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Cross-Species Translation and Target-Dependent Pharmacology of GalNAc-siRNAs Using a Multi-Parameter K-PD Framework
Xiaofei Wu1, Xuanji Peng2, Ranran Jia1
1Clinical Pharmacology Research Center, Beijing Key Laboratory of Key Technologies for Early Clinical Trial Evaluation of Innovative Drugs for Major Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
Background:
Small interfering RNAs (siRNAs) conjugated with N-acetylgalactosamine (GalNAc) are liver-targeted therapeutics with sustained activity. However, their cross-species pharmacokinetic (PK) and pharmacodynamic (PD) translation remains poorly defined, particularly regarding target dependence and PD endpoint selection. This study aimed to develop a kinetic-pharmacodynamic (K-PD) modeling framework to characterize their long-term pharmacology, cross-species translational relationships, and target mRNA effects by quantifying biophase half-life, in vivo potency (IDK5 0), and PD turnover.
Methods:
Publicly available PD time-course data for 30 GalNAc-siRNAs across four species (mice, rats, monkeys, and humans) were compiled through a targeted search of the literature and other public sources. A unified K-PD modeling framework was applied to estimate key parameters and evaluate cross-species translational behavior and target-related variability.
Results:
Human biophase half-life exceeded that in preclinical species, whereas human IDK5 0 was lower. PD half-life in humans approximated that in nonhuman primates. Despite consistent cross-species trends overall, K-PD parameters varied across compounds and targets. PD turnover half-life showed greater within-target consistency than IDK5 0, suggesting differential sensitivity of these endpoints to target biology.
Conclusion:
The K-PD modeling framework characterized the cross-species pharmacology of GalNAc-siRNAs, confirmed consistent translational patterns, and identified target-specific effects on PD kinetics. These findings facilitate early human pharmacological prediction, enabling a shift from empirical scaling toward model-informed translational decision-making.
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