Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach

Lipid nanoparticles for RNA delivery: Self-assembling vs driven-assembling strategies

Among non-viral vectors, lipid nanovectors are considered the gold standard for the delivery of RNA therapeutics. The success of lipid nanoparticles for RNA delivery, with three products approved for human use, has stimulated further investigation into RNA therapeutics for different pathologies. This requires decoding the pathological intracellular processes and tailoring the delivery system to the target tissue and cells. The complexity of the lipid nanovectors morphology originates from the assembling of the lipidic components, which can be elicited by various methods able to drive the formation of nanoparticles with the desired organization. In other cases, pre-formed nanoparticles can be mixed with RNA to induce self-assembly and structural reorganization into RNA-loaded nanoparticles. In this review, the most relevant lipid nanovectors and their potentialities for RNA delivery are described on the basis of the assembling mechanism and of the particle architecture.

Advances in microscopy characterization techniques for lipid nanocarriers in drug delivery: a comprehensive review

This review paper provides an in-depth analysis of the significance of lipid nanocarriers in drug delivery and the crucial role of characterization techniques. It explores various types of lipid nanocarriers and their applications, emphasizing the importance of microscopy-based characterization methods such as light microscopy, confocal microscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic force microscopy (AFM). The paper also delves into sample preparation, quantitative analysis, challenges, and future directions in the field. The review concludes by underlining the pivotal role of microscopy-based characterization in advancing lipid nanocarrier research and drug delivery technologies.

Lipid Nanoparticle (LNP) Delivery Carrier-Assisted Targeted Controlled Release mRNA Vaccines in Tumor Immunity

In recent years, lipid nanoparticles (LNPs) have attracted extensive attention in tumor immunotherapy. Targeting immune cells in cancer therapy has become a strategy of great research interest. mRNA vaccines are a potential choice for tumor immunotherapy, due to their ability to directly encode antigen proteins and stimulate a strong immune response. However, the mode of delivery and lack of stability of mRNA are key issues limiting its application. LNPs are an excellent mRNA delivery carrier, and their structural stability and biocompatibility make them an effective means for delivering mRNA to specific targets. This study summarizes the research progress in LNP delivery carrier-assisted targeted controlled release mRNA vaccines in tumor immunity. The role of LNPs in improving mRNA stability, immunogenicity, and targeting is discussed. This review aims to systematically summarize the latest research progress in LNP delivery carrier-assisted targeted controlled release mRNA vaccines in tumor immunity to provide new ideas and strategies for tumor immunotherapy, as well as to provide more effective treatment plans for patients.

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.

Optimizing nanoliposomal formulations: Assessing factors affecting entrapment efficiency of curcumin-loaded liposomes using machine learning

BACKGROUND

Curcumin faces challenges in clinical applications due to its low bioavailability and poor water solubility. Liposomes have emerged as a promising delivery system for curcumin. This study aims to apply ensemble learning, a machine learning technique, to determine the most effective experimental conditions for formulating stable curcumin-loaded liposomes with a high entrapment efficiency (EE).

METHODS

Two liposomal formulations composed of HSPC:DPPG:Chol:DSPE-mPEG2000 and HSPC:Chol:DSPE-mPEG2000 at 55:5:35:5 and 55:40:5 M ratios, respectively, were prepared using the remote loading method, and their particle size and polydispersity index (PDI) were determined using Dynamic Light Scattering. To model the impact of five factors (molar ratios, particle size, sonication time, pH, and PDI) on EE%, the Least-squares boosting (LSBoost) ensemble learning algorithm was employed due to its capability to effectively handle nonlinear and non-stationary problems. The implementation and optimization of LSBoost were performed using MATLAB R2020a. The dataset was randomly split into training and testing sets, with 70% allocated for training. The mean absolute error (MAE) was used as the cost function to evaluate model performance. Additionally, a novel approach was employed to visualize the results using 3D plots, facilitating practical interpretation.

RESULTS

The optimal model exhibited an MAE of 3.61, indicating its robust predictive capability. The study identified several optimal conditions for achieving the highest EE value of 100%. However, to ensure both the highest EE value and a suitable particle size, it is recommended to set the following conditions: a molar ratio of 55:5:35:5, a PDI within the range of 0.09-0.13, a particle size of approximately 130 nm, a sonication time of 30 min, and a pH within the range of 7.2-8. It is worth mentioning that adjusting the molar ratio to 55:40:5 resulted in a maximum EE of 88.38%.

CONCLUSION

These findings underscore the high performance of ensemble learning in accurately predicting and optimizing the EE of the curcumin-loaded liposomes. The application of this technique provides valuable insights and holds promise for the development of efficient drug delivery systems.