Rational and Simulation-Supported Design of Inhalable RNA Nanocarrier

Introduction

The overarching goal of RatInhalRNA is to computationally predict and develop efficient formulations for pulmonary RNA therapy. New RNA formulations are imperative for clinical RNA delivery beyond the liver. The lung offers undruggable targets which could be treated with RNA therapeutics. However, approved siRNA formulations are not suited for pulmonary delivery due to instability in lung surfactant and during nebulization. Hence, it is my aim to rationally design inhalable and biocompatible polymer-based siRNA formulations for efficient siRNA delivery to the lung.

Methodology

While biomaterials are commonly optimized empirically via one-variable-at-a-time experimentation, I am the first to combine Design-of-Experiments (DoE) with Molecular Dynamics (MD) Simulations and Machine Learning (ML) to accelerate the discovery and optimization process of siRNA nanocarriers towards the metrics of gene silencing efficacy and biocompatibility at reduced wet-lab resources.

Experimental Design

In RatInhalRNA, I will synthesize amphiphilic polyspermines and will prepare siRNA-loaded nanoparticles by microfluidic assembly for experimental assessment of physico-chemical parameters as well as in vitro and in vivo gene silencing efficacy in coronavirus infection models.

Analysis

I will assess siRNA binding of the polyspermines via MD simulations and will analyze the contribution of the nanoparticle design factors on experimental and computational readout responses of the DoE.

Machine Learning Application

I will train a support vector machine for supervised ML and will generate models to identify areas of interest. Based on the predictions, I will test additional formulations to obtain a validation dataset for the assessment of the ability of the ML algorithm to identify design properties of efficient siRNA nanoparticles for pulmonary delivery.

Future Impact

RatInhalRNA will enable me to predict favorable siRNA nanoparticle characteristics in the future prior to polymer synthesis, thereby reducing experimental work and improving sustainability and animal welfare.