Part of the MaRS BioEntrepreneurship series session: Clinical Trials Strategy Speaker: Miklos Schulz This is available as an audio presentation: http://www.marsdd.com/bioent/feb12 Also view the event blog and summary: http://blog.marsdd.com/2007/02/14/bioentrepreneurship-clinical-trial-strategies-its-never-too-soon/

1. Beyond Traditional
Designs in Early Drug
Development
MaRS Centre Toronto – Feb 2006
Miklos Schulz, PhD.
St. Clare Chung, MMath.

2. Early Drug Development
! Phase Ib – maximum tolerated dose / schedule
! Phase I/II efficacy-toxicity trade-off studies
Later-phase Studies
! Phase II or Proof-of-Concept trials
! A trials to “explore” clinical efficacy with a small
number of targeted subjects : provide earlier
evidence of the potential to show clinical efficacy
! Seamless Phase II / III Designs

3. Design Approaches
! Frequentist (traditional)
! Bayesian

4. Frequentist vs. Bayesian

5. Frequentist vs. Bayesian in Clinical
Trials

6. Objective of Phase I trials

7. Phase I: Traditional Design
The only traditional adaptive dose/treatment allocation design
!
“1-in-3” Design (3+3 design)
!
Treat 3 patients at the starting dose level Di
!
! If 0 patients experience dose-limiting toxicity (DLT),
escalate to dose Di+1
! If 1 or more patients experiences DLT, treat 3 more
patients at dose level Di
! If 1 of 6 experiences DLT, escalate to dose Di+1
! If 2 or more experiences DLT, MTD = Di-1
! Dose escalation stops when ! 1/3 patients have DLT at a
given dose level; MTD is next lower dose level

8. Phase I: Traditional Adaptive Design

9. Phase I: Traditional Design
! Limitations of “1-in-3” Design
! Inflexible; what to do if:
! Number of subjects treated at a dose differ from
algorithm of (3 or 6)
! Outcome (DLT) re-assessed after dose-escalation
decision made
! Sample size is variable
! Confidence in MTD is usually poor

10. Continual Reassessment Method
O’Quigley et al., 1990
!
Reconcile practical constraints and ethical demands of Phase I studies
!
Treat patients at the dose which all currently available evidence indicates to
!
be the best estimate of the MTD
Two features of CRM:
!
! Estimate the MTD after every patient has been dosed and has completed
the follow-up segment
! Allocate next patient to the dose-level suggested to be the MTD
Currently available evidence:
!
! Prior knowledge of MTD
! Beliefs in the initial data

11. Continual Reassessment Method
Bayesian procedure: one parameter model
!
Binary response: toxicity vs. no toxicity
!

12. Continual Reassessment Method
! Method accounts for different number of patients per dose
! Targets a pre-selected DLT rate
! Variants of design:
! Two-parameter CRM (Schulz & Chung, 1995)
! Modified CRM (Goodman et al. 1995)
! Extended CRM [2 stage] (Moller, 1995)
! Restricted CRM (Moller, 1995)
! Tri-CRM (Zhang et al. 2005)

13. CRM – Case Study
! Original CRM (one parameter model) adequate
when dose response curve is typical ‘s-shaped’
! Not efficient when toxicity increases at a slower
rate over the dose-range tested
! Deficiency compensated by 2-parameter model

14. CRM – One vs. Two parameter model

15. CRM – Case Study - Background
Cancer patients treated at combination doses of 2 drugs
!
Objective: determine the most efficacious treatment combination which
!
produces at most, 33% toxicity
8 dose combination levels were tested
!
Patients were on 4 cycles of treatment before outcome was determined
!
Dose-limiting toxicity was any Grade III or IV toxicity in hematological
!
parameters
Patients were allocated to dose levels based on the traditional 1-in-3 approach
!
Re-analysis was performed with the 2-parameter CRM model
!

16. CRM – Case Study

17. CRM – Case Study

18. Efficacy/Toxicity Trade-offs
Thall PF, Cook J (2004)
!
Problems with usual Phase I quot; Phase II paradigm
!
Phase I designs ignore Response, but no patient hopes
!
only for “No Toxicity”
For Biologic Agents Pr(Response) may be non-
!
monotone in dose
If Pr(Toxicity) is low for all doses but Pr(Response)
!
increases with dose, then the superior higher dose will
not be found

19. Efficacy/Toxicity Trade-offs
Thall PF, Cook J (2004)
!
! Phase I/II dose-finding strategy
! Patient outcome = {response, toxicity}
! Investigator defines:
! a lower limit P(Res)
! an upper limit P(Tox)
! three equally desirable(quot;R, quot;T) targets - used to
construct an Efficacy-Toxicity Trade-off Contour
! Dose x is acceptable if:
! Pr{quot;E (x,!) > quot;E* | data } > .10 or
! Pr{quot;T (x,!) < quot;T* | data } > .10
Other upper cutoff limits may be used

20. Efficacy/Toxicity Trade-offs
Thall PF, Cook J (2004)
!
Demo and Simulation results from Thall & Cook program
!
Program may be downloaded from:
!
http://biostatistics.mdanderson.org/SoftwareDownload/
The Trade-Off-Based Algorithm reliably:
!
! Finds Safe Doses having High Efficacy
! Stops if no dose is acceptable

21. Efficacy/Toxicity Trade-offs
Yin G, Li Y and Ji Y (2006)
!
Phase I/II design
!
Curve-free; not dependent on a specific response curve
!
Incorporate bivariate outcomes, toxicity and efficacy
!
Model the data to account for the correlation between toxicity
!
and efficacy
! Dose for the next cohort of patients is determined from
responses of previous cohorts and based on odds ratio criteria
from posterior toxicity and efficacy probabilities

22. Summary

23. Clinical Trial Designs: Bayesian /
Adaptive
Learn faster quot; more efficient trials
!
More efficient drug development
!
More effective treatment of patients in the trial
!
Drop or add doses
!
Early stopping for futility
!

24. Traditional Approaches
! Robust, but inflexible: design parameters cannot be
changed without affecting robustness / interpretation
! Inefficient / time-wasting (e.g., treating patients in
ineffective studies arms)
! May focus only on single patient populations -
therapeutic strategies
! Restricts statistical inferences to information in the
current trial