Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm

AI-Based solutions for current challenges in regenerative medicine

The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a significant opportunity that holds the promise of tackling critical challenges and improving therapeutic outcomes. This article examines the ways in which AI, including machine learning and data fusion techniques, can contribute to regenerative medicine, particularly in gene therapy, stem cell therapy, and tissue engineering. In gene therapy, AI tools can boost the accuracy and safety of treatments by analyzing extensive genomic datasets to target and modify genetic material in a precise manner. In cell therapy, AI improves the characterization and optimization of cell products like mesenchymal stem cells (MSCs) by predicting their function and potency. Additionally, AI enhances advanced microscopy techniques, enabling accurate, non-invasive and quantitative analyses of live cell cultures. AI enhances tissue engineering by optimizing biomaterial and scaffold designs, predicting interactions with tissues, and streamlining development. This leads to faster and more cost-effective innovations by decreasing trial and error. The convergence of AI and regenerative medicine holds great transformative potential, promising effective treatments and innovative therapeutic strategies. This review highlights the importance of interdisciplinary collaboration and the continued integration of AI-based technologies, such as data fusion methods, to overcome current challenges and advance regenerative medicine.

Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers

The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, and more, each tailored to specific therapeutic applications. Despite significant achievements, the clinical translation of nanocarriers is limited, primarily due to the low efficiency of drug delivery and an incomplete understanding of nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration and a deep understanding of the nano-bio interface. To enhance nanocarrier design, scientists employ both physics-based and data-driven models. Physics-based models provide detailed insights into chemical reactions and interactions at atomic and molecular scales, while data-driven models leverage machine learning to analyze large datasets and uncover hidden mechanisms. The integration of these models presents challenges such as harmonizing different modeling approaches and ensuring model validation and generalization across biological systems. However, this integration is crucial for developing effective and targeted nanocarrier systems. By integrating these approaches with enhanced data infrastructure, explainable AI, computational advances, and machine learning potentials, researchers can develop innovative nanomedicine solutions, ultimately improving therapeutic outcomes.

Development of fucoidan/polyethyleneimine based sorafenib-loaded self-assembled nanoparticles with machine learning and DoE-ANN implementation: Optimization, characterization, and in-vitro assessment for the anticancer drug delivery

This study aims to develop sorafenib-loaded self-assembled nanoparticles (SFB-SANPs) using the combined approach of artificial neural network and design of experiments (ANN-DoE) and to compare it with other machine learning (ML) models. The central composite design (CCD) and ML algorithms were used to screen the effects of concentrations of both the polymers (polyethyleneimine and fucoidan) on the outcome responses, i.e., particle size and entrapment efficiency with defined constraints. The prediction from different ML models (bootstrap forest, K-nearest neighbors, artificial neural network, generalized regression-lasso and support vector machines) were compared with ANN-DoE model. The ANN-DoE model showed better accuracy and predictability and outperformed all the other models. This depicted that the concept of using ANN and DoE combination approach provided the best, uncomplicated and cost-effective way to optimized the nanoformulations. The optimized formulation generated from the ANN-DoE combined model was further evaluated for characterization and anticancer activity. The optimized SFB-SANPs were prepared using the polyelectrolyte complexation method with Polyethyleneimine (PEI) as a cationic polymer and fucoidan (FCD) as an anionic. The SFB-SANPs were nanometric in size (280.4 ± 0.089 nm) and slightly anionic in nature (zeta potential = -6.03 ± 0.92 mV) with an encapsulation efficiency of 95.56 ± 0.30 %. The drug release from SFB-SANPs was controlled and sustained in the cancer microenvironment (pH 5.0). The SFB-SANPs were compatible with red blood cells (RBCs), and the % hemolysis was found to be <5.0 %. The anticancer activity of the SFB-SANPs exhibited an IC50 at 2.017 ± 0.516 μM against MDMB-231 cells, showing a significantly high inhibitory effect on breast cancer cell lines. Therefore, the nanocarriers developed using various ML tools inherit a huge promise in anticancer drug delivery.

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.

