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

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

Design Approaches
! Frequentist (traditional)
! Bayesian

Frequentist vs. Bayesian

Frequentist vs. Bayesian in Clinical
Trials

Objective of Phase I trials

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

Phase I: Traditional Adaptive Design

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

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

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

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)

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

CRM – One vs. Two parameter model

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
!

CRM – Case Study

CRM – Case Study

Efficacy/Toxicity Trade-offs
Thall PF, Cook J (2004)
!
Problems with usual Phase I \" 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

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(\"R, \"T) targets - used to
construct an Efficacy-Toxicity Trade-off Contour
! Dose x is acceptable if:
! Pr{\"E (x,!) > \"E* | data } > .10 or
! Pr{\"T (x,!) < \"T* | data } > .10
Other upper cutoff limits may be used

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

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

Summary

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

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