Skip to main content

BRIEF RESEARCH REPORT article

Front. Pharmacol.
Sec. Translational Pharmacology
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1487062

IMPACT OF USING TIME-AVERAGED EXPOSURE METRICS ON BINARY ENDPOINTS IN EXPOSURE-RESPONSE ANALYSES

Provisionally accepted
Yu-Wei Lin Yu-Wei Lin 1,2Anna Largajolli Anna Largajolli 2Yin Edwards Yin Edwards 2S. Y. Amy Cheung S. Y. Amy Cheung 2Kashyap Patel Kashyap Patel 2*Stefanie Hennig Stefanie Hennig 2,3*
  • 1 Monash University, Melbourne, Australia
  • 2 Certara (United States), Princeton, New Jersey, United States
  • 3 Queensland University of Technology, Brisbane, Queensland, Australia

The final, formatted version of the article will be published soon.

    Exposure-response (ER) analyses are routinely performed as part of model-informed drug development to evaluate the risk-to-benefit ratio for dose selection, justification, and confirmation. For logistic regression analyses with binary endpoints, several exposure metrics are investigated including time-averaged concentration (CavTE). CavTE is informative because it accounts for dose interruptions, modifications, and reductions and is therefore often compared against ER relationships identified using steady-state exposures. However, its derivation requires consideration in a logistic regression framework for time-invariant ER analysis because it has the potential to introduce bias. This study evaluated different approaches to derive CavTE for subjects whom did not have an event by the end of treatment (EoT) and assessed their impact on the ER relationship. Here we used a modified model based on a real data example for simulating exposures and events in different virtual population sizes and drug effect magnitudes. Events were generated using a proportional odds model with Markov components. For subjects whom did not experience an event, CavTE was derived at EoT, EoT+7 days, +14 days, +21 days, +28 days. The derivation of CavTE at different time points demonstrated significant impact on trends detected in logistic ER relationships. Overall, CavTE can be a useful exposure metrics, when considered along with physiological or biological plausibility, the drug’s pharmacokinetic, and mechanism of action. It is recognized that although time-invariant logistic regression is relatively fast and efficient, it overlooks recurring events and doesn’t take into account the time course with the potential drawback of ignoring important elements of the analysis.

    Keywords: Drug Development, Exposure-response analysis, exposure metrics, pharmacometrics, Logistic regression

    Received: 27 Aug 2024; Accepted: 24 Dec 2024.

    Copyright: © 2024 Lin, Largajolli, Edwards, Cheung, Patel and Hennig. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Kashyap Patel, Certara (United States), Princeton, 08540, New Jersey, United States
    Stefanie Hennig, Certara (United States), Princeton, 08540, New Jersey, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.