The lifecycle of a lipid nanoparticle - from process development to therapeutic impact

Research Project, 2025 – 2030

Lipid nanoparticles (LNPs) have proven highly successful in the clinic for the treatment of specific diseases. Despite their success, predicting which LNPs work best for specific applications is still a bottleneck to developing new therapies. Deep learning algorithms may be able to predict favourable LNP properties, as well as to develop new formulations. However, to realise this, a database detailing the structural and dynamic evolution of an LNP from formulation to therapeutic impact is required. The ESS offers a unique opportunity to address this, with unprecedented temporal resolution, at decreased sample volumes, and with higher sample throughput than is currently possible. This ambitious project has been co-developed by a consortium of national and international spanning academia, industry, research institutes and large-scale facilities. Together, we will develop sample environments for the study of LNPs at ESS, data reduction and analysis pipelines, and couple these with advances in molecular dynamics simulations, machine learning, and generative AI to build a library of data on the LNP life cycle that can be used as training sets for GenAI and ML-driven development of LNPs. This is a key step towards LNP rational design for new therapies and will position LNP structural data as a central cornerstone for developing future generations of LNPs for specific diseases and applications.