Project description
In silico design and experimental assessment of RNA therapeutics for lung diseases
RNA therapeutics are a new class of drugs that use RNA molecules to treat or prevent diseases. RNA interference therapeutics are based on a natural mechanism for silencing specific genes. Despite the potential of RNA therapy to treat undruggable targets in the lung, existing RNA formulations are unstable for inhaled delivery. Funded by the European Research Council, the RatInhalRNA project aims to improve RNA therapy for pulmonary disorders. Researchers will employ molecular dynamics and machine learning strategies in combination with polymer synthesis and physico-chemical characterisation to optimise nanoparticles for the pulmonary delivery of RNA therapeutics.
Objective
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.
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.
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. 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. 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 the ability of the ML algorithm to identify design properties of efficient siRNA nanoparticles for pulmonary delivery.
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.
Fields of science
- medical and health scienceshealth sciencesinfectious diseasesRNA virusescoronaviruses
- natural sciencesbiological sciencesgeneticsRNA
- engineering and technologynanotechnologynano-materials
- engineering and technologyindustrial biotechnologybiomaterials
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
80539 MUNCHEN
Germany