Designing nanotheranostics with machine learning

The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as 'nanotheranostics'. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano-bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

Advances in the design and delivery of RNA vaccines for infectious diseases

RNA medicines represent a paradigm shift in treatment and prevention of critical diseases of global significance, e.g., infectious diseases. The highly successful messenger RNA (mRNA) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were developed at record speed during the coronavirus disease 2019 pandemic. A consequence of this is exceptionally shortened vaccine development times, which in combination with adaptability makes the RNA vaccine technology highly attractive against infectious diseases and for pandemic preparedness. Here, we review state of the art in the design and delivery of RNA vaccines for infectious diseases based on different RNA modalities, including linear mRNA, self-amplifying RNA, trans-amplifying RNA, and circular RNA. We provide an overview of the clinical pipeline of RNA vaccines for infectious diseases, and present analytical procedures, which are paramount for characterizing quality attributes and guaranteeing their quality, and we discuss future perspectives for using RNA vaccines to combat pathogens beyond SARS-CoV-2.

Review of machine learning for lipid nanoparticle formulation and process development

Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes of vaccines, and alternate modes of intracellular delivery for therapeutic proteins. In the pharmaceutical industry, establishing a robust formulation and process to achieve target product performance is a critical component of drug development. Fundamental understanding of the processes for making LNPs and their interactions with biological systems have advanced considerably in the wake of the COVID-19 pandemic. Nevertheless, LNP formulation research remains largely empirical and resource intensive due to the multitude of input parameters and the complex physical phenomena that govern the processes of nanoparticle precipitation, self-assembly, structure evolution, and stability. Increasingly, artificial intelligence and machine learning (AI/ML) are being applied to improve the efficiency of research activities through in silico models and predictions, and to drive deeper fundamental understanding of experimental inputs to functional outputs. This review will identify current challenges and opportunities in the development of robust LNP formulations of nucleic acids, review studies that apply machine learning methods to experimental datasets, and provide discussion on associated data science challenges to facilitate collaboration between formulation and data scientists, aiming to accelerate the advancement of AI/ML applied to LNP formulation and process optimization.

Rational Design of Lipid Nanoparticles for Enhanced mRNA Vaccine Delivery via Machine Learning

Since the coronavirus pandemic, mRNA vaccines have revolutionized the field of vaccinology. Lipid nanoparticles (LNPs) are proposed to enhance mRNA delivery efficiency; however, their design is suboptimal. Here, a rational method for designing LNPs is explored, focusing on the ionizable lipid composition and structural optimization using machine learning (ML) techniques. A total of 213 LNPs are analyzed using random forest regression models trained with 314 features to predict the mRNA expression efficiency. The models, which predict mRNA expression levels post-administration of intradermal injection in mice, identify phenol as the dominant substructure affecting mRNA encapsulation and expression. The specific phospholipids used as components of the LNPs, as well as the N/P ratio and mass ratio, are found to affect the efficacy of mRNA delivery. Structural analysis highlights the impact of the carbon chain length on the encapsulation efficiency and LNP stability. This integrated approach offers a framework for designing advanced LNPs and has the potential to unlock the full potential of mRNA therapeutics.

Current landscape of mRNA technologies and delivery systems for new modality therapeutics

Realizing the immense clinical potential of mRNA-based drugs will require continued development of methods to safely deliver the bioactive agents with high efficiency and without triggering side effects. In this regard, lipid nanoparticles have been successfully utilized to improve mRNA delivery and protect the cargo from extracellular degradation. Encapsulation in lipid nanoparticles was an essential factor in the successful clinical application of mRNA vaccines, which conclusively demonstrated the technology's potential to yield approved medicines. In this review, we begin by describing current advances in mRNA modifications, design of novel lipids and development of lipid nanoparticle components for mRNA-based drugs. Then, we summarize key points pertaining to preclinical and clinical development of mRNA therapeutics. Finally, we cover topics related to targeted delivery systems, including endosomal escape and targeting of immune cells, tumors and organs for use with mRNA vaccines and new treatment modalities for human diseases.

Physicochemical and structural insights into lyophilized mRNA-LNP from lyoprotectant and buffer screenings

