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

Area of Science:

• Pharmacology
• Clinical Pharmacology
• Drug Development

Background:

• Cotadutide is a dual glucagon-like peptide-1 (GLP-1)/glucagon receptor agonist.
• Gastrointestinal adverse effects, particularly nausea, are common with GLP-1 receptor agonists.
• Gradual dose titration is a strategy to mitigate these effects and improve patient tolerance.

Purpose of the Study:

• To characterize the exposure-nausea relationship for cotadutide.
• To optimize cotadutide titration schemes for improved tolerability.
• To simulate nausea event rates under different dosing strategies.

Main Methods:

• Pooled data from cotadutide studies were used.
• Three modeling approaches were employed: proportional odds, discrete-time Markov, and two-stage discrete-time Markov models.
• Nausea severity was modeled (non-nausea, mild, moderate/severe), and covariate analysis identified influencing factors.

Main Results:

• Markov models outperformed the proportional odds model in characterizing the exposure-nausea relationship.
• Disease indications (NASH, obesity) and sex were identified as covariates affecting nausea.
• Biweekly titration with twofold dose escalation showed a lower predicted nausea rate (25% at 600 μg) and faster achievement of therapeutic dose.

Conclusions:

• A biweekly, twofold dose escalation titration scheme is superior for cotadutide administration.
• This optimized titration strategy can minimize nausea and improve the risk-benefit balance.
• The developed model can inform titration strategies for other therapeutics in clinical trials.