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Dose-Response and Clinical Equivalence of Semaglutide and Tirzepatide for Weight Loss in Type 2 Diabetes: A
Carlos E Builes-Montaño1, Andres F Suarez-Rodriguez2, Maria A Alzate-Vinasco2
1Hospital Pablo Tobón Uribe and Universidad de Antioquia, Medellín, Colombia. esteban.builes@udea.edu.co.
Introduction:
Semaglutide and tirzepatide are highly effective incretin-based therapies for weight reduction in individuals with type 2 diabetes (T2DM). However, direct comparisons across the full range of clinically relevant doses in T2DM are limited. This study aimed to evaluate dose-response relationships and clinical equivalence between semaglutide and tirzepatide using a model-based approach.
Methods:
Arm-level data from phase III randomized controlled trials of semaglutide (SUSTAIN, STEP) and tirzepatide (SURPASS, SURMOUNT-2) in T2DM were analyzed. Dose-response relationships for percent weight change were modeled using generalized additive models and Bayesian hierarchical spline models. Posterior probabilities of clinical equivalence were estimated across prespecified dose pairs using an equivalence margin of ± 2 percentage points. Model-based intensification scenarios were evaluated to estimate incremental benefit associated with treatment switching or dose escalation.
Results:
A total of 48 treatment arms (n = 16,524 participants) were included. Both agents demonstrated nonlinear dose-response relationships, with attenuation of incremental effects at higher doses. High probabilities of equivalence were observed for semaglutide 2.4 mg versus tirzepatide 10 mg (99.4%) and semaglutide 7.2 mg versus tirzepatide 15 mg (94.8%). Lower doses of semaglutide were not equivalent to higher doses of tirzepatide. In intensification scenarios, both switching and dose escalation strategies improved weight loss, although the probability of achieving ≥ 2 additional percentage points varied across regimens.
Conclusion:
Semaglutide and tirzepatide demonstrate comparable weight-loss efficacy at specific dose combinations, with equivalence driven by dose rather than drug identity. Model-based analyses provide a quantitative framework to support individualized treatment decisions when direct comparative evidence is unavailable.
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