3: 20%
Dose4: 35%
Dose5: 17%
Dose6: 8%
Random
Random
Placebo
Random
Random
Random
Random
Required Randomization is:
Placebo: 25%
Partially
Dose1:
6%
blocked
Dose2:
9%
Dose3: 15%
Dose4: 26%
Dose5: 13%
Dose6:
6%
Placebo
Partial Blocking
IVRS treatment allocation
• IVRS if not loading a randomization list needs to be
able to supply a treatment allocation list:
Patient ID,
05041101,
05042301,
05041102,
05040701,
Treatment Arm
2
3
1
1
• From first patient first visit and weekly or fortnightly
thereafter.
Adaptive Trial Infrastructure
Randomization
Treatment
Pack Data
Drug Supply
Management
Relative %
Randomization
IVRS
Randomiszation
List
Model
DMC
Report
Data
Monitoring
Committee
EDC
Response
Data
EDC
Lite
EDC
• EDC needs to be able to extract key response data:
Patient ID,
05041101,
05041101,
05041101,
05042301,
05042301,
05041102,
05040701,
Visit #, resp1,
1,
6.3,
2,
5.2,
3,
5.0,
1,
4.3,
2,
4.6,
1,
5.9,
1,
6.5, 0
resp2
0
0
0
0
1
0
• Within a 1-2 months of first patient first visit and
weekly or fortnightly thereafter.
EDC-Lite
• If the main EDC cannot produce response data
quickly, frequently and reliably
• A parallel EDC system can be used, just collecting
headline response values (possibly just two values
per patient visit)
• Can be made convenient to use
• EDC-Lite data can be replaced by main EDC data
as it becomes available
• Forward EDC-Lite data to EDC to aid data checking
EDC-Lite
Faxes back to centres:
• Subject randomised
• Subject response overdue
Subjects phone in:
• for randomisation
• with response
Subjects receive:
• text reminder
Web
access
Faxes in from centres:
• Subject screened
• Subject eligible
• Subject mobile phone #
Monitoring by
study manager
Adaptive Trial Infrastructure
Randomization
Treatment
Pack Data
Drug Supply
Management
Relative %
Randomization
IVRS
Randomiszation
List
Model
DMC
Report
Data
Monitoring
Committee
EDC
Response
Data
EDC
Lite
Drug Supply
• Initial negotiation with supply as to what is possible
number of different doses, quantity of API, etc.
before design
• Trial design simulations provide estimates of max
number of subjects allocates to any one treatment
arm
• Trial supply simulations allow manufacturing
estimate to be fine tuned, and supply / logistics
trade-offs to be explored
Drug supply during
• Unblinded supply representative included on supply
implications of DMC report.
• supply proportionate to probability of randomization
• total supply requirements implied by predictive
probabilities
Adaptive Trial Infrastructure
Randomization
Treatment
Pack Data
Drug Supply
Management
Relative %
Randomization
IVRS
Randomiszation
List
Model
DMC
Report
Data
Monitoring
Committee
EDC
Response
Data
EDC
Lite
Data Monitoring
• A process change not infrastructure
• DMC include someone competent to check:
• correctness of the data supplied to the model,
• the design’s performance,
• the implementation of the adaptation (is the randomization
adapting?)
• Phase 1 & 2 trial DMCs staffed internally unless
external specialist required.
• Regular automated DMC report with 10 minute
teleconferences to review.
• Small number of big review meetings. Timing
flexible based on review of report
DMC report
•
•
•
•
•
The current recommendation
The data
The model fit
The decisions
The likely outcome (predictive
probability)
Example trial setup
•
•
•
•
Phase 2 dose finding
Designs by Berry Consultants
Data weekly from EDC (in-house, 3rd party)
Possibly supplemented by direct fax of key
endpoint data
• New randomizations sent to IVRS (in-house
or 3rd party)
• DMC report
• Secure file transfer
Example Weekly Update
System
EDC
SMS
IVRS
Reminders
New randomization
list, or randomization
probabilities
Fax
Weekly
complete
response
data
DMC report
Stats
Trial monitoring
website
Main Clinical Operations
challenges
• The high level data is collected and
sent to the adaptive ‘back box’ reliably,
accurately and frequently
• Efficiently supplying in a changing
world
• But, you will be able to monitor your
trial better
Adaptive Trial Infrastructure
Randomization
Treatment
Pack Data
Drug Supply
Management
Relative %
Randomization
IVRS
Randomiszation
List
Model
DMC
Report
Data
Monitoring
Committee
EDC
Response
Data
EDC
Lite
Adaptive Design
• Need good tools (R, S-Plus, WinBUGS, Matlab)
and good statisticians to generate designs, or very
customizable implementations of designs.