The surge in RNA therapeutics has revolutionized treatments for infectious diseases like COVID-19 and shows the potential to expand into other therapeutic areas. However, the typical requirement for ultra-cold storage of mRNA-LNP formulations poses significant logistical challenges for global distribution. Lyophilization serves as a potential strategy to extend mRNA-LNP stability while eliminating the need for ultra-cold supply chain logistics. Although recent advancements have demonstrated the promise of lyophilization, the choice of lyoprotectant is predominately focused on sucrose, and there remains a gap in comprehensive evaluation and comparison of lyoprotectants and buffers. Here, we aim to systematically investigate the impact of a diverse range of excipients including oligosaccharides, polymers, amino acids, and various buffers, on the quality and performance of lyophilized mRNA-LNPs. From the screening of 45 mRNA-LNP formulations under various lyoprotectant and buffer conditions for lyophilization, we identified previously unexplored formulation compositions, e.g., polyvinylpyrrolidone (PVP) in Tris or acetate buffers, as promising alternatives to the commonly used oligosaccharides to maintain the physicochemical stability of lyophilized mRNA-LNPs. Further, we delved into how physicochemical and structural properties influence the functionality of lyophilized mRNA-LNPs. Leveraging high-throughput small-angle X-ray scattering (SAXS) and cryogenic transmission electron microscopy (cryo-TEM), we showed that there is complex interplay between mRNA-LNP structural features and cellular translation efficacy. We also assessed innate immune responses of the screened mRNA-LNPs in human peripheral blood mononuclear cells (PBMCs), and showed minimal alterations of cytokine secretion profiles induced by lyophilized formulations. Our results provide valuable insights into the structure-activity relationship of lyophilized formulations of mRNA-LNP therapeutics, paving the way for rational design of these formulations. This work creates a foundation for a comprehensive understanding of mRNA-LNP properties and in vitro performance change resulting from lyophilization.

Lipid Nanoparticles for Nucleic Acid Delivery Beyond the Liver

Lipid nanoparticles (LNPs) are the most clinically advanced drug delivery system for nucleic acid therapeutics, exemplified by the success of the COVID-19 mRNA vaccines. However, their clinical use is currently limited to hepatic diseases and vaccines due to their tendency to accumulate in the liver upon intravenous administration. To fully leverage their potential, it is essential to understand and address their liver tropism, while also developing strategies to enhance delivery to tissues beyond the liver. Ensuring that these therapeutics reach their target cells while avoiding off-target cells is essential for both their efficacy and safety. There are three potential targeting strategies-passive, active, and endogenous-which can be used individually or in combination to target nonhepatic tissues. In this review, we delve into the recent advancements in LNP engineering for delivering nucleic acid beyond the liver.

Chemical Design Strategy of Ionizable Lipids for In Vivo mRNA Delivery

Lipid nanoparticles (LNPs) are the most clinically successful drug delivery systems that have accelerated the development of mRNA drugs and vaccines. Among various structural components of LNPs, more recent attention has been paid in ionizable lipids (ILs) that was supposed as the key component in determining the effectiveness of LNPs for in vivo mRNA delivery. ILs are typically comprised of three moieties including ionizable heads, linkers, and hydrophobic tails, which suggested that the combination of different functional groups in three moieties could produce ILs with diverse chemical structures and biological identities. In this concept article, we provide a summary of chemical design strategy for high-performing IL candidates and discuss their structure-activity relationships for shifting tissue-selective mRNA delivery. We also propose an outlook for the development of next-generation ILs, enabling the broader translation of mRNA formulated with LNPs.

A bibliometric insight into nanomaterials in vaccine: trends, collaborations, and future avenues

Background

The emergence of nanotechnology has injected new vigor into vaccine research. Nanovaccine research has witnessed exponential growth in recent years; yet, a comprehensive analysis of related publications has been notably absent.

Objective

This study utilizes bibliometric methodologies to reveal the evolution of themes and the distribution of nanovaccine research.

Methods

Using tools such as VOSviewer, CiteSpace, Scimago Graphica, Pajek, R-bibliometrix, and R packages for the bibliometric analysis and visualization of literature retrieved from the Web of Science database.

Results

Nanovaccine research commenced in 1981. The publication volume exponentially increased, notably in 2021. Leading contributors include the United States, the Chinese Academy of Sciences, the "Vaccine", and researcher Zhao Kai. Other significant contributors comprise China, the University of California, San Diego, Veronique Preat, the Journal of Controlled Release, and the National Natural Science Foundation of China. The USA functions as a central hub for international cooperation. Financial support plays a pivotal role in driving research advancements. Key themes in highly cited articles include vaccine carrier design, cancer vaccines, nanomaterial properties, and COVID-19 vaccines. Among 7402 keywords, the principal nanocarriers include Chitosan, virus-like particles, gold nanoparticles, PLGA, and lipid nanoparticles. Nanovaccine is primarily intended to address diseases including SARS-CoV-2, cancer, influenza, and HIV. Clustering analysis of co-citation networks identifies 9 primary clusters, vividly illustrating the evolution of research themes over different periods. Co-citation bursts indicate that cancer vaccines, COVID-19 vaccines, and mRNA vaccines are pivotal areas of focus for current and future research in nanovaccines. "candidate vaccines," "protein nanoparticle," "cationic lipids," "ionizable lipids," "machine learning," "long-term storage," "personalized cancer vaccines," "neoantigens," "outer membrane vesicles," "in situ nanovaccine," and "biomimetic nanotechnologies" stand out as research interest.

