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Enhanced Quantitative Decision Making - Reducing the likelihood of incorrect decisions. Mike K. Smith, Jonathan French, (Pfizer) Ken Kowalski, (A2PG) Wayne Ewy (formerly Pfizer, retired). Six Components of Model-Based Drug Development*. PK/PD & Disease Models. Trial Performance Metrics.

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enhanced quantitative decision making reducing the likelihood of incorrect decisions

Enhanced Quantitative Decision Making - Reducing the likelihood of incorrect decisions

Mike K. Smith, Jonathan French, (Pfizer)

Ken Kowalski, (A2PG)

Wayne Ewy (formerly Pfizer, retired).

six components of model based drug development
Six Components ofModel-Based Drug Development*

PK/PD & Disease Models

Trial Performance Metrics

Model-Based Drug Development

Competitor Info. & Meta-Analysis

Decision Criteria

Design & Trial Execution Models

Data Analysis Model

* Lalonde et al, Clin Pharm & Ther, 2007; 82: pp21-32

quantitative decision criteria
Quantitative Decision Criteria
  • “I’ll know it when I see it…”
  • “Evidence of an effect…”
  • “Reasonable efficacy and safety tradeoffs”
  • WRONG!!!
quantitative decision criteria1
Quantitative Decision Criteria
  • 2 points improvement over placebo.
    • Better.
    • At least it’s quantitative
  • How sure do you want to be?
    • Mean 2 points?
    • Lower CI 2 points?
    • Mean 2 points and lower CI > 0?
p criteria data
P(Criteria|Data)
  • Not just P(… | Data)
    • Data
    • Prior data, model assumptions, parameter uncertainties
    • Trial design
    • Dropouts, imputation methods etc.
    • Data analytic method
truth vs trial
Truth vs Trial
  • For a given set of model parameters / assumptions there will be a “true” outcome against the decision criteria.
    • What is the chance of achieving 2 points improvement given current information?
    • For a given set of parameters we will know whether we achieve 2 points improvement or not.
  • Then for this same set of parameters, apply design, dropout / imputation models, analytic technique and assess decision criteria.
truth vs trial formally
Truth vs. Trial - Formally
  •  is the true (unknown) treatment effect
    • =f(, , ) is specified for a given set of model assumptions
      •   vector of fixed effects parameters
      •   covariance matrix for between-unit (subject or study) random effects
      •   covariance matrix for within-unit (subject or study) random effects
truth vs trial formally1
Truth vs. Trial - Formally
  • Define quantitative decision rule under truth () and data-analytic results (T), e.g.,
    • Truth: Go if TV, No Go if <TV
    • Data: Go if TTV, No Go if T<TV
      • Note TV denotes the Target Value
      • Note T could be a point estimate or confidence limit on estimate/prediction of 
operating characteristics
Operating Characteristics

Trial Go

Trial No Go

Total

“True” No Go

“True” Go

Total

P(correct)

PTS

P(Go)

example
Example
  • Comparing SC-75416 with ibuprofen in dental pain.
    • Published in Kowalski, K.G, et al. “Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective COX-2 Inhibitor for the Treatment of Acute and Chronic Pain”.
  • Decision criteria based on 3 point difference from ibuprofen in TOTPAR6 endpoint.
example1

PTS = P(  3) = 67.2

Mean = 3.27, SD=0.60

Obs Mean = 3.3

Example

From Kowalski et al: A model-based framework for quantitative decision-making in drug development

Presentation at ACOP, Tuscon, AZ 2008.

example2
Example

P(correct) = 70.72%

P(Go) = 61.90%

PTS = 67.20%

17

From Kowalski et al: A model-based framework for quantitative decision-making in drug development

Presentation at ACOP, Tuscon, AZ 2008.

nominal values for ocs
“Nominal” values for OCs
  • P(Correct) can be fixed at >=80%
  • PTS for initiating a new trial depends on quadrant, portfolio, stage of development.
    • Perhaps minimal “dignity level” for starting a trial.
  • Fixing these two implies P(False GO) and P(False NO GO) must float, depend on P(Correct) and PTS.
    • Driven by decision criteria.
  • E.g. For P(Correct) = 80%, P(Incorrect) = 20%, spent across P(False GO), P(False NO GO).
iterate optimise
Iterate / Optimise

If the operating characteristics “don’t look good”…

Change the data analytic model

Change the design constraints (↑ n /group)

Change the data-analytic decision criteria for the trial.

If we fix one or more of the above (e.g. n /group) then there is limited other things that can improve OCs.

Change the data analytic model, change data-analytic decision criteria for the trial.

the components may change over time
The components may change over time

“Truth” model / prior will be refined over time.

P(“True” Go given current knowledge / model) changes.

Decision criteria may change.

Commercial viability changes. [This may change both our compound target criteria – truth decision rule, as well as the data-analytic decision rule]

Acceptable level of confidence for Trial Go decision changes. [This applies only to data-analytic decision rule]

final remarks 1
Final Remarks (1)
  • Greater collaboration required among kineticists/modelers, statisticians and clinicians
  • Kineticists/modelers:
    • Explicit and transparent about the assumptions and limitations of their PK/PD and disease models
    • Think strategically about how model will be used to influence internal decision-making
    • Avoid excessive use of NONMEM-jargon and write reports to broader audience
    • Calibrate models against data-derived (non-model-based) statistics of interest
final remarks 2
Final Remarks (2)
  • Statisticians:
    • Embrace assumption-rich nonlinear models for decision-making especially in early clinical development
    • Avoid “Phase 3” mentality when designing Phase 2 studies…relying on empirical (assumption-poor) models to make decisions in early clinical development can be costly
  • Clinicians:
    • Quantitatively define clinically relevant effects and commercial targets
    • Explicitly and quantitatively defined decision rules
bibliography
Bibliography
  • Kowalski, K.G., Ewy, W., Hutmacher, M.M., Miller, R., and Krishnaswami, S. “Model-Based Drug Development – A New Paradigm for Efficient Drug Development”. Biopharmaceutical Report 2007;15:2-22.
  • Lalonde, R.L., et al. “Model-Based Drug Development”. Clin Pharm Ther 2007;82:21-32.
  • Kowalski, K.G., Olson, S., Remmers, A.E., and Hutmacher, M.M. “Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective COX-2 Inhibitor for the Treatment of Acute and Chronic Pain”. Clin Pharm Ther, 2008; 83: 857-866.
  • Kowalski, K.G., French, J.L., Smith, M.K., Hutmacher, M.M. “A model-based framework for quantitative decision making in drug development”. Presentation at ACOP, Tuscon, AZ. 2008. http://tucson2008.go-acop.org/pdfs/8-Kowalski_FINAL.pdf
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