Kinetic Modeling to Accelerate the Development of Nucleic Acid Formulations.

A critical hurdle in the clinical translation of nucleic acid drugs is the inefficiency in testing formulations for therapeutic potential. Specifically, the ability to quantitatively predict gene expression is lacking when transitioning between cell culture and animal studies. We address this challenge by developing a mathematical framework that can reliably predict short-interfering RNA (siRNA)-mediated gene silencing with as few as one experimental data point as an input, evaluate the efficacies of existing formulations in an expeditious manner, and ultimately guide the design of nanocarriers with optimized performances. The model herein consisted of only essential rate-limiting steps and parameters with easily characterizable values of the RNA interference process, enabling the easy identification of which parameters play dominant roles in determining the potencies of siRNA formulations. Predictions from our framework were in close agreement with in vitro and in vivo experimental results across a retrospective analysis using multiple published data sets. Notably, our findings suggested that siRNA dilution was the primary determinant of gene-silencing kinetics. Our framework shed light on the fact that this dilution rate is governed by different parameters, i.e., cell dilution (in vitro) versus clearance from target tissue (in vivo), highlighting a key reason why in vitro experiments do not always predict in vivo outcomes. Moreover, although our current effort focuses on siRNA, we anticipate that the framework can be modified and applied to other nucleic acids, such as mRNA, that rely on similar biological processes.