Adaptive model-based dose selection methods Francois Vandenhende, - - PowerPoint PPT Presentation

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Adaptive model-based dose selection methods Francois Vandenhende, - - PowerPoint PPT Presentation

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Adaptive model-based dose selection methods Francois Vandenhende, Ph.D. CEO, Clinbay francois@clinbay.com NCS 2008, Leuven, 24 Sept. 2008 Outline Adaptive modelling strategy Background and example Principles and components Analysis of a

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Adaptive model-based dose selection methods

Francois Vandenhende, Ph.D.

CEO, Clinbay francois@clinbay.com NCS 2008, Leuven, 24 Sept. 2008

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Outline

Adaptive modelling strategy Background and example Principles and components Analysis of a case study Conclusions

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Preclinical Phase I Phase II Phase III Launch

First in man POM Efficacy Dose ranging Registration trials Tox/Biology

  • Evidence of pharmacological activity
  • Early go/no go
  • Optimized dose selection for phase II

Proof of mechanism

  • Challenges:

– Availability of a validated biomarker – Cost effectiveness – Predictivity

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Example: Receptor Occupancy PET

Baseline scan 50% Occupancy 75% Occupancy

Tracer

Drug

Blocking scan 5 mg drug Blocking scan 20mg drug

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Dose-Occupancy Relationship

  • J. Meyer et al., [C-11]DASB uptake

before and after SSRI, Toronto.

No occupancy : Quick kill Dose selection: Quantiles of dose-response

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Adaptive Modelling Strategy

  • Parametric dose-response model

– E.g., Emax model or 4PL

  • Bayesian inference

– Uses available prior information p(θ) from preclinical assays or competitors. – Posterior update possible after every subject ) , (  dose f RO  ) | ( ) ( ) | (    RO L p RO p 

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Adaptive Modelling Strategy (II)

  • Adaptive dose selection during study:

– Select next dose dz that optimizes a property

  • f

– E.g., D-optimal design: min |Var(θ)|

  • Decision to stop POM trial

– Stop enrolment when

  • Precision around f(dose, θ) is sufficient, or
  • For futility, when, eg: Pr[f(dose, θ)>50%]<5%.

) , | (

dz hist RO

RO p 

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Adaptive Modelling Strategy (III)

  • Predicting relevant doses for phase II:

– Based on posterior predictive distribution: – E.g.:

   d RO p RO RO p RO RO p

POM POM POM

) | ( ) , | ( ) | (

patient patient

% 90 ) | % 70 (

patient

 

POM

RO RO p % 50 ) | % 70 (

patient

 

POM

RO RO p % 10 ) | % 70 (

patient

 

POM

RO RO p

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DASB Case Study

www.decimaker.com Design and analysis settings: Emax model (flat priors) Next dose: D-optimal Stop study if CV(ED50)<30% or Pr[Emax<50%]>95%.  Phase II doses based on PP(RO>70%)

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Emax model and Priors

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Bayesian Emax model fit

Param mean sd 2.5% median 97.5% Emax 85.82 5.191 76.54 85.47 97.06 ED50 2.199 0.539 1.39 2.154 3.398 tau 0.022 0.011 0.006 0.021 0.046

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Next dose and stopping rules

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Dose selection for Phase II

Clinbay 2007 - Confidential

Next Patient RO>70%?

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Conclusions

  • Adaptive modelling strategy permits quantitative,

data-driven decisions:

– Within study:

  • Dose selection
  • Trial termination

– Across drug development:

  • Probability of failure (success) drives go/no go decisions
  • Summary of all historical data
  • Prediction of future patient responses
  • Technical challenges when using quantitative

methods:

– More work upfront on definition of decision tree. – Trial simulations to validate strategy. – Software availability as a key enabler.

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Any Question?

Thank you!

www.clinbay.com