A kinetic modeling platform for predicting the efficacy of siRNA formulations in vitro and in vivo

We present a computational modeling protocol that can accurately predict changes in both in vitro and in vivo gene expression levels in response to the application of various siRNA formulations. We describe how to use this Python-based pipeline to obtain crucial information, namely maximum silencing level and duration of silencing, toward the design of therapeutically relevant dosing regimens. The protocol details the steps for running internalization rate fitting to produce predictions based on experimental measurements from a single time point. For complete details on the use and execution of this protocol, please refer to Roh et al., 2021.