Accelerated discovery of synthetic polymers for ribonucleoprotein delivery through the integration of active learning, machine learning, and polymer science

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PROJECT SUMMARY/ABSTRACT Gene editing systems such as CRISPR/Cas9 have rapidly grown in popularity as research tools and hold the potential to cure a diverse set of genetic disorders. However, effective, safe, and effective delivery remains a significant challenge for therapeutic translation and for application to cell types that are difficult to culture ex vivo. Ideally, intact Cas9 protein would be delivered with its guide RNA (sgRNA) as a purified ribonucleoprotein (RNP), as opposed to Cas9-encoding mRNA or plasmids, to minimize off-target effects. Viral vectors (e.g., AAVs) cannot deliver such large cargo due to their limited capsid size, which exhibit additional challenges with respect to immunogenicity, cost, and manufacturability. Fortunately, synthetic polymers--widely studied in the context of nucleic acid delivery and as biomaterials--have recently shown promise as vehicles for in vivo delivery of sgRNA- Cas9 RNPs. However, there are no consistent design principles by which novel synthetic polymers with improved delivery efficiency, tissue specificity, and safety can be developed. There are far too many polymer structures to test exhaustively or through ad hoc experimentation, so a systematic approach to polymer design, synthesis, and evaluation is required to identify promising candidates. This proposal presents a framework for the discovery of functional polymers through Bayesian experimental design. Machine learning models trained on experimental outcomes will serve as surrogates for experimentation in order to virtually screen a massive library of potential polymer candidates. Polymer candidates will be selected algorithmically through Bayesian Optimization to balance exploration of unknown chemical space and exploitation of structures known to effectively deliver RNPs. Aim 1 will involve (a) the synthesis of a diverse library of biodegradable poly(ester urea amines) (PEUAs), (b) the evaluation of their functional performance using a model fluorescent reporter knock-in/knock-out assay, a cell viability assay, and a metabolic activity assay, and (c) the development and validation of a machine learning model to learn a quantitative relationship between polymer structure/composition and these multiple performance metrics. Aim 2 will involve (a) the enumeration of the chemical space of synthetically accessible PEUAs, and (b) the development and application of a Bayesian Optimization framework leveraging the machine learning model from Aim 1 to guide the selection of candidate polymers from the enumerated space through iterative rounds of experimentation. The outcome of the proposed work will be an integrated tool combining machine learning and polymer science for the unbiased exploration of a broad biomaterial design space, validated through the development of effective and safe RNP delivery vehicles for gene editing that outperform existing commercial polymeric vehicle solutions.