global pharmacometrics enhanced quantitative decision making - reducing the likelihood of incorrect...
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GlobalPharmacometrics
Enhanced Quantitative Decision Making
- Reducing the likelihood of incorrect decisions
Mike K. Smith, Jonathan French, (Pfizer)
Ken Kowalski, (A2PG)
Wayne Ewy (formerly Pfizer, retired).
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Six Components ofModel-Based Drug Development*
Model-Based Drug Development
PK/PD & Disease Models
Competitor Info. & Meta-Analysis
Design & Trial Execution Models
Data Analysis Model
Decision Criteria
Trial Performance Metrics
* Lalonde et al, Clin Pharm & Ther, 2007; 82: pp21-32
Quantitative Decision Criteria
• “I’ll know it when I see it…”
• “Evidence of an effect…”
• “Reasonable efficacy and safety tradeoffs”
• WRONG!!!
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)
• Not just P(… | Data)– Data– Prior data, model assumptions, parameter
uncertainties– Trial design– Dropouts, imputation methods etc.– Data analytic method
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
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 - 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 TTV, 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
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Operating Characteristics
Correct No Go Incorrect GoP(True No
Go)
Incorrect No Go Correct Go P(True Go)
P(Trial No Go) P(Trial Go) 1.0
Trial No Go Trial Go Total
“Tru
e”
No
Go
“Tru
e”
Go
To
tal
P(correct) P(Go) PTS
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.
Example
0
500
1000
1500
2000
2500
Fre
qu
en
cy
0 1 2 3 4 5Delta-TOTPAR6
360 mg SC-75416 vs 400 mg Ibuprofen360 mg SC-75416 vs 400 mg Ibuprofen
Obs Mean = 3.3
PTS = P( 3) = 67.2
From Kowalski et al: A model-based framework for quantitative decision-making in drug developmentPresentation at ACOP, Tuscon, AZ 2008.
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Trial
Truth
Trial No Go
LCL95 0 or Mean<3
Trial Go
LCL95> 0 and Mean3 Total
k<3 2081
20.81%
1199
11.99%
3280
32.80%
k3 1729
17.29%
4991
49.91%
6720
67.20%
Total 3810
38.10%
6190
61.90%
10,000
100%
P(correct) = 70.72% P(Go) = 61.90% PTS = 67.20%
Example
From Kowalski et al: A model-based framework for quantitative decision-making in drug developmentPresentation at ACOP, Tuscon, AZ 2008.
“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
• 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
• “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)
• 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)
• 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
Bibliography1. 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.
2. Lalonde, R.L., et al. “Model-Based Drug Development”. Clin Pharm Ther 2007;82:21-32.
3. 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.
4. 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