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Impact of using time-averaged exposure metrics on binary endpoints in exposure-response analyses

This Frontiers in Pharmacology article investigates how using different time-averaged exposure metrics, specifically the time-averaged concentration to event (CavTE), impacts exposure-response (ER) analyses, particularly when using logistic regression with binary endpoints in drug development. ER analyses are crucial in determining the optimal drug dose that balances efficacy and safety.

The study focuses on how CavTE, which accounts for actual dosing variations like interruptions or reductions, can provide a more accurate reflection of drug exposure compared to steady-state metrics. However, deriving CavTE for subjects who do not experience an event (e.g., side effects or treatment responses) by the end of the study poses challenges. Different methods for estimating CavTE in these “censored” cases can introduce biases in the analysis.

Using a simulated dataset, the authors examined various approaches to calculate CavTE for subjects without events, evaluating how these methods affected the trends in ER relationships. They found that the timing of CavTE calculation significantly influenced the results, potentially leading to incorrect conclusions about drug efficacy or safety. Therefore, careful consideration is needed in selecting and deriving exposure metrics like CavTE to avoid biases and improve the accuracy of ER analyses in drug development.

Author(s): Yu-Wei Lin, Anna Largajolli, A. Yin Edwards, S. Y. Amy Cheung, Kashyap Patel, Stefanie Hennig

Year: 2025年1月15日

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