Pharmacokinetic‐pharmacodynamic (PK/PD) modelling of cotadutide effect in patients with chronic kidney disease and type 2 diabetes mellitus

May 9, 2025British journal of clinical pharmacology

Modeling how cotadutide works and moves through the body in people with chronic kidney disease and type 2 diabetes

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Abstract

After 26 weeks of treatment with 600 μg cotadutide, urine albumin-to-creatinine ratio (UACR) decreased by 45.6%.

A significant relationship was identified between cotadutide exposure and changes in UACR, urinary albumin (UALB), and body weight.
Higher doses of cotadutide were associated with greater reductions in both UACR and UALB.
Baseline blood pressure and urinary albumin levels influenced the effectiveness of cotadutide in reducing UACR and UALB.
Model predictions indicated that treatment with cotadutide resulted in a 47.2% reduction in UALB after 26 weeks.
Findings suggest that the developed pharmacokinetic-pharmacodynamic models can be used for data analysis in chronic kidney disease and diabetic kidney disease studies.

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AIMS: Cotadutide is a dual glucagon-like peptide-1/glucagon receptor agonist. The objective of the analysis was to develop a pharmacokinetic-pharmacodynamic (PK/PD) model to describe the relationship between cotadutide exposure and response on urine albumin-to-creatinine ratio (UACR), urinary albumin (UALB), and body weight in participants with chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) using data from a Phase2b study (NCT04515849).

METHODS: A total of 247 participants with CKD and T2DM were randomized and titrated to either 100, 300 or 600 μg cotadutide, 1 mg semaglutide or placebo. UACR was measured biweekly from either morning void (Weeks 14 and 26) or spot urine (other visits). The analysis was implemented using a longitudinal non-linear mixed-effect model. The potential impact of covariates on efficacy in participants was quantified.

RESULTS: PK/PD models were developed, and a significant relationship was identified between cotadutide exposure and PD biomarkers of UACR, UALB and body weight. The models described the data adequately; greater changes in PD responses were observed with higher cotadutide doses. Baseline mean blood pressure and baseline UALB were found to affect the reductions in UACR and UALB, respectively. Model-predicted relative change from placebo in UACR, UALB and body weight after 26 weeks of 600 μg cotadutide treatment were -45.6% (-52.4%, -38.7%), -47.2% (-56.0%, -39.9%) and -5.3% (-7.6%, -4.1%), respectively.

CONCLUSIONS: This modelling assessment was successfully applied for cotadutide to understand the relationship between cotadutide dosing regimen and the response in UACR, UALB and body weight. These models have general application in analysing and interpreting data from CKD/diabetic kidney disease (DKD) studies.

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Pharmacokinetic‐pharmacodynamic (PK/PD) modelling of cotadutide effect in patients with chronic kidney disease and type 2 diabetes mellitus

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