Prediction of thyroid C-cell carcinogenicity after chronic administration of GLP1-R agonists in rodents
2017
Abstract Increased incidence of C-cell carcinogenicity has been observed for glucagon-like-protein-1 receptor (GLP-1r) agonists in rodents. It is suggested that the duration of exposure is an indicator of carcinogenic potential in rodents of the different products on the market. Furthermore, the role of GLP-1-related mechanisms in the induction of C-cell carcinogenicity has gained increased attention by regulatory agencies. This study proposes an integrative pharmacokinetic/pharmacodynamic (PKPD) framework to identify explanatory factors and characterize differences in carcinogenic potential of the GLP-1r agonist products. PK models for four products (exenatide QW (once weekly), exenatide BID (twice daily), liraglutide and lixisenatide) were developed using nonlinear mixed effects modelling. Predicted exposure was subsequently linked to GLP-1r stimulation using in vitro GLP-1r potency data. A logistic regression model was then applied to exenatide QW and liraglutide data to assess the relationship between GLP-1r stimulation and thyroid C-cell hyperplasia incidence as pre-neoplastic predictor of a carcinogenic response. The model showed a significant association between predicted GLP-1r stimulation and C-cell hyperplasia after 2 years of treatment. The predictive performance of the model was evaluated using lixisenatide, for which hyperplasia data were accurately described during the validation step. The use of a model-based approach provided insight into the relationship between C-cell hyperplasia and GLP-1r stimulation for all four products, which is not possible with traditional data analysis methods. It can be concluded that both pharmacokinetics (exposure) and pharmacodynamics (potency for GLP-1r) factors determine C-cell hyperplasia incidence in rodents. Our work highlights the pharmacological basis for GLP-1r agonist-induced C-cell carcinogenicity. The concept is promising for application to other drug classes.
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