exposure-response in the presence of confounding in the presence of confounding ... ss dx c 2016...
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Exposure-response in the presence of confounding
Jonathan L. French, ScD
Metrum Research Group LLC
May 3, 2016
c©2016 Metrum Research Group LLC TICTS, Durham, NC, 2016 May 3, 2016 1 / 24
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Outline
1 Introduction
2 Example of exposure-response with multiple doses
3 Exposure-response with one dose?
4 Concluding thoughts
c©2016 Metrum Research Group LLC TICTS, Durham, NC, 2016 May 3, 2016 2 / 24
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Introduction
Exposure-response is in the mind of the beholder
Ranging across . . .PK-PD modelK-PD modelPD model driven by a summary measure derived from PK modelPD model based on observed concentrationsDose-response
Dose Exposure Response
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Introduction
What summary measures?
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Some commonly used include:
AUCss (Dose/(CL/F))Caveragess (AUCss/τ )CmaxCtrough
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Introduction
Why do we need exposure-response analyses?
What do they give us that we can’t learn from dose-response?
May be closer to mechanism-based models
Concentration of drug→ biomarker (possibly with a delayed effect)
May allow more precise estimation when dose-response modeling isn’tinformed by the design (e.g., only 2-3 dose levels studied).
May allow more seamless interpolation (or extrapolation) to doses orpopulations that weren’t studied
May allow us to understand if a higher dose is necessary for a subset ofpatients
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Example of exposure-response with multiple doses
(Simulated) Example of E-R with multiple doses
Phase 2 dose-ranging study3 treatment groups: placebo, 3, or 5 mg QDn=25 per group
Key decision-making endpoints is a continuous landmark endpoint
Objectives:Estimate E-R relationship in Phase 2 populationPredict E-R in Phase 3 population and impact on dose selection
Dose Exposure Response
Covariates
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Example of exposure-response with multiple doses
Observed data show an apparant E-R relationship . . .
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Dose Exposure Response
Covariates
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Example of exposure-response with multiple doses
Observed data show an apparant E-R relationship . . .
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There also appears to be anassociation with a covariate,x1.
Dose Exposure Response
Covariates
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Example of exposure-response with multiple doses
. . . and two covariates are associated with exposure
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Example of exposure-response with multiple doses
Exposure-response models
4-parameter Emax model for efficacy:
Yi = θ0 +Emaxi · Cavgγ
ss
EC50γ + Cavgγss
+ εi
εi ∼ N(0, σ2)
We will model the maximum effect as afunction of X1:
Emaxi = θ1 + θ2 x1i
EC50 = exp(θ3)
γ = exp(θ4)
EmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmaxEmax
EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50EC50
Exposure
Res
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Gammagamma = 1
gamma = 3
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Example of exposure-response with multiple doses
Fitted model
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Low X1 High X1
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Parameter Estimate 95% CIθ0 9.56 (8.79 , 10.3)θ1 9.91 (8.18 , 18.1)θ2 1.43 (0.60 , 2.99)EC50 29.7 (20.0 , 44.0)γ 1.63 (0.74 , 3.60)σ 1.92
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Example of exposure-response with multiple doses
Extrapolation to phase 3 population
Based on the planned inclusion criteria the Phase 3 population isexpected to have different distributions for X1 and X2.
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Example of exposure-response with multiple doses
Expected exposure and response
Higher response given exposure
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Lower exposure given covariates
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dens
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Example of exposure-response with multiple doses
How do we translate this to dose-response?
If we focus on the expected response, then
E(Y | dose,X ) =
∫E(Y | Cavgss,X ) f (Cavgss | dose,X )dCavgss
and
E(Y | dose,Popn) =∫
E(Y | Cavgss,X ) f (Cavgss | dose,X ) f (X |Popn) dCavgss dX
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Example of exposure-response with multiple doses
Comparison of expected dose-response
10.0
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e PopulationPhase 2
Phase 3
A 4 mg dose gives ∼85% of maximalresponse in Phase 2population.
Equivalent expectedresponse at 6 mg dose inPhase 3 population.
Potential for higherresponse rates in Phase3 population at higherdoses?
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Example of exposure-response with multiple doses
Dose Exposure Response
Covariates
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Exposure-response with one dose?
Can we use E-R modeling when we’ve only studiedone dose?
Sometimes (e.g. in oncology), a single dose is studied in Phase 2(and Phase 3).We may have some variation in exposure . . .. . . but what if exposure is related to measured (or unmeasured)confounding variables?
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Exposure-response with one dose?
An apparent exposure-response relationship. . .
Yang et al. (2012) The Combination ofExposure-Response and Case-ControlAnalyses in Regulatory Decision Making
Suppose we want to . . .
Estimate exposure-responserelationship on the hazard ratiofor trastuzumab relative to control
Estimate the hazard ratio for Q1relative to control
Predict the effects of a higherdose in patients with lowerexposure
. . . in an unbiased (causal) manner.
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Exposure-response with one dose?
What if there are covariates associated with exposureand outcome . . .
Yang et al. (2012) The Combination of Exposure-Response and Case-Control Analyses inRegulatory Decision Making
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Exposure-response with one dose?
. . . and the distribution is different across the exposurerange?
Yang et al. (2012) The Combination of Exposure-Response and Case-Control Analyses inRegulatory Decision Making
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Exposure-response with one dose?
Potential solutions to confounded E-R
Include potential confounders as covariates in the E-R modelTreat the analysis as if it were from an observational study (e.g.,using matching)
Categorize exposure (e.g., quantiles)Calculate propensity scores (for each exposure category relative toplacebo)Use for matched or weighted regression analyses
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Concluding thoughts
Exposure-response modeling is a key ingredient indrug development
”Exposure-response information is at the heart of any determination of the safetyand effectiveness of drugs.” – FDA Guidance for Industry: Exposure-ResponseRelationships ? Study Design, Data Analysis, and Regulatory Applications
Uses an understanding of pharmacology of the drug (dose→ exposure)
May allow the use of models closely tied to the action of the drug (exposure→response)
Need to consider potential for confounding/effect modification (particularly if E-Rbased on one dose level)
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Concluding thoughts
Many opportunities for interdisciplinary collaboration
Identifying development questions to address throughexposure-response modeling (at portfolio level as well as compoundlevel)
Design of studies to inform exposure-response modeling
Execution of M&S work
What can statisticians do to get more involved?
Be open to learning a new topic
Share what you do know in a collaborative manner (e.g., knowledge ofstudy population vs. knowledge of pharmacology)
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Concluding thoughts
Back-up Slides
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Concluding thoughts
E-R for what endpoints? (editorial aside)
E-R is most believable when you’re modeling endpoints that aremost closely related to the mechanism of action of the drug.
Concentration of drug→ biomarker (possibly with a delayed effect)
As we move farther away from MOA, K-PD or summary measuresof exposure may be reasonable
e.g., average concentration→ tumor growth inhibition
Even farther away, we may need to rely on somescientifically-based hand waving
e.g., Average concentration in Cycle 1→ survival
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