Impact of using time-averaged exposure metrics on binary endpoints in exposure-response analyses
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, based on ph...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1487062/full |
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| author | Yu-Wei Lin Yu-Wei Lin Yu-Wei Lin Yu-Wei Lin Yu-Wei Lin Anna Largajolli Anna Largajolli A. Yin Edwards A. Yin Edwards S. Y. Amy Cheung S. Y. Amy Cheung Kashyap Patel Stefanie Hennig Stefanie Hennig |
| author_facet | Yu-Wei Lin Yu-Wei Lin Yu-Wei Lin Yu-Wei Lin Yu-Wei Lin Anna Largajolli Anna Largajolli A. Yin Edwards A. Yin Edwards S. Y. Amy Cheung S. Y. Amy Cheung Kashyap Patel Stefanie Hennig Stefanie Hennig |
| author_sort | Yu-Wei Lin |
| collection | DOAJ |
| description | 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, based on pharmacological plausibility, including time-averaged concentration to event (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 (safety) in different virtual population sizes (n = 50, 100, or 200) and drug effect magnitudes (0.5, 0.75, or 1). 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 that could bias subsequent event projection, dose selection and Go/No-Go decisions. CavTE in censored subjects must therefore be carefully derived to avoid potentially making false positive or negative conclusions. Overall, CavTE can be a useful exposure metrics in an ER analysis, when considered along with physiological or biological plausibility, the drug’s pharmacokinetic, and mechanism of action. Biological plausibility and different analysis factors (e.g., the time of the events with respect to observational period, the level of dose reduction/interruption) should be considered in the choice of the exposure metric. It is recognized that although time-invariant logistic regression is relatively fast and efficient, it overlooks recurring events and does not take into account the exposure and response time course with the potential drawback of ignoring important elements of the analysis like onset or duration of the effect. Care should be taken when ER relationships with other exposure metrics do not identify any statistically significant trends. |
| format | Article |
| id | doaj-art-4da3ee36ca354369b1d7bf9b3c2f7452 |
| institution | Kabale University |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| spelling | doaj-art-4da3ee36ca354369b1d7bf9b3c2f74522025-01-16T08:37:52ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-01-011510.3389/fphar.2024.14870621487062Impact of using time-averaged exposure metrics on binary endpoints in exposure-response analysesYu-Wei Lin0Yu-Wei Lin1Yu-Wei Lin2Yu-Wei Lin3Yu-Wei Lin4Anna Largajolli5Anna Largajolli6A. Yin Edwards7A. Yin Edwards8S. Y. Amy Cheung9S. Y. Amy Cheung10Kashyap Patel11Stefanie Hennig12Stefanie Hennig13Certara Inc., Melbourne, VIC, AustraliaMonash Biomedicine Discovery Institute, Infection Program and Department of Microbiology, Monash University, Clayton, VIC, AustraliaCentre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, VIC, AustraliaMalaya Translational and Clinical Pharmacometrics Group, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur, MalaysiaCertara Inc., Princeton, NJ, United StatesRadnor Corporate Center, Radnor, PA, United StatesCertara Inc., Princeton, NJ, United StatesRadnor Corporate Center, Radnor, PA, United StatesCertara Inc., Princeton, NJ, United StatesRadnor Corporate Center, Radnor, PA, United StatesCertara Inc., Melbourne, VIC, AustraliaCertara Inc., Melbourne, VIC, AustraliaSchool of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, AustraliaExposure-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, based on pharmacological plausibility, including time-averaged concentration to event (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 (safety) in different virtual population sizes (n = 50, 100, or 200) and drug effect magnitudes (0.5, 0.75, or 1). 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 that could bias subsequent event projection, dose selection and Go/No-Go decisions. CavTE in censored subjects must therefore be carefully derived to avoid potentially making false positive or negative conclusions. Overall, CavTE can be a useful exposure metrics in an ER analysis, when considered along with physiological or biological plausibility, the drug’s pharmacokinetic, and mechanism of action. Biological plausibility and different analysis factors (e.g., the time of the events with respect to observational period, the level of dose reduction/interruption) should be considered in the choice of the exposure metric. It is recognized that although time-invariant logistic regression is relatively fast and efficient, it overlooks recurring events and does not take into account the exposure and response time course with the potential drawback of ignoring important elements of the analysis like onset or duration of the effect. Care should be taken when ER relationships with other exposure metrics do not identify any statistically significant trends.https://www.frontiersin.org/articles/10.3389/fphar.2024.1487062/fulldrug developmentexposure-response analysisexposure metricspharmacometricslogistic regression |
| spellingShingle | Yu-Wei Lin Yu-Wei Lin Yu-Wei Lin Yu-Wei Lin Yu-Wei Lin Anna Largajolli Anna Largajolli A. Yin Edwards A. Yin Edwards S. Y. Amy Cheung S. Y. Amy Cheung Kashyap Patel Stefanie Hennig Stefanie Hennig Impact of using time-averaged exposure metrics on binary endpoints in exposure-response analyses Frontiers in Pharmacology drug development exposure-response analysis exposure metrics pharmacometrics logistic regression |
| title | Impact of using time-averaged exposure metrics on binary endpoints in exposure-response analyses |
| title_full | Impact of using time-averaged exposure metrics on binary endpoints in exposure-response analyses |
| title_fullStr | Impact of using time-averaged exposure metrics on binary endpoints in exposure-response analyses |
| title_full_unstemmed | Impact of using time-averaged exposure metrics on binary endpoints in exposure-response analyses |
| title_short | Impact of using time-averaged exposure metrics on binary endpoints in exposure-response analyses |
| title_sort | impact of using time averaged exposure metrics on binary endpoints in exposure response analyses |
| topic | drug development exposure-response analysis exposure metrics pharmacometrics logistic regression |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1487062/full |
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