Beyond Traditional
Designs in Early Drug
Development
       MaRS Centre Toronto – Feb 2006
       Miklos Schulz, PhD.
    ...
Early Drug Development
! Phase Ib – maximum tolerated dose / schedule
! Phase I/II efficacy-toxicity trade-off studies

La...
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...
Phase I: Traditional Adaptive Design
Phase I: Traditional Design
! Limitations of “1-in-3” Design
   ! Inflexible; what to do if:
      ! Number of subjects tr...
Continual Reassessment Method
    O’Quigley et al., 1990
!
    Reconcile practical constraints and ethical demands of Phas...
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 rat...
CRM – Case Study
! Original CRM (one parameter model) adequate
  when dose response curve is typical ‘s-shaped’
! Not effi...
CRM – One vs. Two parameter model
CRM – Case Study - Background
    Cancer patients treated at combination doses of 2 drugs
!
    Objective: determine the m...
CRM – Case Study
CRM – Case Study
Efficacy/Toxicity Trade-offs

     Thall PF, Cook J (2004)
!
         Problems with usual Phase I quot; Phase II paradigm
...
Efficacy/Toxicity Trade-offs
     Thall PF, Cook J (2004)
!
     ! Phase I/II dose-finding strategy
         ! Patient out...
Efficacy/Toxicity Trade-offs
    Thall PF, Cook J (2004)
!
        Demo and Simulation results from Thall & Cook program
 ...
Efficacy/Toxicity Trade-offs
    Yin G, Li Y and Ji Y (2006)
!
       Phase I/II design
     !
       Curve-free; not depe...
Summary
Clinical Trial Designs: Bayesian /
    Adaptive

     Learn faster quot; more efficient trials
!
     More efficient drug ...
Traditional Approaches
! Robust, but inflexible: design parameters cannot be
  changed without affecting robustness / inte...
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Beyond Traditional Designs in Early Drug Development

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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/

Published in: Business, Health & Medicine
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Beyond Traditional Designs in Early Drug Development

  1. 1. Beyond Traditional Designs in Early Drug Development MaRS Centre Toronto – Feb 2006 Miklos Schulz, PhD. St. Clare Chung, MMath.
  2. 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. 3. Design Approaches ! Frequentist (traditional) ! Bayesian
  4. 4. Frequentist vs. Bayesian
  5. 5. Frequentist vs. Bayesian in Clinical Trials
  6. 6. Objective of Phase I trials
  7. 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. 8. Phase I: Traditional Adaptive Design
  9. 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. 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. 11. Continual Reassessment Method Bayesian procedure: one parameter model ! Binary response: toxicity vs. no toxicity !
  12. 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. 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. 14. CRM – One vs. Two parameter model
  15. 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. 16. CRM – Case Study
  17. 17. CRM – Case Study
  18. 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. 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. 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. 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. 22. Summary
  23. 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. 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

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