Rajith Singh Manan

Hydrogels that adhere to biological tissues and resist fibrosis are required to provide both optimal functionality and appropriate stiffness on diverse soft tissues to achieve therapeutic efficacy and biocompatibility. However, their performance is often limited by an intrinsic trade-off between functionality and stiffness. Through the incorporation of polymer brush coatings, we develop a modular hydrogel system to enable independent control of functionality and stiffness. By tailoring coating chemistry, coating thickness, and hydrogel network topology, we obtain consistent bioadhesion (~100 joules per square meter) and fibrosis suppression across the full stiffness range of soft tissues (1 kilopascal to 1 megapascal). Using this approach, we design a hydrogel that can maintain stable adhesion in vivo on a beating mouse heart and a hydrogel with no fibrotic capsule in immunocompetent mice over 40 days. This modular system offers a customizable approach for designing functional implants with tailored mechanical properties.

Lipid nanoparticles (LNPs) are the leading nonviral nucleic acid delivery technology, but LNP structure-function data remains fragmented and nonstandardized. Unlike protein engineering which is anchored by the centralized Protein Data Bank, the LNP field lacks a unified repository for systematic analysis. To address this, we develop Lipid Nanoparticle Database (LNPDB) ( https://lnpdb.molcube.com ), an integrated database and web tool that consolidates structural and functional data for 19,528 LNPs. LNPDB standardizes LNP featurization by encoding lipid composition, experimental methods, and functional results, and generates CHARMM force field files for constituent lipids to enable molecular dynamics simulations. LNPDB also supports future data contributions for continued growth. We examine the utility of LNPDB through two applications: advancing our deep learning model for predicting LNP delivery performance, and simulating bilayer dynamics to identify structural features – bilayer stability and critical packing parameter – that correlate with LNP delivery performance. Altogether, LNPDB provides the digital framework for LNP modeling and data-driven rational design.

Membranes that prevent cellular infiltration are essential components of immune-isolation devices. Stringent pore size control is critical to robustly exclude immune cells while preserving the efficient transport of nutrients and therapeutic molecules. Conventional membrane fabrication methods, however, struggle to balance these two properties. For instance, phase separation and fiber spinning generate highly tortuous pores that restrict diffusion, while random pore overlaps due to ion track etching result in oversized defects that compromise immune isolation. Here, we leverage scalable laser-writing tools to achieve spatially controlled open-through and dense pores with uniformly distributed submicrometer sizes for rapid transport and robust immune cell exclusion. Given the optical limits of laser aligners when fabricating submicrometer features, we tuned substrate reflectivity and exposure parameters to overcome these resolution limits. Self-standing and flexible membranes were achieved via thickness-tunable grid layers supporting membrane mechanical integrity. We report pore sizes as small as ca. 600 nm (below the resolution limits of the laser exposure systems) and over 20% open area at the exposure times of ∼3 min/cm². Compared with conventional methods, this approach minimizes pore tortuosity, narrows the pore distribution, and improves the open pore density. Enhanced pore control is highlighted by macrophage infiltration studies, which indicate that these membranes reliably exclude macrophages at nominal pore sizes greater than those previously achievable. This approach thus provides a scalable pathway toward next-generation immunoisolation membranes for enhanced implantable therapeutic devices.

Vascular inflammation regulates endothelial pathophenotypes, particularly in pulmonary arterial hypertension (PAH). Dysregulated lysosomal activity and cholesterol metabolism activate pathogenic inflammation, but their relevance to PAH is unclear. Nuclear receptor coactivator 7 ( NCOA7 ) deficiency in endothelium produced an oxysterol and bile acid signature through lysosomal dysregulation, promoting endothelial pathophenotypes. This oxysterol signature overlapped with a plasma metabolite signature associated with human PAH mortality. Mice deficient for endothelial Ncoa7 or exposed to an inflammatory bile acid developed worsened PAH. Genetic predisposition to NCOA7 deficiency was driven by single-nucleotide polymorphism rs11154337, which alters endothelial immunoactivation and is associated with human PAH mortality. An NCOA7-activating agent reversed endothelial immunoactivation and rodent PAH. Thus, we established a genetic and metabolic paradigm that links lysosomal biology and oxysterol processes to endothelial inflammation and PAH.

