DESTINATION is an EU FET-Open project. By combining the interdisciplinary fields of AI/machine learning with RNA nanotechnology, biochemistry and advanced imaging methods, DESTINATION aims to create an RNA-based delivery platform (RNano) for effective delivery of information such as mRNA into cells in vivo. mRNA translates the information encoded in a cell’s DNA into the proteins that are essential for diverse cell function and can be deficient in disease. Administering mRNA into a cell could enable diverse novel functions such as production of its own medicine by replacing the faulty mRNA or engineering cells to fight diseases from genetic disorders and cancer to infectious diseases. However, the ability to deliver mRNA to specific cells in a targeted organ remains an unmet challenge that limits its clinical and commercial potential. Addressing this challenge requires a novel, biocompatible and scalable system capable of:
(1) protecting the mRNA from degradation in blood;
(2) evading the immune response;
(3) and providing high selectivity for targeted cells.
DESTINATION will generate an intelligent library of:
(a) programmable RNano scaffolds for attachment of packaged mRNA and
(b) RNA aptamers for laser-specific internalization of RNanos into cells.
Promising candidates will be tested in vitro, with lead candidates progressing to novel animal models. Super resolution imaging will allow for the evaluation of the technology, with an iterative R&D approach aimed at demonstrating 3 breakthrough preclinical proof-of-concepts including phenylketonuria treatments. RNanos utilise a unique and disruptive strategy compared to competitors, designed to ensure the following competitive advantages: enhanced safety, efficacy, and scalable synthesis, manufacturing and production.
This project is important for society, as it is targeting 3 diseases of highly unmet need: Phenylketonuria, Melanoma and B-Cell Lymphoma. Successful validation of RNanos could eventually improve safety and efficacy of treatments and ultimately decrease cost for end users/public health systems.
Overall objectives include:
Development of an AI discovery platform
Validation of an RNano scaffold for therapeutic delivery
Creation of an intelligent library of lead cell-internalizing aptamers
Animal models for proof-of-concept in vivo CAR-T, mRNA and CRISPR therapies
Novel methods based on multicolour single molecule imaging tailored for RNA imaging
DNA/RNA origami synthesis and conjugation to RNanos
Logic gates (for mRNA protection)
Optimised synthesis workflows
Communication of outputs via international conferences, workshops and high-impact publications.
