首页|Johns Hopkins University Reports Findings in Gene Therapy (Machine Learning Eluc idates Design Features of Plasmid Deoxyribonucleic Acid Lipid Nanoparticles for Cell Type-Preferential Transfection)
Johns Hopkins University Reports Findings in Gene Therapy (Machine Learning Eluc idates Design Features of Plasmid Deoxyribonucleic Acid Lipid Nanoparticles for Cell Type-Preferential Transfection)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Biotechnology - Gene Therapy is t he subject of a report. According to news reporting originating in Baltimore, Ma ryland, by NewsRx journalists, research stated, “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.” The news reporters obtained a quote from the research from Johns Hopkins Univers ity, “In this study, we employed a machine learning (ML) workflow, utilizing wel l-curated plasmid DNA LNP transfection data sets across six cell types, to extra ct compositional and chemical insights from HTS studies. Our approach achieved p rediction 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 tr ansfection efficiency. Notably, we identified consistent LNP composition paramet ers that enhance 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 m olar percentage of ionizable and helper lipids, N/P ratio, PEGylated lipid molar percentage of uncharged lipids, and hydrophobicity of the helper lipid.”
BaltimoreMarylandUnited StatesNort h and Central AmericaBioengineeringBiotechnologyCyborgsDrugs and Therapi esEmerging TechnologiesGene TherapyMachine LearningNanoparticlesNanote chnology
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