Cationic polymers are versatile alternatives to engineered viruses for the delivery of genome editing payloads. Yet, their clinical translation hinges on facile exploration of broad chemical design spaces and deriving structure-function relationships governing performance. We have discovered a polymer for efficient intracellular CRISPR-Cas 9 ribonucleoprotein (RNP) delivery through combinatorial polymer design and parallelized experimental. A chemically diverse library of 43 statistical copolymers has been synthesized via RAFT polymerization, realizing systematic variations in physicochemical properties. This multiparametric polymer library was examined througha variety of physical and biological characterization techniques, which rapidly uncoveredahitpolymer that outperformed commercial transfection reagents, achieving nearly 60% editing efficiency via non-homologous end-joining. Structure-function correlations underlying editing efficiency, cellular toxicity, and RNP uptake were probed through machine learning approaches to uncover the physicochemical basis of delivery performance. Polymer hydrophobicity and the Hill coefficient, a parameter describing cooperativity-enhanced polymer deprotonation, were identified as the critical determinants of editing efficiency. Combinatorial synthesis and high-throughput characterization methodologies coupled with data science approaches enabled the rapid discovery of a novel polymeric vehicle that would have otherwise remained inaccessible to chemical intuition.

Efficient polymer-mediated delivery of gene editing payloads through combinatorial design, parallelized experimentation, and machine learning

ACS Spring Meeting 2021

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