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

通过整合主动学习、机器学习和聚合物科学，加速发现用于核糖核蛋白递送的合成聚合物

• 批准号: 10195432
• 负责人: Connor Wilson Coley
• 金额: $ 19.76万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10195432
• 关键词: Active Learning; Algorithms; Amines; Architecture; Biocompatible Materials; Biological Assay; Biological Process; CRISPR/Cas technology; Capsid; Cell Survival; Cell model; Cells; Chemicals; Clinical Trials; Complex; DNA; Data; Development; Equilibrium; Esters; Evaluation; Exhibits; Experimental Designs; Gene Delivery; Genes; Genetic Diseases; Guide RNA; Human; Knock-in; Knock-out; Learning; Length; Libraries; Lipids; Machine Learning; Maps; Mediating; Messenger RNA; Metabolic; Methods; Modeling; Outcome; Performance; Plasmids; Polymer Chemistry; Polymers; Property; Proteins; RNA; RNA delivery; Reporter; Research; Ribonucleoproteins; Ribonucleotides; Safety; Science; Specificity; Structure; System; Techniques; Technology; Testing; Therapeutic; Tissues; Training; Translations; Urea; Validation; Viral Vector; Work; base; biomaterial compatibility; candidate selection; cell type; cheminformatics; cost; cytotoxicity; design; exhaustion; experimental study; gene therapy; immunogenicity; improved; in silico; in vivo; manufacturability; manufacturing process; novel; nucleic acid delivery; polypeptide; relating to nervous system; tool; virtual; virtual screening

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.

项目概要/摘要 CRISPR/Cas9 等基因编辑系统作为研究工具已迅速普及，并占据着主导地位。 具有治愈多种遗传性疾病的潜力。然而，有效、安全和有效的交付仍然是一个 治疗转化和应用于难以离体培养的细胞类型面临着重大挑战。 理想情况下，完整的 Cas9 蛋白将与其向导 RNA (sgRNA) 作为纯化的核糖核蛋白 (RNP) 一起递送， 与 Cas9 编码 mRNA 或质粒相反，可以最大限度地减少脱靶效应。病毒载体（例如 AAV）不能 由于衣壳尺寸有限，运送如此大的货物，这在运输方面带来了额外的挑战 免疫原性、成本和可制造性。幸运的是，合成聚合物——在以下背景下得到了广泛研究： 核酸递送和生物材料——最近显示出作为 sgRNA 体内递送载体的前景—— Cas9 RNP。然而，目前还没有一致的设计原则来改进新型合成聚合物的性能。 可以开发递送效率、组织特异性和安全性。聚合物结构太多了 彻底测试或通过临时实验，因此采用系统的方法进行聚合物设计、合成、 需要进行评估以确定有前途的候选人。该提案提出了一个发现框架 通过贝叶斯实验设计的功能聚合物。经过实验训练的机器学习模型 结果将作为实验的替代品，以虚拟地筛选大量的潜在潜力库 聚合物候选者。将通过贝叶斯优化算法选择候选聚合物 平衡对未知化学空间的探索和对已知有效传递 RNP 的结构的利用。 目标 1 将涉及 (a) 合成可生物降解的聚(酯脲胺) (PEUA) 的多样化库，(b) 使用模型荧光报告基因敲入/敲除试验评估其功能性能， 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/成分与这些多重性能之间的定量关系 指标。目标 2 将涉及 (a) 枚举可合成的 PEUA 的化学空间，以及 (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 开始，通过迭代轮次来指导从枚举空间中选择候选聚合物 实验。拟议工作的成果将是一个结合机器学习和 聚合物科学用于对广泛的生物材料设计空间进行公正的探索，并通过 开发用于基因编辑的有效且安全的 RNP 运载工具，其性能优于现有商业产品 聚合物车辆解决方案。