New Insights into Dose-Dependent Effects of Curcumin on ARPE-19 Cells

Machine Learning Elucidates Design Features of Plasmid Deoxyribonucleic Acid Lipid Nanoparticles for Cell Type-Preferential Transfection

To broaden the accessibility of cell and gene therapies, it is essential to develop and optimize nonviral, cell type-preferential gene carriers such as lipid nanoparticles (LNPs). While high-throughput screening (HTS) approaches have proven effective in accelerating LNP discovery, they are often costly, labor-intensive, and do not consistently yield actionable design rules that direct screening efforts toward the most relevant chemical and formulation parameters. In this study, we employed a machine learning (ML) workflow, utilizing well-curated plasmid DNA LNP transfection data sets across six cell types, to extract compositional and chemical insights from HTS studies. Our approach achieved prediction errors averaging between 5 and 10%, depending on the cell type. By applying SHapley Additive exPlanations to our ML models, we uncovered key composition-function relationships that govern cell type-preferential LNP transfection efficiency. Notably, we identified consistent LNP composition parameters that enhance in vitro transfection efficiency across diverse cell types, including a helper lipid molar percentage of charged lipids between 9 and 50% and the inclusion of cationic/zwitterionic helper lipids. Additionally, several parameters were found to modulate cell type-preferentiality, such as the total molar percentage of ionizable and helper lipids, N/P ratio, PEGylated lipid molar percentage of uncharged lipids, and hydrophobicity of the helper lipid. This study leverages HTS of compositionally diverse LNP libraries combined with ML analysis to elucidate the interactions between lipid components in LNP formulations, providing insights that contribute to the design of LNP compositions tailored for cell type-preferential transfection.

Small Extracellular Vesicles and Oxidative Pathophysiological Mechanisms in Retinal Degenerative Diseases

This review focuses on the role of small extracellular vesicles in the pathophysiological mechanisms of retinal degenerative diseases. Many of these mechanisms are related to or modulated by the oxidative burden of retinal cells. It has been recently demonstrated that cellular communication in the retina involves extracellular vesicles and that their rate of release and cargo features might be affected by the cellular environment, and in some instances, they might also be mediated by autophagy. The fate of these vesicles is diverse: they could end up in circulation being used as markers, or target neighbor cells modulating gene and protein expression, or eventually, in angiogenesis. Neovascularization in the retina promotes vision loss in diseases such as diabetic retinopathy and age-related macular degeneration. The importance of micro RNAs, either as small extracellular vesicles' cargo or free circulating, in the regulation of retinal angiogenesis is also discussed.

Machine Learning Elucidates Design Features of Plasmid DNA Lipid Nanoparticles for Cell Type-Preferential Transfection

For cell and gene therapies to become more broadly accessible, it is critical to develop and optimize non-viral cell type-preferential gene carriers such as lipid nanoparticles (LNPs). Despite the effectiveness of high throughput screening (HTS) approaches in expediting LNP discovery, they are often costly, labor-intensive, and often do not provide actionable LNP design rules that focus screening efforts on the most relevant chemical and formulation parameters. Here we employed a machine learning (ML) workflow using well-curated plasmid DNA LNP transfection datasets across six cell types to maximize chemical insights from HTS studies and has achieved predictions with 5-9% error on average depending on cell type. By applying Shapley additive explanations to our ML models, we unveiled composition-function relationships dictating cell type-preferential LNP transfection efficiency. Notably, we identified consistent LNP composition parameters that enhance in vitro transfection efficiency across diverse cell types, such as ionizable to helper lipid ratios near 1:1 or 10:1 and the incorporation of cationic/zwitterionic helper lipids. In addition, several parameters were found to modulate cell type-preferentiality, including the ionizable and helper lipid total molar percentage, N/P ratio, cholesterol to PEGylated lipid ratio, and the chemical identity of the helper lipid. This study leverages HTS of compositionally diverse LNP libraries and ML analysis to understand the interactions between lipid components in LNP formulations; and offers fundamental insights that contribute to the establishment of unique sets of LNP compositions tailored for cell type-preferential transfection.