Ionizable lipids are a key component of lipid nanoparticles, the leading nonviral messenger RNA delivery technology. Here, to advance the identification of ionizable lipids beyond current methods, which rely on experimental screening and/or rational design, we introduce lipid optimization using neural networks, a deep-learning strategy for ionizable lipid design. We created a dataset of >9,000 lipid nanoparticle activity measurements and used it to train a directed message-passing neural network for prediction of nucleic acid delivery with diverse lipid structures. Lipid optimization using neural networks predicted RNA delivery in vitro and in vivo and extrapolated to structures divergent from the training set. We evaluated 1.6 million lipids in silico and identified two structures, FO-32 and FO-35, with local mRNA delivery to the mouse muscle and nasal mucosa. FO-32 matched the state of the art for nebulized mRNA delivery to the mouse lung, and both FO-32 and FO-35 efficiently delivered mRNA to ferret lungs. Overall, this work shows the utility of deep learning for improving nanoparticle delivery.

Lipid nanoparticles (LNPs) have great potential to enable inhaled delivery of mRNA to treat pulmonary diseases. However, this potential has been limited by the challenge of nebulizing the LNPs. Nebulization of LNPs can cause LNPs to aggregate and release encapsulated mRNA, limiting their delivery efficacy. To overcome this challenge, LNPs are developed whereby the PEG-lipid is fully replaced with a zwitterionic polymer (ZIP)-lipid conjugate to greatly enhance the nebulizer stability. LNPs formulated with ZIP-lipids (ZIP-LNPs) were stable to nebulization across a wide range of formulation parameters. The optimized ZIP-LNP formulation, containing reduced cholesterol content relative to traditional PEG-lipid LNPs, demonstrated improved inhaled mRNA delivery in both healthy and mucoobstructed mouse lungs. Repeat administration of the optimized ZIP-LNP formulation was well tolerated and did not result in pulmonary inflammation. This study demonstrates the potential of zwitterionic polymer-lipid conjugates for improving the performance of inhaled mRNA-LNP formulations.

Inhaled delivery of mRNA has the potential to treat a wide variety of diseases. However, nebulized mRNA lipid nanoparticles (LNPs) face several unique challenges including stability during nebulization and penetration through both cellular and extracellular barriers. Here we develop a combinatorial approach addressing these barriers. First, we observe that LNP formulations can be stabilized to resist nebulization-induced aggregation by altering the nebulization buffer to increase the LNP charge during nebulization, and by the addition of a branched polymeric excipient. Next, we synthesize a combinatorial library of ionizable, degradable lipids using reductive amination, and evaluate their delivery potential using fully differentiated air–liquid interface cultured primary lung epithelial cells. The final combination of ionizable lipid, charge-stabilized formulation and stability-enhancing excipient yields a significant improvement in lung mRNA delivery over current state-of-the-art LNPs and polymeric nanoparticles.

Developing safe and effective nanoparticles for the delivery of messenger RNA (mRNA) is slow and expensive, partly due to the lack of predictive power of in vitro screening methods and the low-throughput nature of in vivo screening. While DNA barcoding and batch analysis present methods for increasing in vivo screening throughput, they can also result in incomplete or misleading measures of efficacy. Here, we describe a high-throughput and accurate method for the screening of pooled nanoparticle formulations within the same animal. The method uses liquid chromatography with tandem mass spectrometry to detect peptide barcodes translated from mRNAs in nanoparticle-transfected cells. We show the method’s applicability by evaluating a library of over 400 nanoparticle formulations with 384 unique ionizable lipids using only nine mice to optimize the formulation of a biodegradable lipid nanoparticle for mRNA delivery to the liver. Barcoding lipid nanoparticles with peptide-encoding mRNAs may facilitate the rapid development of nanoparticles for mRNA delivery to specific cells and tissues.

The expanding applications of nonviral genomic medicines in the lung remain restricted by delivery challenges. Here, leveraging a high-throughput platform, we synthesize and screen a combinatorial library of biodegradable ionizable lipids to build inhalable delivery vehicles for messenger RNA and CRISPR–Cas9 gene editors. Lead lipid nanoparticles are amenable for repeated intratracheal dosing and could achieve efficient gene editing in lung epithelium, providing avenues for gene therapy of congenital lung diseases.