ArticleMolecular therapy. Nucleic acids2026
A computational model-powered platform to inform the development of GalNAc-conjugated siRNA therapeutics.
Article in Molecular therapy. Nucleic acids, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
N-acetylgalactosamine-conjugated small interfering RNA (GalNAc-siRNA) therapeutics have emerged as a groundbreaking modality with unparalleled efficacy for battling previously "undruggable" diseases. The unique pharmacokinetic (PK) and pharmacodynamic (PD) characteristics of GalNAc-siRNA therapeutics provide an opportunity to leverage PK/PD modeling strategies for drug development. By utilizing the wealth of literature data, we developed and validated a mechanistic computational model-driven platform to guide the development of new GalNAc-siRNA therapeutics, optimizing their clinical translation. This platform integrates preclinical and clinical data from all seven FDA-approved GalNAc-siRNA drugs-fitusiran, givosiran, inclisiran, lumasiran, vutrisiran, nedosiran, and plozasiran-spanning multiple species (mouse, rat, monkey, and human). To enhance user accessibility, we further implemented a web-based Shiny application. The platform was used to inform the development of an investigational new angiotensinogen-silencing GalNAc-siRNA (SAL0132). Multiple PK/PD datasets from rats and monkeys were satisfactorily fitted, and extrapolated to humans. The platform successfully predicted the PK and simulated the PD profiles of SAL0132 in humans, which demonstrated model-informed strategies to support efficient drug development of this modality. In conclusion, this platform enables users to predict GalNAc-siRNA PK/PD profiles across species by inputting specific model parameters, providing a powerful resource to guide the development of next-generation GalNAc-siRNA therapeutics.
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