Conclusions

This analysis emphasizes the increasing scholarly interest in nanovaccine research and highlights pivotal recent research themes such as cancer and COVID-19 vaccines, with lipid nanoparticle-mRNA vaccines leading novel research directions.

Lipid nanoparticle (LNP) mediated mRNA delivery in cardiovascular diseases: Advances in genome editing and CAR T cell therapy

Cardiovascular diseases (CVDs) are the leading cause of global mortality among non-communicable diseases. Current cardiac regeneration treatments have limitations and may lead to adverse reactions. Hence, innovative technologies are needed to address these shortcomings. Messenger RNA (mRNA) emerges as a promising therapeutic agent due to its versatility in encoding therapeutic proteins and targeting "undruggable" conditions. It offers low toxicity, high transfection efficiency, and controlled protein production without genome insertion or mutagenesis risk. However, mRNA faces challenges such as immunogenicity, instability, and difficulty in cellular entry and endosomal escape, hindering its clinical application. To overcome these hurdles, lipid nanoparticles (LNPs), notably used in COVID-19 vaccines, have a great potential to deliver mRNA therapeutics for CVDs. This review highlights recent progress in mRNA-LNP therapies for CVDs, including Myocardial Infarction (MI), Heart Failure (HF), and hypercholesterolemia. In addition, LNP-mediated mRNA delivery for CAR T-cell therapy and CRISPR/Cas genome editing in CVDs and the related clinical trials are explored. To enhance the efficiency, safety, and clinical translation of mRNA-LNPs, advanced technologies like artificial intelligence (AGILE platform) in RNA structure design, and optimization of LNP formulation could be integrated. We conclude that the strategies to facilitate the extra-hepatic delivery and targeted organ tropism of mRNA-LNPs (SORT, ASSET, SMRT, and barcoded LNPs) hold great prospects to accelerate the development and translation of mRNA-LNPs in CVD treatment.

AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery

Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE's potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies.

A comprehensive comparison of DNA and RNA vaccines

Nucleic acid technology has revolutionized vaccine development, enabling rapid design and production of RNA and DNA vaccines for prevention and treatment of diseases. The successful deployment of mRNA and plasmid DNA vaccines against COVID-19 has further validated the technology. At present, mRNA platform is prevailing due to its higher efficacy, while DNA platform is undergoing rapid evolution because it possesses unique advantages that can potentially overcome the problems associated with the mRNA platform. To help understand the recent performances of the two vaccine platforms and recognize their clinical potentials in the future, this review compares the advantages and drawbacks of mRNA and DNA vaccines that are currently known in the literature, in terms of development timeline, financial cost, ease of distribution, efficacy, safety, and regulatory approval of products. Additionally, the review discusses the ongoing clinical trials, strategies for improvement, and alternative designs of RNA and DNA platforms for vaccination.

Oligonucleotide therapies for nonalcoholic steatohepatitis

Nonalcoholic steatohepatitis (NASH) represents a severe disease subtype of nonalcoholic fatty liver disease (NAFLD) that is thought to be highly associated with systemic metabolic abnormalities. It is characterized by a series of substantial liver damage, including hepatocellular steatosis, inflammation, and fibrosis. The end stage of NASH, in some cases, may result in cirrhosis and hepatocellular carcinoma (HCC). Nowadays a large number of investigations are actively under way to test various therapeutic strategies, including emerging oligonucleotide drugs (e.g., antisense oligonucleotide, small interfering RNA, microRNA, mimic/inhibitor RNA, and small activating RNA) that have shown high potential in treating this fatal liver disease. This article systematically reviews the pathogenesis of NASH/NAFLD, the promising druggable targets proven by current studies in chemical compounds or biological drug development, and the feasibility and limitations of oligonucleotide-based therapeutic approaches under clinical or pre-clinical studies.

