A computational model-powered platform to inform the development of GalNAc-conjugated siRNA therapeutics

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.