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RatInhalRNA

Financiado

Cerrado

Rational and Simulation-Supported Design of Inhalable RNA Nanocarrier

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 undrug... ver más

31/03/2028

LMU MUENCHEN

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-02-22

ERC-2022-COG: ERC CONSOLIDATOR GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

LMU MUENCHEN

2.00M€ | Lider

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Duración del proyecto: 61 meses Fecha Inicio: 2023-02-22
Fecha Fin: 2028-03-31

Líder del proyecto

LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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