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Rational and Simulation-Supported Design of Inhalable RNA Nanocarrier

Periodic Reporting for period 1 - RatInhalRNA (Rational and Simulation-Supported Design of Inhalable RNA Nanocarrier)

Reporting period: 2023-04-01 to 2025-09-30

The RatInhalRNA project is progressing very well and is currently slightly ahead of schedule. So far, we have completed all planned milestones up to number seven. We successfully developed and tested a new set of materials designed to carry RNA into cells and created a precise method for producing nanoparticles using these materials. These nanoparticles were then tested after being turned into an inhalable mist, using standard lab-grown cells. We also used advanced computer simulations to better understand how the materials work on a molecular level, which helped us refine their performance. Additionally, we established a lung infection model using a common coronavirus and are now working with SARS-CoV-2, thanks to recently updated safety guidelines. The results of our experiments with lung infection and gene delivery are highly promising. At the same time, we developed two machine learning models that can predict which materials will work best for RNA delivery. Using these predictions, we created improved materials, tested them in the lab, and began initial studies in healthy animals.
Our team has made several important breakthroughs. We are the first to report a new RNA-delivery material based on spermine, a naturally occurring compound. We are also the first to establish a detailed laboratory model that mimics how common coronaviruses infect the lungs. Furthermore, we are leading the way in using molecular simulations to understand how our delivery systems function, and in using machine learning to design new materials for this purpose. Although the use of artificial intelligence in developing drug delivery systems has grown rapidly, our group is among the first to apply it specifically to polymer-based RNA carriers, positioning us at the forefront of this innovative field.
The project has led to several important breakthroughs in developing safer and more effective RNA delivery systems. One major achievement was finding a way to improve the effectiveness of the nanoparticles without increasing their toxicity, which has long been a challenge in the field. We also succeeded in creating RNA-loaded nanoparticles that can be turned into an inhalable mist while still keeping their biological activity intact. To fine-tune how these particles are made, we used advanced tools including computer simulations and molecular analysis to identify and reduce the amount of material in the formulation that does not actually contribute to RNA delivery. Another key step was building a laboratory model that closely mimics how common coronaviruses infect the lungs, which provides a realistic testing ground for future therapies. Finally, using machine learning, we were able to predict new materials that not only work well in lab tests but also effectively switch off specific genes in living animals, which is a promising step toward future treatments.
This project has made several groundbreaking contributions to the science of RNA formulation and delivery. One key advancement lies in improving how we design the materials used to carry RNA into cells. Because the process of making these polymers is usually hard to control, our ability to predict their properties based on how they are made marks a major step forward in materials science. Another challenge in the field has been the fact that RNA nanoparticles are very fragile and can be damaged when turned into a mist for inhalation. Our success in developing stable, nebulizable RNA formulations opens the door to delivering RNA therapies directly to the lungs. Additionally, scientists have long struggled with the question of unused or “free” material in these formulations, which can reduce effectiveness or cause side effects. By combining lab experiments with computer simulations, we were able to finally measure and better understand this problem, which is a significant breakthrough for researchers working on self-assembling nanoparticles. Finally, we were the first to use lab-based data to predict new RNA delivery materials that went on to work successfully in living animals, which is an exciting milestone for anyone developing new RNA-based treatments.
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