Sucrose ester embedded lipid carrier for DNA delivery

Sucrose esters (SEs) have great potential in the field of nucleic acid delivery due to their unique physical and chemical properties and good biosafety. However, the mechanism of the effect of SEs structure on delivery efficiency has not been studied. The liposomes containing peptide lipids and SEs were constructed, and the effects of SEs on the interaction between the liposomes and DNA were studied. The addition of SEs affects the binding rate of liposomes to DNA, and the binding rate gradually decreases with the increase of SEs' carbon chain length. SEs also affect the binding site and affinity of liposomes to DNA, promoting the aggregation of lipids to form liposomes, where DNA wraps around or compresses inside the liposomes, allowing it to compress DNA without damaging the DNA structure. COL-6, which is composed of sucrose laurate, exhibits the optimal affinity for DNA, and SE promotes the formation of ordered membrane structure and enhances membrane stability, so that COL-6 exhibits a balance between rigidity and flexibility, and thus exhibits the highest delivery efficiency of DNA among these formulations. This work provides theoretical foundations for the application of SE in gene delivery and guides for the rational design of delivery systems.

Strategies for non-viral vectors targeting organs beyond the liver

In recent years, nanoparticles have evolved to a clinical modality to deliver diverse nucleic acids. Rising interest in nanomedicines comes from proven safety and efficacy profiles established by continuous efforts to optimize physicochemical properties and endosomal escape. However, despite their transformative impact on the pharmaceutical industry, the clinical use of non-viral nucleic acid delivery is limited to hepatic diseases and vaccines due to liver accumulation. Overcoming liver tropism of nanoparticles is vital to meet clinical needs in other organs. Understanding the anatomical structure and physiological features of various organs would help to identify potential strategies for fine-tuning nanoparticle characteristics. In this Review, we discuss the source of liver tropism of non-viral vectors, present a brief overview of biological structure, processes and barriers in select organs, highlight approaches available to reach non-liver targets, and discuss techniques to accelerate the discovery of non-hepatic therapies.

Data-balanced transformer for accelerated ionizable lipid nanoparticles screening in mRNA delivery

Despite the widespread use of ionizable lipid nanoparticles (LNPs) in clinical applications for messenger RNA (mRNA) delivery, the mRNA drug delivery system faces an efficient challenge in the screening of LNPs. Traditional screening methods often require a substantial amount of experimental time and incur high research and development costs. To accelerate the early development stage of LNPs, we propose TransLNP, a transformer-based transfection prediction model designed to aid in the selection of LNPs for mRNA drug delivery systems. TransLNP uses two types of molecular information to perceive the relationship between structure and transfection efficiency: coarse-grained atomic sequence information and fine-grained atomic spatial relationship information. Due to the scarcity of existing LNPs experimental data, we find that pretraining the molecular model is crucial for better understanding the task of predicting LNPs properties, which is achieved through reconstructing atomic 3D coordinates and masking atom predictions. In addition, the issue of data imbalance is particularly prominent in the real-world exploration of LNPs. We introduce the BalMol block to solve this problem by smoothing the distribution of labels and molecular features. Our approach outperforms state-of-the-art works in transfection property prediction under both random and scaffold data splitting. Additionally, we establish a relationship between molecular structural similarity and transfection differences, selecting 4267 pairs of molecular transfection cliffs, which are pairs of molecules that exhibit high structural similarity but significant differences in transfection efficiency, thereby revealing the primary source of prediction errors. The code, model and data are made publicly available at https://github.com/wklix/TransLNP.

Computational design of mRNA vaccines

mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines' target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.

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.

Effect of lipid composition on RNA-Lipid nanoparticle properties and their sensitivity to thin-film freezing and drying

