Computationally-Inspired Design of Non-Viral Gene Delivery Vehicles for mRNA-Based Cystic Fibrosis Therapies

项目总结 囊性纤维化是一种使人衰弱和缩短生命的疾病，全世界有超过7万人受到影响， 预计每年约有1000例新病例被诊断出来。本病为常染色体隐性遗传 与CF跨膜电导调节因子(CFTR)突变相关的疾病。这些突变 损害跨细胞膜的离子运输。在肺上皮中，这种损伤导致 粘液的过度生产和堆积，导致呼吸道阻塞，使患者变得脆弱 持续的病原体感染和严重的呼吸衰竭。近年来，用小剂量中药治疗慢性萎缩性胃炎 分子疗法非常有效，然而，并不是所有的患者都可以商业化地使用这些疗法。 可用的治疗方法。最近，基因治疗的进展使治疗慢性萎缩性胃炎的新方法成为可能， 例如针对CF肺部病理的根本原因，甚至恢复或替换CFTR基因，用 对改善预后和生存结果的期望。然而，这些疗法通常规模都很大， 复杂的分子，如信使核糖核酸，需要专门的输送系统。病毒载体与脂质 纳米颗粒代表了基因传递的最新技术，然而这些方法受到以下限制 免疫原性，复杂的制造，最关键的是，穿越粘液层的能力有限 CF局部给药的三个主要障碍是：(I)需要克服粘膜内的卡压 屏障，(Ii)避免被肺巨噬细胞等免疫细胞识别和破坏，最后，(Iii) 有效的细胞内进入。我们建议利用计算优化和结构动力学 为克服这些障碍，为信使核糖核酸有效载荷设计基于聚合物的输送载体的建模。这 该项目将Nanite在高通量聚合物合成和机器方面的先进能力结合在一起 利用第一性原理计算生物学方法共同学习和设计糖肽 由东北大学的Srirupa Chakraborty博士首创。有效的运输工具的可用性 在所有适应症的几乎所有基因治疗模式中，都存在着公认的商业需求。 Nanite的方法是通过使用组合来覆盖尽可能广泛的设计空间来满足这一需求 计算设计、高通量综合和筛选以及基于机器学习的优化。 这一第一阶段项目的成功完成将使我们能够将这种方法扩展到CF。

PROJECT SUMMARY Cystic fibrosis (CF) is a debilitating and life-shortening disease affecting more than 70,000 people worldwide, with ~1,000 new cases expected to be diagnosed every year. This disease is an autosomal recessive genetic disorder associated with mutations in the CF transmembrane conductance regulator (CFTR). These mutations impairs the ionic transport across the cell membrane. In the pulmonary epithelium, this impairment results in an overproduction and accumulation of mucus, leading to airway obstructions and leaving patients vulnerable to persistent pathogenic infections and severe respiratory failure. In recent years, treatment of CF with small molecule therapies has been very impactful, however not all patients can be treated with these commercially available therapies. More recently, advances in gene therapy enable new approaches to the greatment of CF, such as target the underlying cause of CF lung pathology, and even restore or replace the CFTR gene, with expectations of improved prognosis and survival outcomes. However, these therapies are typically large, complex molecules, such as mRNA, which require specialized delivery systems. Viral vectors and lipid nanoparticles represent the current state of the art in gene delivery, however these methods are limited by immunogenicity, complex manufacturing, and most crucially, limited ability to traverse the mucus layerThe three principal obstacles of CF localized delivery are the need to (i) overcome entrapment within the mucosal barrier, (ii) avoiding recognition and disruption by immune cells such as lung macrophages, and finally, (iii) effective intracellular entry. We propose to leverage computational optimization and structure-dynamics modeling to design polymer-based delivery vehicles for mRNA payloads to overcome these obstacles. This project brings together Nanite’s advanced capabilities in high throughput polymer synthesis and machine learning together with design of glycopeptides using first-principles computational biology approaches pioneered by Dr. Srirupa Chakraborty at Northeastern University. The availability of effective delivery vehicles across virtually all modes of gene therapies across all indications is a well-recognized commercial need. Nanite’s approach is to address this need by covering the broadest possible design space using a combination of computational design, high throughput synthesis and screening, and machine learning-based optimization. Successful completion of this Phase I project will enable us to extend this approach to CF.