(9h) Accelerating the Discovery of Polymeric Vehicles for Gene Editing through Combinatorial Synthesis and Statistical Learning

Complexity is a hallmark of interactions between synthetic materials and the biological environment, but frequently poses barriers to the hypothesis-guided discovery of new materials. Developing polymers for gene delivery is one such multi-dimensional design challenge that requires an intricate balance between intermolecular forces implicated in the packaging, cellular uptake and release of genetic payloads from polymer-nucleic acid complexes. Lacking a complete mechanistic picture of the effects of polymer composition and architecture on its efficiency and toxicity, polymer chemists rely on iterative “one-polymer-at-a-time” approaches that are often plagued by trial and error. Though gene editing has made rapid progress, the paucity of chemically defined, safe and efficient delivery vehicles has hindered its clinical translation. To accelerate polymer discovery for vital clinical applications such as gene editing, we need to replace traditional iterative processes with alternative experimental strategies that leverage advances in parallel synthesis and data analytics. In this talk, I will describe how combinatorial polymer libraries, high-throughput experimentation and statistical models have been combined to yield a powerful predictive platform for the discovery and optimization of polymeric vehicles for CRISPR/Cas9 delivery. A chemically diverse library of well-defined statistical copolymers was synthesized through RAFT polymerization and physicochemical properties such as polymer composition, molecular weight, zeta potential, pKa, polyplex diameter evaluated. Polymer candidates were screened after complexation with clinically relevant genetic payloads such as plasmid DNA and ribonucleoprotein complexes (RNP) comprising single guide RNA (sgRNA) and Cas9 protein. The hit polymer identified through this study outperformed commercial transfection reagents by achieving nearly 60% editing efficiency via non-homologous end-joining. We suggest that this polymer could not have been accessible through human intuition and hypothesis-driven approaches. Further, for each payload, we identified the structural drivers for nucleic acid internalization, cell viability and transfection efficiency and mined structure-function correlations and predictive relationships that can aid experimentalists engaged in polymer design and synthesis for diverse gene therapy applications.