Developing a Computer Model to Deliver Gene Therapies for Cystic Fibrosis
ChE Assistant Professor Srirupa Chakraborty was awarded a $100K NIH SBIR award for “Computationally-Inspired Design of Non-Viral Gene Delivery Vehicles for mRNA-Based Cystic Fibrosis Therapies.” This grant is a valuable resource for small businesses seeking to advance their research and innovation efforts in the field of healthcare and life sciences, with the ultimate goal of improving public health and driving economic growth.
Abstract Source: NIH
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 treatment 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 layer. The 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.