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

用于基于 mRNA 的囊性纤维化治疗的非病毒基因传递载体的计算启发设计

基本信息

批准号：
10760605
负责人：
Shashi Murthy
金额：
$ 30万
依托单位：
NANITE INC.
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-10 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10760605
关键词：
Address Affect Binding Biotechnology Breathing Businesses Cell Line Cell membrane Cells Chemicals Complex Computational Biology Cystic Fibrosis Cystic Fibrosis Transmembrane Conductance Regulator Development Diagnosis Disease Drug Delivery Systems Electrolytes Encapsulated Environment Epithelial Cells Epithelium Foundations Gene Delivery Genetic Diseases Glycopeptides Human Immune Immune Evasion Immunologics Impairment In Vitro Infection Investigation Lectin Letters Libraries Life Liquid substance Lung Machine Learning Macrophage Mechanics Messenger RNA Methodology Methods Modality Modeling Mucous Membrane Mucous body substance Mutation Nucleic Acids Pathogenicity Patients Penetration Peptides Performance Persons Phase Polymers Polysaccharides Process Production Prognosis Pulmonary Cystic Fibrosis Pulmonary Pathology Regulator Genes Respiratory Failure Sodium Channel Specificity Structure System Therapeutic Agents Toxic effect Universities Viral Vector airway obstruction autosome bronchial epithelium chronic infection delivery vehicle design expectation functional group gene therapy imaging modality immunogenicity improved innovation lipid nanoparticle mRNA delivery manufacture mucus clearance non-viral gene delivery novel strategies novel therapeutics nucleic acid-based therapeutics patient population recessive genetic trait respiratory virus screening small molecule survival outcome uptake virtual

项目摘要

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.

项目总结