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. Therefore, we hypothesize that combinatorial polymer libraries, high-throughput experimentation and statistical models can be combined to yield a powerful predictive platform for the discovery and optimization of polymeric vehicles for CRISPR/Cas9 delivery. In this work, a chemically diverse library of well-defined statistical copolymers was synthesized through RAFT polymerization and physicochemical properties such as polymer composition, molecular weight, ζ-potential, pK<sub>a</sub>, polyplex diameter evaluated. Polymer candidates were screened after complexation with two types of clinically relevant genetic payloads: 1) plasmid DNA and 2) ribonucleoprotein complexes (RNP) comprising a single guide RNA (sgRNA) and Cas9 protein. We suggest that the hit polymers identified through this study could not have been accessible through human intuition and hypothesis-driven approaches. In addition, we probed differences between polymer design criteria for plasmids and RNPs. Using the data obtained through this high-throughput screen, we expect to apply statistical techniques to mine structure-function correlations and predictive relationships that can aid experimentalists engaged in polymer design and synthesis for diverse gene therapy applications.

Development of polymeric biomaterials using high-throughput experimentation and statistical learning

2019 Fall National Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.