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Impact of using time-averaged exposure metrics on binary endpoints in exposure-response analyses
Yu-Wei Lin1,2,3,4,5, Anna Largajolli6,7, A Yin Edwards6,7
1Certara Inc., Melbourne, VIC, Australia.
Frontiers in Pharmacology
|January 31, 2025
Summary
Careful derivation of time-averaged concentration to event (CavTE) in censored subjects is crucial for accurate exposure-response (ER) analysis. Incorrect CavTE derivation can bias drug development decisions and risk-benefit assessments.
Area of Science:
- Pharmacometrics and Quantitative Pharmacology
- Drug Development and Regulatory Science
Background:
- Exposure-response (ER) analyses are integral to model-informed drug development for dose selection and risk-benefit evaluation.
- Time-averaged concentration to event (CavTE) is an exposure metric that accounts for dose modifications but can introduce bias in logistic regression models.
Purpose of the Study:
- To evaluate different methods for deriving CavTE in subjects without an event by the end of treatment (EoT).
- To assess the impact of CavTE derivation on ER relationships and potential biases in drug development decisions.
Main Methods:
- Simulated exposure and event data using a modified model based on real data.
- Varied virtual population sizes (n=50-200) and drug effect magnitudes (0.5-1).
- Derived CavTE at multiple time points post-EoT for censored subjects (EoT, EoT+7, +14, +21, +28 days).
Main Results:
- The time point chosen for CavTE derivation significantly impacted logistic ER relationships.
- Biased ER trends could lead to incorrect event projections, dose selection, and Go/No-Go decisions.
- Careful derivation of CavTE in censored subjects is essential to avoid false positive or negative conclusions.
Conclusions:
- CavTE is a valuable exposure metric when integrated with pharmacokinetic, pharmacodynamic, and biological plausibility.
- Consideration of biological plausibility, event timing, and dose modification levels is vital for metric selection.
- Time-invariant logistic regression for ER analysis may overlook important temporal aspects of exposure and response.
Keywords:
drug developmentexposure metricsexposure-response analysislogistic regressionpharmacometricsMore Related Videos
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