Discovery and characterization of single-domain antibodies for polymeric Ig receptor-mediated mucosal delivery of biologics.

Mucosal immunity is dominated by secretory IgA and IgM, although these are less favorable compared to IgG molecules for therapeutic development. Polymeric IgA and IgM are actively transported across the epithelial barrier via engagement of the polymeric Ig receptor (pIgR), but IgG molecules lack a lumen-targeted active transport mechanism, resulting in poor biodistribution of IgG therapeutics in mucosal tissues. In this work, we describe the discovery and characterization of single-domain antibodies (VHH) that engage pIgR and undergo transepithelial transport across the mucosal epithelium. The anti-pIgR VHH panel displayed a broad range of biophysical characteristics, epitope diversity, IgA competition profiles and transcytosis activity in cell and human primary lung tissue models. Making use of this diverse VHH panel, we studied the relationship between biophysical and functional properties of anti-pIgR binders targeting different domains and epitopes of pIgR. These VHH molecules will serve as excellent tools for studying pIgR-mediated transport of biologics and for delivering multispecific IgG antibodies into mucosal lumen, where they can target and neutralize mucosal antigens.

Mathematical Modeling: A Tool for Optimization of Lipid Nanoparticle-Mediated Delivery of siRNA.

Lipid nanoparticles (LNPs) have been used to successfully deliver small interfering RNAs (siRNAs) to target cells in both preclinical and clinical studies and currently are the leading systems for in vivo delivery. Here, we propose the use of an ordinary differential equation (ODE)-based model as a tool for optimizing LNP-mediated delivery of siRNAs. As a first step, we have used a combination of experimental and computational approaches to develop and validate a mathematical model that captures the critical features for efficient siRNA-LNP delivery in vitro. This model accurately predicts mRNA knockdown resulting from novel combinations of siRNAs and LNPs in vitro. As demonstrated, this model can be effectively used as a screening tool to select the most efficacious LNPs, which can then further be evaluated in vivo. The model serves as a starting point for the future development of next generation models capable of capturing the additional complexity of in vivo delivery.

Induced conformational change in human IL-4 upon binding of a signal-neutralizing DARPin.

The crystal structure of DARPin 44C12V5 that neutralizes IL-4 signaling has been determined alone and bound to human IL-4. A significant conformational change occurs in the IL-4 upon DARPin binding. The DARPin binds to the face of IL-4 formed by the A and C α-helices. The structure of the DARPin remains virtually unchanged. The conformational changes in IL-4 include a reorientation of the C-helix Trp91 side chain and repositioning of CD-loop residue Leu96. Both side chains move by >9 Å, becoming buried in the central hydrophobic region of the IL-4:DARPin interface. This hydrophobic region is surrounded by a ring of hydrophilic interactions comprised of hydrogen bonds and salt bridges and represents a classical "hotspot." The structures also reveal how the DARPin neutralizes IL-4 signaling. Comparing the IL-4:DARPin complex structure with the structures of IL-4 bound to its receptors (Hage et al., Cell 1999; 97, 271-281; La Porte et al., Cell 2008, 132, 259-272), it is found that the DARPin binds to the same IL-4 face that interacts with the junction of the D1 and D2 domains of the IL-4Rα receptors. Signaling is blocked since IL-4 cannot bind to this receptor, which it must do first before initiating a productive receptor complex with either the IL-13α1 or the γc receptor.

Analysis of lipid nanoparticles by Cryo-EM for characterizing siRNA delivery vehicles.

Lipid nanoparticles are self-assembling, dynamic structures commonly used as carriers of siRNA, DNA, and small molecular therapeutics. Quantitative analysis of particle characteristics such as morphological features can be very informative as biophysical properties are known to influence biological activity, biodistribution, and toxicity. However, accurate characterization of particle attributes and population distributions is difficult. Cryo-Electron Microscopy (Cryo-EM) is a leading characterization method and can reveal diversity in particle size, shape and lamellarity, however, this approach is traditionally used for qualitative review or low throughput image analysis due to inherent EM micrograph contrast characteristics and artifacts in the images which limit extraction of quantitative feature values. In this paper we describe the development of a semiautomatic image analysis framework to facilitate reliable image enhancement, object segmentation, and quantification of nanoparticle attributes in Cryo-EM micrographs. We apply this approach to characterize two formulations of siRNA-loaded lipid nanoparticles composed of cationic lipid, cholesterol, and poly(ethylene glycol)-lipid, where the formulations differ only by input component ratios. We found Cryo-EM image analysis provided reliable size and morphology information as well as the detection of smaller particle populations that were not detected by standard dynamic light scattering (DLS) analysis.