Limited · human
Two single-dose pharmacokinetic studies investigated whether renal or hepatic impairment affects how the body processes cagrilintide, a long-acting amylin agonist in development for weight management and type 2 diabetes (also studied in combination with semaglutide as "CagriSema"). In each study, adult participants were grouped by organ function (normal, mild, moderate, or severe impairment) and received a single subcutaneous dose of cagrilintide. The renal study enrolled 33 participants and the hepatic study enrolled 32. The primary measure was total drug exposure (AUC₀–∞), with secondary measures including peak concentration (Cmax) and time to peak (tmax). Both studies found that cagrilintide exposure was broadly similar across all impairment levels; estimated AUC ratios relative to normal function ranged from approximately 0.99 to 1.23, with overlapping confidence intervals. No serious adverse events, study withdrawals, or deaths occurred, and no increase in adverse events was observed with worsening organ impairment. The authors concluded that dose adjustment may not be necessary in these populations. Key limitations include small group sizes, single-dose design, and the inability to generalize to steady-state conditions or combined therapies.
Clinical pharmacokinetics · Jun 2026DOI ↗ Limited · human
This study used synthetic target trial emulation and computational predictive modeling to compare amylin-pathway therapies — specifically CagriSema, cagrilintide, and amycretin formulations — for obesity and type 2 diabetes. Following PRISMA 2020 and TARGET framework guidelines, the researchers pooled data from seven randomized controlled trials (N = 5,786 participants) published through September 2025. Rather than analyzing real individual patient data, they reconstructed high-precision synthetic individual patient datasets and applied network meta-analysis, dose-response modeling, virtual head-to-head comparisons, and machine learning. The study reported that synthetic data reconstruction achieved greater than 99% fidelity to source trials, and virtual modeling suggested CagriSema outperformed subcutaneous amycretin at matched timepoints (posterior probability >0.95). Dose-response modeling identified an estimated ED80 for amycretin and benefit-risk analysis suggested a potential therapeutic window in the 10–20 mg subcutaneous range. Machine learning models predicted treatment response with 82–87% accuracy from baseline characteristics. Key limitations include reliance on reconstructed — not real — individual patient data, indirect comparisons rather than direct head-to-head trial evidence, and calibration metrics indicating moderate model uncertainty. The authors suggest these findings may inform future confirmatory trial design.
Metabolism open · Oct 2025DOI ↗