Exposure-response modeling for nausea incidence for cotadutide using a Markov modeling approach

Abstract

Cotadutide is a dual glucagon-like peptide-1 (GLP-1)/glucagon receptor agonist. Gastrointestinal adverse effects are known to be associated with GLP-1 receptor agonism and can be mitigated through tolerance development via a gradual up-titration. This analysis aimed to characterize the relationship between exposure and nausea incidence and to optimize titration schemes. The model was developed with pooled data from cotadutide-administrated studies. Three different modeling approaches, proportional odds (PO), discrete-time Markov, and two-stage discrete-time Markov models, were employed to characterize the exposure-nausea relationship. The severity of nausea was modeled as different states (non-nausea, mild, and moderate/severe). The most appropriate model was selected to perform the covariate analysis, and the final covariate model was used to simulate the nausea event rates for various titration scenarios. The two Markov models demonstrated comparable performance and were better than the PO model. The covariate analysis was conducted with the standard Markov model for operational simplification and identified disease indications (NASH, obesity) and sex as covariates on Markov parameters. The simulations indicated that the biweekly titration with twofold dose escalation is superior to other titration schemes with a relatively low predicted nausea event rate at 600 μg (25%) and a shorter titration interval (8 weeks) to reach the therapeutic dose. The model can be utilized to optimize starting dose and titration schemes for other therapeutics in clinical trials to achieve an optimal risk-benefit balance and reach the therapeutic dose with minimal titration steps.

FIGURE 1

Markov model structure to estimate the probability of the severity transitions of nausea/vomiting for cotadutide. P(0|0), P(1|1), P(2|2): the probability of NV stay at the same state; P(1|0) and P(2|0) are the probability for NV transition from 0 to 1 and 2, respectively; P(0|1) and P(2|1) are the probability for NV transition from 0 to 2 and 1, respectively; P(0|2) and P(1|2) are the probability for NV transition from 2 to 0 and 1, respectively.

FIGURE 2

Posterior predictive check (PPC) for different models. PPC for a total number of transitions between varying levels of nausea for 100 simulated datasets. The vertical gray line indicates the observed number of transitions in the clinical data.

FIGURE 3

Forest plot of covariates on nausea incidence compared to the reference (T2DM female). The nausea event rate was focused at 600 μg. Effects of the identified statistically significant covariates on the total nausea, mild nausea, and moderate/severe nausea.

FIGURE 4

Simulation of the proportion of participants with nausea in different titration scenarios in a population with NASH. Titration doses: (1) weekly, biweekly, and monthly: 50, 100, 200, 300, 400, 500, 600 μg; (2) biweekly/monthly with twofold dose increment: 50, 100, 200, 400, 600 μg. The solid line represents the mean incidence of nausea in 200 simulated studies with 200 participants in each study; the shaded area indicates the 90% percentile.