A revolutionary cell programming platform based on the targeted nano-delivery of a transposon gene editing system

Aim

The NANO-ENGINE project aims to develop a first-in-class, DNA-based, non-viral, targeted in vivo cell programming technology that can be utilized for treating a broad range of diseases, including cancer and genetic disorders. In this project, the consortium aims to assemble, characterize, and test the Targeted Nanoparticles, explore small-scale synthesis, conduct proof of concept studies, and develop a preliminary business plan.

Unmet Need

Currently, the accessibility of cell therapies, specifically CAR-T cell therapy, to the general patient population is limited by high costs, complexity, and safety concerns. These limitations are caused by:

1. Ex vivo manufacturing processes of adoptive cell technologies
2. Requirement of highly specialized clinical facilities
3. The clinical toxicities and the need for preconditioning chemotherapy

Therefore, there is a large unmet need for alternative approaches that make adoptive cell therapy truly affordable, scalable, and widely accessible in an out-patient setting.

Solution

We will develop Targeted Nanoparticles that form CAR-T cells in vivo, by combining a proprietary T cell binder-coated long-circulatory lipid nanoparticle and a non-viral, transposon-based, gene editing system. As such, we will circumvent the aforementioned issues associated with traditional cell therapies with reduced cost, complexity, and associated clinical risks, greatly increasing the accessibility of cell therapies to patients worldwide.

Consortium

The consortium will leverage main applicant NANOCELL’s deep knowledge on non-viral gene transfer technologies.

Expertise Contributions

• UU provides critical expertise on nanomedicine, protein, and cell engineering.
• KI/UOXF has developed a revolutionary single particle analysis method, which allows the consortium to obtain unique drug product characterization insights.
• SINTEF adds crucial know-how on bulk analysis of Targeted Nanoparticles and is closely involved in regulatory standards for nucleic acid therapeutics.
• QSAR uses machine learning to predict nanoparticle performance and minimize experimental work.