A library of 16 lipid nanoparticle (LNP) formulations with orthogonally varying lipid molar ratios was designed and synthesized, using polyadenylic acid [poly(A)] as a model for mRNA, to explore the effect of lipid composition in LNPs on (i) the initial size of the resultant LNPs and encapsulation efficiency of RNA and (ii) the sensitivity of the LNPs to various conditions including cold storage, freezing (slow vs. rapid) and thawing, and drying. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify the optimal lipid molar ratios and interactions that favorably affect the physical properties of the LNPs and enhance their stability in various stress conditions. LNPs exhibited distinct responses under each stress condition, highlighting the effect of lipid molar ratios and lipid interactions on the LNP physical properties and stability. It was then demonstrated that it is feasible to use thin-film freeze-drying to convert poly(A)-LNPs from liquid dispersions to dry powders while maintaining the integrity of the LNPs. Importantly, the residual moisture content in LNP dry powders significantly affected the LNP integrity.Residual moisture content of ≤ 0.5% or > 3-3.5% w/w negatively affected the LNP size and/or RNA encapsulation efficiency, depending on the LNP composition. Finally, it was shown that the thin-film freeze-dried LNP powders have desirable aerosol properties for potential pulmonary delivery. It was concluded that Design of Experiments can be applied to identify mRNA-LNP formulations with the desired physical properties and stability profiles. Additionally, optimizing the residual moisture content in mRNA-LNP dry powders during (thin-film) freeze-drying is crucial to maintain the physical properties of the LNPs.

On the interactions between RNA and titrateable lipid layers: implications for RNA delivery with lipid nanoparticles

Characterising the interaction between cationic ionisable lipids (CIL) and nucleic acids (NAs) is key to understanding the process of RNA lipid nanoparticle (LNP) formation and release of NAs from LNPs. Here, we have used different surface techniques to reveal the effect of pH and NA type on the interaction with a model system of DOPC and the CIL DLin-MC3-DMA (MC3). At only 5% MC3, differences in the structure and dynamics of the lipid layer were observed. Both pH and %MC3 were shown to affect the absorption behaviour of erythropoietin mRNA, polyadenylic acid (polyA) and polyuridylic acid (polyU). The adsorbed amount of all studied NAs was found to increase with decreasing pH and increasing %MC3 but with different effects on the lipid layer, which could be linked to the NA secondary structure. For polyA at pH 6, adsorption to the surface of the layer was observed, whereas for other conditions and NAs, penetration of the NA into the layer resulted in the formation of a multilayer structure. By comparison to simulations excluding the secondary structure, differences in adsorption behaviours between polyA and polyU could be observed, indicating that the NA's secondary structure also affected the MC3-NA interactions.

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.

A preparation method for mRNA-LNPs with improved properties

The properties of mRNA lipid nanoparticles (mRNA-LNPs), including size, empty particles, morphology, storage stability, and transfection potency, are critically dependent on the preparation methods. Here, a Two-step tangential-flow filtration (TFF) method was successfully employed to improve the properties of mRNA-LNPs during the preparation process. This method involves an additional ethanol removal step prior to the particle fusion process. Notably, this innovative approach has yielded mRNA-LNPs with larger particles, a reduced proportion of empty LNPs, optimized storage stability (at least 6 months at 2-8 °C), improved in vitro transfection efficiency, and minimized distribution in the heart and blood in vivo. In summary, this study represents the implementation of the innovative Two-step TFF method in the preparation of mRNA-LNPs. Our findings indicate substantial enhancements in the properties of our mRNA-LNPs, specifically with regard to the percentage of empty LNPs, stability, transfection efficiency, and in vivo distribution. These improvements have the potential to optimize their industrial applicability and expand their clinical use.

The applications of machine learning to predict the forming of chemically stable amorphous solid dispersions prepared by hot-melt extrusion

Amorphous solid dispersion (ASD) is one of the most important strategies to improve the solubility and dissolution rate of poorly water-soluble drugs. As a widely used technique to prepare ASDs, hot-melt extrusion (HME) provides various benefits, including a solvent-free process, continuous manufacturing, and efficient mixing compared to solvent-based methods, such as spray drying. Energy input, consisting of thermal and specific mechanical energy, should be carefully controlled during the HME process to prevent chemical degradation and residual crystallinity. However, a conventional ASD development process uses a trial-and-error approach, which is laborious and time-consuming. In this study, we have successfully built multiple machine learning (ML) models to predict the amorphization of crystalline drug formulations and the chemical stability of subsequent ASDs prepared by the HME process. We utilized 760 formulations containing 49 active pharmaceutical ingredients (APIs) and multiple types of excipients. By evaluating the built ML models, we found that ECFP-LightGBM was the best model to predict amorphization with an accuracy of 92.8%. Furthermore, ECFP-XGBoost was the best in estimating chemical stability with an accuracy of 96.0%. In addition, the feature importance analyses based on SHapley Additive exPlanations (SHAP) and information gain (IG) revealed that several processing parameters and material attributes (i.e., drug loading, polymer ratio, drug's Extended-connectivity fingerprints (ECFP) fingerprints, and polymer's properties) are critical for achieving accurate predictions for the selected models. Moreover, important API's substructures related to amorphization and chemical stability were determined, and the results are largely consistent with the literature. In conclusion, we established the ML models to predict formation of chemically stable ASDs and identify the critical attributes during HME processing. Importantly, the developed ML methodology has the potential to facilitate the product development of ASDs manufactured by HME with a much reduced human workload.

