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

项目总结/摘要 基因编辑系统如CRISPR/Cas9作为研究工具已经迅速普及，并保持了基因编辑的优势。 治愈多种遗传疾病的潜力。然而，有效，安全和有效的交付仍然是一个问题。 这对于治疗性转化和应用于难以离体培养细胞类型是重大挑战。 理想地，完整的Cas9蛋白将与其向导RNA（sgRNA）一起作为纯化的核糖核蛋白（RNP）递送， 与编码Cas9的mRNA或质粒相反，以使脱靶效应最小化。病毒载体（例如，AAVs）不能 由于其有限的衣壳大小，其递送如此大的货物，这在以下方面表现出额外的挑战 免疫原性、成本和可制造性。幸运的是，合成聚合物--在 核酸递送和作为生物材料--最近已经显示出作为sgRNA体内递送的载体的前景， Cas9 RNP。然而，没有一致的设计原则，通过这些原则，具有改进的聚合物性能的新型合成聚合物可以被用于制备聚合物。 可以开发递送效率、组织特异性和安全性。有太多的聚合物结构， 测试详尽或通过特设的实验，所以聚合物设计，合成， 需要进行评估，以确定有前途的候选人。该提案提出了一个框架， 通过贝叶斯实验设计的功能聚合物。机器学习模型在实验上训练 结果将作为实验的替代品，以虚拟筛选大量潜在的 聚合物候选物。将通过贝叶斯优化算法选择聚合物候选物， 平衡探索未知的化学空间和利用已知有效递送RNP的结构。 目的1将涉及（a）生物可降解聚酯脲胺（PEUAs）的多样性库的合成，（B） 使用模型荧光报告基因敲入/敲除测定， 细胞活力测定和代谢活性测定，以及（c）机器学习的开发和验证 模型来了解聚合物结构/组成与这些多重性能之间的定量关系 指标.目标2将涉及（a）可合成的PEUA的化学空间的计数，和（B） 利用机器学习模型的贝叶斯优化框架的开发和应用 从目标1，以指导候选聚合物的选择，从枚举的空间，通过迭代轮的 实验拟议工作的成果将是一个结合机器学习和 聚合物科学的广泛生物材料设计空间的公正探索，通过验证， 开发用于基因编辑的有效和安全的RNP递送载体，其性能优于现有的商业载体。 聚合物载体溶液。

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.