• R, S-Plus, WinBUGS, Matlab – are very statistician
friendly and good tools for researching designs –
but slow for running large numbers of simulations.
• Can code them up (C++ / Fortran) once proven.
• Berry Consultants with Tessella will be releasing
customizable implementations of Berry Consultants
designs later this year.
Why simulate designs?
• For some trial designs we can no longer simply prescribe our
desired probability of a false positive (alpha) and or false
negative (beta).
• Simulate with treatment arms no more efficacious than placebo
• Simulate with different arms (and different numbers of arms)
being clinically effective
• But there are other properties of interest too:
• How likely is the best treatment arm to be chosen?
• How likely are we to stop early and will it be correctly or
incorrectly?
• What if we are studying more than one endpoint?
• Or more than one compound?
Why simulate designs? (2)
• We have more to decide:
• Is it worth doing an adaptive design?
• Which of these adaptive designs is better?
• What is the impact of this protocol change (more visits,
more treatment arms, longer follow-up, change of
endpoint)?
• For this design what values should I choose as design
parameters:
•
•
•
•
required confidence of futility/success to stop early
the earliest the trial is allowed to stop early
frequency of looking at data
thresholds for dropping arms, adding arms etc.
Simulation Functionality
GUI
Orchestrate running
Set and validate design
simulations of all versions Display, analyze and
parameters and scenarios
of the design
chart the results
to simulate
over all scenarios
Centralize storage of
designs and run
simulations on
a computing grid
Black Box
Compare designs
with common design
constraints and scenarios
Simulation
manager
Server
Execute Trial
with selected design
and parameters
Trial Execution
Deployment at the company
level
• Decide on type of adaptive trials you can and want
to run.
• Establish cross functional adaptive review team
(clinical, biostats, supply, IT, trial operations) to
review candidate trials and assist teams to Go
Adaptive.
• Development teams should be responsible for all
compounds in an indication, not a single compound
or
• So they see benefit in early determination of futility
• Can learn across a a number of trials
Aligning the stakeholders
• Involve them early
• help them understand what adaptive clinical
trials are and why the company wants to use
them
• in identifying the problems and solving them
• Ensure personal objectives are aligned with
running adaptive clinical trials
• Top down & bottom up
Development teams
•
•
•
•
Don’t design and evaluate design in isolation
Trials aren’t islands,
or steps on a single path
They are decision nodes in a complex tree of
investigation – looking at different endpoints,
populations, indications, combinations.
• The more you can learn each trial and the more
quickly you can learn, the more efficient you
decision making and overall development.
• Can use interim data to start/stop other branches in
the development
Example
Phase 1
A
Combined
Operationally seamless
phase 2a/2b in A & B phase 3 with best of A or B
B
Poc complete start 2nd
ndication development
Sufficient confidence in
efficacy to start
manufacturing API for
rest of development
Second confirmatory
trial
Drop a dose
and chose
dose for 2nd
trial
Start planning 2nd
confirmatory trial
Implementation
1.
2.
3.
4.
5.
6.
7.
8.
Can we capture the response data quickly and reliably
Can we calculate and agree the adaptation quickly
How do we implement the adaptation?
How does one effectively manage the clinical drug
supply chain in an adaptive trial?
How do we get all the stakeholders aligned so the trial is
a success?
From a clinical operations perspective, what are the
challenges in managing a complex trial that could have
from 300 to 800 subjects at multiple sites in different
global locations?
How do we go about deploying adaptive clinical trials?
How do you make it mainstream and industrialize the
process?
You are not on your own
• Tessella and Berry Consultants can
help you do this.
• Berry Consultants:
• Designs & “Black Boxes”
• Tessella:
• Simulation framework for black boxes
• Systems and services to help execute the
trial
Summary
• Despite their differences from normal trials,
Adaptive Clinical Trials can be implemented
• They are becoming increasingly easy to implement
as we
• learn the lessons from the early adopters
• and build tools to support them
• As we integrate them fully into the development
process, the benefits of cost savings and quicker
and better informed decisions will continue to grow
as the development process is redesigned