Targeting cancer with mRNA-lipid nanoparticles: key considerations and future prospects

Harnessing mRNA-lipid nanoparticles (LNPs) to treat patients with cancer has been an ongoing research area that started before these versatile nanoparticles were successfully used as COVID-19 vaccines. Currently, efforts are underway to harness this platform for oncology therapeutics, mainly focusing on cancer vaccines targeting multiple neoantigens or direct intratumoural injections of mRNA-LNPs encoding pro-inflammatory cytokines. In this Review, we describe the opportunities of using mRNA-LNPs in oncology applications and discuss the challenges for successfully translating the findings of preclinical studies of these nanoparticles into the clinic. We critically appraise the potential of various mRNA-LNP targeting and delivery strategies, considering physiological, technological and manufacturing challenges. We explore these approaches in the context of the potential clinical applications best suited to each approach and highlight the obstacles that currently need to be addressed to achieve these applications. Finally, we provide insights from preclinical and clinical studies that are leading to this powerful platform being considered the next frontier in oncology treatment.

Revolutionizing drug formulation development: The increasing impact of machine learning

Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.

RNA Nanomedicine: Delivery Strategies and Applications

Delivery of RNA using nanomaterials has emerged as a new modality to expand therapeutic applications in biomedical research. However, the delivery of RNA presents unique challenges due to its susceptibility to degradation and the requirement for efficient intracellular delivery. The integration of nanotechnologies with RNA delivery has addressed many of these challenges. In this review, we discuss different strategies employed in the design and development of nanomaterials for RNA delivery. We also highlight recent advances in the pharmaceutical applications of RNA delivered via nanomaterials. Various nanomaterials, such as lipids, polymers, peptides, nucleic acids, and inorganic nanomaterials, have been utilized for delivering functional RNAs, including messenger RNA (mRNA), small interfering RNA, single guide RNA, and microRNA. Furthermore, the utilization of nanomaterials has expanded the applications of functional RNA as active pharmaceutical ingredients. For instance, the delivery of antigen-encoding mRNA using nanomaterials enables the transient expression of vaccine antigens, leading to immunogenicity and prevention against infectious diseases. Additionally, nanomaterial-mediated RNA delivery has been investigated for engineering cells to express exogenous functional proteins. Nanomaterials have also been employed for co-delivering single guide RNA and mRNA to facilitate gene editing of genetic diseases. Apart from the progress made in RNA medicine, we discuss the current challenges and future directions in this field.

Lipid carriers for mRNA delivery

Messenger RNA (mRNA) is the template for protein biosynthesis and is emerging as an essential active molecule to combat various diseases, including viral infection and cancer. Especially, mRNA-based vaccines, as a new type of vaccine, have played a leading role in fighting against the current global pandemic of COVID-19. However, the inherent drawbacks, including large size, negative charge, and instability, hinder its use as a therapeutic agent. Lipid carriers are distinguishable and promising vehicles for mRNA delivery, owning the capacity to encapsulate and deliver negatively charged drugs to the targeted tissues and release cargoes at the desired time. Here, we first summarized the structure and properties of different lipid carriers, such as liposomes, liposome-like nanoparticles, solid lipid nanoparticles, lipid-polymer hybrid nanoparticles, nanoemulsions, exosomes and lipoprotein particles, and their applications in delivering mRNA. Then, the development of lipid-based formulations as vaccine delivery systems was discussed and highlighted. Recent advancements in the mRNA vaccine of COVID-19 were emphasized. Finally, we described our future vision and perspectives in this field.

Engineering nanoparticle toolkits for mRNA delivery

The concept of using mRNA to produce its own medicine in situ in the body makes it an ideal drug candidate, holding great potential to revolutionize the way we approach medicine. The unique characteristics of mRNA, as well as its customizable biomedical functions, call for the rational design of delivery systems to protect and transport mRNA molecules. In this review, a nanoparticle toolkit is presented for the development of mRNA-based therapeutics from a drug delivery perspective. Nano-delivery systems derived from either natural systems or chemical synthesis, in the nature of organic or inorganic materials, are summarised. Delivery strategies in controlling the tissue targeting and mRNA release, as well as the role of nanoparticles in building and boosting the activity of mRNA drugs, have also been introduced. In the end, our insights into the clinical and translational development of mRNA nano-drugs are presented.

