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

项目摘要 囊性纤维化（CF）是一种使人衰弱和缩短寿命的疾病，影响全球超过70，000人， 预计每年将有约1,000例新病例被诊断出来。这种病是一种常染色体隐性遗传病 与CF跨膜传导调节因子（CFTR）突变相关的疾病。这些突变 削弱了穿过细胞膜的离子运输。在肺上皮中，这种损伤导致 粘液的过度产生和积累，导致气道阻塞，使患者易受伤害 持续性病原体感染和严重呼吸衰竭。近年来，治疗CF的小 分子疗法已经非常有效，然而并非所有患者都可以用这些商业化治疗。 可用的疗法。最近，基因治疗的进展使新的方法来增加CF， 例如靶向CF肺病理学的根本原因，甚至恢复或替换CFTR基因， 改善预后和生存结果的期望。然而，这些疗法通常很大， 复杂的分子，如mRNA，需要专门的传递系统。病毒载体和脂质 纳米颗粒代表了基因递送领域的当前状态，然而这些方法受到以下因素的限制： 免疫原性，复杂的制造，最关键的是，有限的能力，穿越粘液层。 CF局部递送的三个主要障碍是需要（i）克服粘膜内的截留 屏障，（ii）避免被免疫细胞如肺巨噬细胞识别和破坏，以及最后，（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 建模来设计用于mRNA有效载荷的基于聚合物的递送载体以克服这些障碍。这 该项目汇集了Nanite在高通量聚合物合成和机器方面的先进能力 使用第一原理计算生物学方法设计糖肽 由东北大学的Srirupa Chakraborty博士开创。提供有效的运载工具 在所有适应症的几乎所有基因治疗模式中的应用是公认的商业需求。 Nanite的方法是通过使用组合来覆盖尽可能广泛的设计空间， 计算设计，高通量合成和筛选，以及基于机器学习的优化。 第一阶段项目的成功完成将使我们能够将这种方法推广到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.