Advancing Delivery through Digital Bottlebrush Polymers

About This Grant

Technical Summary This proposal aims to develop a platform for the rational design of polymer-based oligonucleotide delivery vehicles using digital bottlebrush architectures. The central innovation is the design of sequence-defined polymer backbones that allow precise tuning of physiochemical and biological properties. The system—termed pacDNA—consists of antisense oligonucleotides conjugated to the backbone of bottlebrush poly(ethylene glycol) (PEG). Earlier studies showed that slight variations in backbone composition (e.g., polynorbornene vs. polyphosphodiester) cause significant differences in biological behavior, despite accounting for less than 5% of total molecular weight. This observation motivated the 'backbonomics' approach: systematically modifying backbone structure to study and predict delivery efficiency, biodistribution, and immune evasion. The project has two main goals: (1) synthesize and characterize a library of ribose-based digital bottlebrush polymers with controlled hydrophobicity and charge, and (2) study their biodistribution, pharmacokinetics, immune response, and therapeutic efficacy in vitro and in vivo. Experimental results will be used to train machine learning (ML) models for predictive design. Preliminary studies demonstrate feasibility in synthesis, molecular simulation, and biological evaluation. Key advances include identifying spacing patterns of hydrophobic units that enhance cellular uptake and using pacDNA to suppress IL-17RA expression in a murine psoriasis model. Overall, the proposed research integrates synthetic chemistry, biomaterials science, molecular biology, and machine learning to expand oligonucleotide delivery capabilities. Non-Technical Summary This research aims to improve the way genetic medicines are delivered into the body. Many promising drugs made from DNA or RNA struggle to reach their targets inside our cells due to poor stability and limited absorption. The proposed project will develop new delivery vehicles made of polymers—large molecules that can be engineered to carry and protect these drugs. Specifically, the team will use a ‘bottlebrush’ design, where many protective chains are attached to a central backbone. By changing the chemical makeup and spacing of units along the backbone, we can fine-tune how these particles behave in the body. The project also uses machine learning to find patterns between structure and function, helping to predict what designs work best. The ultimate goal is to create a delivery platform that helps genetic medicines reach difficult tissues like skin and muscle more effectively and with fewer side effects. In addition to scientific innovation, the project includes educational outreach to high school and college students and course development to train the next generation of scientists in this field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.