Emilie Langeskov Salim (1,2), Kim Kristensen (2), Erik Sjögren (1)
(1) Department of Pharmaceutical Biosciences, Translational Drug Discovery and Development, Uppsala University, SE-75124, Uppsala, Sweden; (2) Department of Discovery PKPD & QSP Modelling, Novo Nordisk A/S, DK-2760 Måløv Denmark.
Objectives: N-Acetyl-galactosamine small interfering RNAs (GalNAc-siRNA) are an emerging class of drugs that have revolutionized RNA interfering drug therapy due to its high target specificity and durable knockdown of disease related protein. Conjugation onto GalNAc allows for target specific uptake into hepatocytes via the Asia Glycoprotein Receptor (ASGPR). GalNAc-siRNA exhibits their target protein knockdown from a series of events in the cell cytosol. Initially, the siRNA is loaded into the RNA-induced Silencing Complex (RISC) that cleaves the warranted mRNA, preventing the protein from being translated. With a transient plasma exposure combined with a rapid uptake and prolonged half-life in the liver, GalNAc-siRNA exhibit distinct disposition characteristics compared to small molecules and other therapeutic peptides(1). Within recent years several publications characterizing the unique PK-PD relationship, absorption, and distribution by means of conventional PK-PD modelling have been published(2-4). Physiologically-based pharmacokinetic (PBPK) modeling facilitate for quantitative local disposition description and complex PK-PD relationships and thus can address the needs for investigations of GalNAc-siRNAs(5). To this date only one minimal PBPK (mPBPK) model has been developed aiming to explain and quantifying presumed parameters governing the PBPKPK-PD of GalNAc-siRNAs(6). We aimed to build a generic GalNAc-siRNAs Whole-Body PBPK-PD (WB-PBPK-PD) model for the means of describing systemic and local disposition and the identification of modality specific knowledge gaps.
Methods: For model development a reference data set was compiled from published studies on investigational GalNAc-siRNAs targeting different proteins. For identification and characterization of distribution, uptake and elimination processes data on GalNAc-siRNAs targeting thrombin (ALN-AT3 and SIAT-2)(7, 8), Factor IX (siF9-1 and siF9-2), Factor VII (siF7-3) (9) and Transthyretin protein (SITTR-2) was used(7). The disposition analysis was further informed and evaluated by liver mRNA for each of the compounds included. The time course of the pharmacological effect, expressed as the relative change to base line of the target protein in plasma, was investigated for ALN-AT3 and SITTR2. The WB-PBPK model structure leveraged the PK-Sim default implementation for large molecules, e.g., including the two-pore-formalism for extravasation and elimination from vascular endothelial endosomes(10) (REF). Specification of siRNA and ASGPR/RISC dynamics, e.g., binding, internalization, recycling, as well as downstream translation was further implemented in MoBi. The model was implemented using the open source platform Open System Pharmacology Suite, PK-Sim®/MoBi®, Version 11.2 (11). The PBPK-PD model parameters were either derived from the literature (In vitro/ In vivo experiments) or optimized via Monte Carlo simulation towards reference data.
Results: The model successfully described the GalNAc-siRNAs reference data in terms of I), plasma PK II), liver uptake and elimination, III) siRNA antisense strand loading into RISC, IV) RISC induced silencing of target mRNA, V) and downstream effects on target proteins. Furthermore, the analysis identified significant compound variability to stability and efficacy with direct consequences for the pharmacological effect. In accordance with previous reports, we found cytosolic degradation and endosomal release to be the most impactful processes related to the PK-PD relationship. Equal susceptibility to cytosolic degradation was assumed for all compounds whereas compound specific sensitivity towards RNase and endosomal release/stability was allowed. Overall, the final model was able to describe observed data within a 2-fold difference across all studies. Finally, relevant knowledge gaps were identified during model development, such as mechanistic understanding in extravasation and overall tissue distribution, emphasizing the unique characteristics of GalNAc-siRNAs and the need for further research.
Conclusions: The presented WB-PBPK-PD model successfully quantified systemic and target tissue distribution as well as the complex cellular mechanisms of GalNAc-siRNAs. Compound and system specific attributes of importance for the PK-PD relationship were identified and characterized. This provides a generic WB-PBPK-PD model for the investigation of GalNAc-siRNAs implemented in an open-source platform.
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[11] Open system Pharmacology Suite Manual [Available from: https://docs.open-systems-pharmacology.org/.
Reference: PAGE 32 (2024) Abstr 10802 [www.page-meeting.org/?abstract=10802]
Poster: Drug/Disease Modelling - Absorption & PBPK