Model-based meta-analysis to quantify the effects of short interfering RNA therapeutics on hepatitis B surface...

The objective of this study was to compare the efficacy of short interfering RNA therapeutics (siRNAs) in reducing hepatitis B surface antigen (HBsAg) levels in hepatitis B-infected (HBV) mice across multiple siRNA therapeutic classes using model-based meta-analysis (MBMA) techniques. Literature data from 10 studies in HBV-infected mice were pooled, including 13 siRNAs, formulated as liposomal nanoparticles (LNPs) or conjugated to either cholesterol (chol) or N-acetylgalactosamine (GalNAc). Time course of the baseline- and placebo-corrected mean HBsAg profiles were modeled using kinetics of drug effect (KPD) model coupled to an indirect response model (IRM) within a longitudinal non-linear mixed-effects MBMA framework. Single and multiple dose simulations were performed exploring the role of dosing regimens across evaluated siRNA classes. The HBsAg degradation rate (0.72 day^-1) was consistent across siRNAs but exhibited a large between-study variability of 31.4% (CV%). The siRNA biophase half-life was dependent on the siRNA class and was highest for GalNAc-siRNAs (21.06 days) and lowest for chol-siRNAs (2.89 days). ID₅₀ estimates were compound-specific and were lowest for chol-siRNAs and highest for GalNAc-siRNAs. Multiple dose simulations suggest GalNAc-siRNAs may require between 4 and 7 times less frequent dosing at higher absolute dose levels compared to LNP-siRNAs and chol-siRNAs, respectively, to reach equipotent HBsAg-lowering effects in HBV mice. In conclusion, non-clinical HBsAg concentration-time data after siRNA administration can be described using the presented KPD-IRM MBMA framework. This framework allows to quantitatively compare the effects of siRNAs on the HBsAg time course and inform dose and regimen selection across siRNA classes. These results may support siRNA development, optimize preclinical study designs, and inform data analysis methodology of future anti-HBV siRNAs; and ultimately, support siRNA model-informed drug development (MIDD) strategies.