The Application of Light-Assisted Drying to the Thermal Stabilization of Nucleic Acid Nanoparticles.

Background: Cold-chain storage can be challenging and expensive for the transportation and storage of biologics, especially in low-resource settings. Nucleic acid nanoparticles (NANPs) are an example of new biological products that require refrigerated storage. Light-assisted drying (LAD) is a new processing technique to prepare biologics for anhydrous storage in a trehalose amorphous solid matrix at ambient temperatures. In this study, LAD was used to thermally stabilize four types of NANPs with differing structures and melting temperatures. Methods: Small volume samples (10 µL) containing NANPs were irradiated with a 1064 nm laser to speed the evaporation of water and create an amorphous trehalose preservation matrix. Samples were then stored for 1 month at 4°C or 20°C. A FLIR C655 mid-IR camera was used to record the temperature of samples during processing. The trehalose matrix was characterized using polarized light imaging (PLI) to determine if crystallization occurred during processing or storage. Damage to LAD-processed NANPs was assessed after processing and storage using gel electrophoresis. Results: Based on the end moisture content (EMC) as a function time and the thermal histories of samples, a LAD processing time of 30 min is sufficient to achieve low EMCs for the 10 µL samples used in this study. PLI demonstrates that the trehalose matrix was resistant to crystallization during processing and after storage at 4°C and at room temperature. The native-polyacrylamide gel electrophoresis results for DNA cubes, RNA cubes, and RNA rings indicate that the main structures of these NANPs were not damaged significantly after LAD processing and being stored at 4°C or at room temperature for 1 month. Conclusions: These preliminary studies indicate that LAD processing can stabilize NANPs for dry-state storage at room temperature, providing an alternative to refrigerated storage for these nanomedicine products.

Anhydrous Nucleic Acid Nanoparticles for Storage and Handling at Broad Range of Temperatures.

Recent advances in nanotechnology now allow for the methodical implementation of therapeutic nucleic acids (TNAs) into modular nucleic acid nanoparticles (NANPs) with tunable physicochemical properties which can match the desired biological effects, provide uniformity, and regulate the delivery of multiple TNAs for combinatorial therapy. Despite the potential of novel NANPs, the maintenance of their structural integrity during storage and shipping remains a vital issue that impedes their broader applications. Cold chain storage is required to maintain the potency of NANPs in the liquid phase, which greatly increases transportation costs. To promote long-term storage and retention of biological activities at higher temperatures (e.g., +50 °C), a panel of representative NANPs is first exposed to three different drying mechanisms-vacuum concentration (SpeedVac), lyophilization (Lyo), and light-assisted drying (LAD)-and then rehydrated and analyzed. While SpeedVac primarily operates using heat, Lyo avoids temperature increases by taking advantage of pressure reduction and LAD involves a near-infrared laser for uniform drying in the presence of trehalose. This work compares and defines refinements crucial in formulating an optimal strategy for producing stable, fully functional NANPs and presents a forward advancement in their development for clinical applications.

Light-assisted drying for anhydrous preservation of biological samples: optical characterization of the trehalose preservation matrix.

Protein-based drugs have been developed to treat a variety of conditions and assays use immobilized capture proteins for disease detection. Freeze-drying is currently the standard for the preservation of proteins, but this method is expensive and requires lengthy processing times. Anhydrous preservation in a trehalose amorphous solid matrix offers a promising alternative to freeze-drying. Light assisted drying (LAD) is a processing method to create an amorphous trehalose matrix. Proteins suspended in a trehalose solution are dehydrated using near-infrared laser light. The laser radiation accelerates drying and as water is removed the trehalose forms a protective matrix. In this work, LAD samples are characterized to determine the crystallization kinetics of the trehalose after LAD processing and the distribution of amorphous trehalose in the samples. These characteristics influence the long-term stability of the samples. Polarized light imaging revealed that LAD processed samples are stable against crystallization during low-humidity storage at room temperature. Scanning white light interferometry and Raman spectroscopy indicated that trehalose was present across samples in an amorphous form. In addition, differential scanning microcalorimetry was used to measure the thermodynamic characteristics of the protein lysozyme after LAD processing. These results demonstrate that LAD does not change the properties of this protein.

Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods.

Typical Bayesian methods for models with latent variables (or random effects) involve directly sampling the latent variables along with the model parameters. In high-level software code for model definitions (using, e.g., BUGS, JAGS, Stan), the likelihood is therefore specified as conditional on the latent variables. This can lead researchers to perform model comparisons via conditional likelihoods, where the latent variables are considered model parameters. In other settings, however, typical model comparisons involve marginal likelihoods where the latent variables are integrated out. This distinction is often overlooked despite the fact that it can have a large impact on the comparisons of interest. In this paper, we clarify and illustrate these issues, focusing on the comparison of conditional and marginal Deviance Information Criteria (DICs) and Watanabe-Akaike Information Criteria (WAICs) in psychometric modeling. The conditional/marginal distinction corresponds to whether the model should be predictive for the clusters that are in the data or for new clusters (where "clusters" typically correspond to higher-level units like people or schools). Correspondingly, we show that marginal WAIC corresponds to leave-one-cluster out cross-validation, whereas conditional WAIC corresponds to leave-one-unit out. These results lead to recommendations on the general application of the criteria to models with latent variables.