Descripción del proyecto
Diseño «in silico» y evaluación experimental de terapias de ARN para enfermedades pulmonares
Los tratamientos con ARN son una nueva clase de fármacos que utilizan moléculas de ARN para tratar o evitar enfermedades. Las terapias de interferencia por ARN se basan en un mecanismo natural para silenciar genes específicos. A pesar del potencial de la terapia de ARN para tratar dianas no tratables en el pulmón, las formulaciones de ARN existentes resultan inestables para su administración inhalada. El equipo del proyecto RatInhalRNA, financiado por el Consejo Europeo de Investigación, pretende mejorar la terapia con ARN para los trastornos pulmonares. Los investigadores emplearán estrategias de dinámica molecular y aprendizaje automático junto con la síntesis de polímeros y la caracterización fisicoquímica para optimizar nanopartículas destinadas a la administración pulmonar de fármacos de ARN.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
80539 MUNCHEN
Alemania