Evidence map›Paper›PMID 42840589›Full record

ArticleFrontiers in pharmacology2026

Exposure-driven tolerance modeling of efpeglenatide-induced nausea or vomiting using repeated time-to-event analysis: implications for dose-escalation strategies.

Jiyoung Seo, Seungchan Choi, NaYoung Kim, Hyeeun Kim, Hyeong-Seok Lim

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Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Jiyoung SeoAsan Medical Center, Department of Clinical Pharmacology and Therapeutics, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Seungchan ChoiAsan Medical Center, Department of Clinical Pharmacology and Therapeutics, University of Ulsan College of Medicine, Seoul, Republic of Korea.
NaYoung KimHanmi Pharm. Co., Ltd., Seoul, Republic of Korea.
Hyeeun KimHanmi Pharm. Co., Ltd., Seoul, Republic of Korea.
Hyeong-Seok LimAsan Medical Center, Department of Clinical Pharmacology and Therapeutics, University of Ulsan College of Medicine, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Nausea or vomiting are common adverse events (AEs) associated with glucagon-like peptide-1 receptor agonists and often decline over time despite continued exposure, suggesting tolerance development. However, the longitudinal dynamics of recurrent events and tolerance have not been quantitatively characterized for efpeglenatide. This study aimed to characterize efpeglenatide-induced nausea/vomiting in non-diabetic patients with obesity using repeated time-to-event (RTTE) modeling and to explore its implications for dose-escalation strategies. Methods: Data from six clinical trials were pooled, and 678 participants with obesity and/or type 2 diabetes mellitus were included in the analysis. A total of 672 nausea or vomiting events were analyzed using a sequential PK-AE approach based on a previously developed population pharmacokinetic model. Separate RTTE models were developed for an obesity population and a pooled total population. The models incorporated baseline hazard, a time-decaying non-drug effect, and an exposure-driven drug effect with tolerance linked to cumulative exposure (AUC). Model performance was evaluated using bootstrap and visual predictive checks, and Monte Carlo simulations were performed to assess nine dose-escalation scenarios. The obesity model served as the primary model for predicting dosing regimen-dependent AE patterns, whereas the pooled total model was used as an exploratory model to assess the concentration-AE response relationship. Results: Among the 678 participants, 247 (36.4%) experienced at least one episode of nausea or vomiting. In the obesity model, the exposure-hazard relationship was best described by a log-linear function. The estimated AUC associated with 50% tolerance development was 109,872 h ng/mL in the obesity model. Among the nine dosing scenarios, the Case 1-4 titration regimens achieved approximately 50% tolerance within 3-4 weeks while maintaining a lower early AE burden than high fixed-dose regimens. Low fixed-dose regimens delayed tolerance development, whereas high fixed-dose regimens increased the early AE burden. Conclusion: Efpeglenatide-associated nausea or vomiting was well characterized using an exposure-driven RTTE framework, and early cumulative exposure appeared to play a central role in tolerance development. These findings suggest that, in non-diabetic patients with obesity, stepwise titration of efpeglenatide may help optimize tolerability by mitigating early AE burden while allowing sufficient cumulative exposure for tolerance development.

Indexed as

adverse eventsefpeglenatideGLP-1 agonistHM11260Cnausea or vomitingNONMEM®repeated time-to-eventstolerance

Identifiers

PMID42840589
PMCPMC13639893

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.