Lipid nanoparticle-based mRNA delivery systems for cancer immunotherapy

Cancer immunotherapy, which harnesses the power of the immune system, has shown immense promise in the fight against malignancies. Messenger RNA (mRNA) stands as a versatile instrument in this context, with its capacity to encode tumor-associated antigens (TAAs), immune cell receptors, cytokines, and antibodies. Nevertheless, the inherent structural instability of mRNA requires the development of effective delivery systems. Lipid nanoparticles (LNPs) have emerged as significant candidates for mRNA delivery in cancer immunotherapy, providing both protection to the mRNA and enhanced intracellular delivery efficiency. In this review, we offer a comprehensive summary of the recent advancements in LNP-based mRNA delivery systems, with a focus on strategies for optimizing the design and delivery of mRNA-encoded therapeutics in cancer treatment. Furthermore, we delve into the challenges encountered in this field and contemplate future perspectives, aiming to improve the safety and efficacy of LNP-based mRNA cancer immunotherapies.

Advanced subunit vaccine delivery technologies: From vaccine cascade obstacles to design strategies

Designing and manufacturing safe and effective vaccines is a crucial challenge for human health worldwide. Research on adjuvant-based subunit vaccines is increasingly being explored to meet clinical needs. Nevertheless, the adaptive immune responses of subunit vaccines are still unfavorable, which may partially be attributed to the immune cascade obstacles and unsatisfactory vaccine design. An extended understanding of the crosstalk between vaccine delivery strategies and immunological mechanisms could provide scientific insight to optimize antigen delivery and improve vaccination efficacy. In this review, we summarized the advanced subunit vaccine delivery technologies from the perspective of vaccine cascade obstacles after administration. The engineered subunit vaccines with lymph node and specific cell targeting ability, antigen cross-presentation, T cell activation properties, and tailorable antigen release patterns may achieve effective immune protection with high precision, efficiency, and stability. We hope this review can provide rational design principles and inspire the exploitation of future subunit vaccines.

The Effect of Ethanol on Lipid Nanoparticle Stabilization from a Molecular Dynamics Simulation Perspective

Lipid nanoparticles (LNPs) have emerged as a promising delivery system, particularly for genetic therapies and vaccines. LNP formation requires a specific mixture of nucleic acid in a buffered solution and lipid components in ethanol. Ethanol acts as a lipid solvent, aiding the formation of the nanoparticle's core, but its presence can also affect LNP stability. In this study, we used molecular dynamics (MD) simulations to investigate the physicochemical effect of ethanol on LNPs and gain a dynamic understanding of its impact on the overall structure and stability of LNPs. Our results demonstrate that ethanol destabilizes LNP structure over time, indicated by increased root mean square deviation (RMSD) values. Changes in the solvent-accessible surface area (SASA), electron density, and radial distribution function (RDF) also suggest that ethanol affects LNP stability. Furthermore, our H-bond profile analysis shows that ethanol penetrates the LNP earlier than water. These findings emphasize the importance of immediate ethanol removal in lipid-based systems during LNP production to ensure stability.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

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

To develop a combinatorial artificial-neural-network design-of-experiment (ANN-DOE) model, the effect of ionizable lipid, an ionizable lipid-to-cholesterol ratio, N/P ratio, flow rate ratio (FRR), and total flow rate (TFR) on the outcome responses of mRNA-LNP vaccine were evaluated using a definitive screening design (DSD) and machine learning (ML) algorithms. Particle size (PS), PDI, zeta potential (ZP), and encapsulation efficiency (EE) of mRNA-LNP were optimized within a defined constraint (PS 40-100 nm, PDI≤0.30, ZP≥(±)0.30 mV, EE≥70%), fed to ML algorithms (XGBoost, bootstrap forest, support vector machines, k-nearest neighbors, generalized regression-Lasso, ANN) and prediction was compared to ANN-DOE model. Increased FRR decreased the PS and increased ZP, while increased TFR increased PDI and ZP. Similarly, DOTAP and DOTMA produced higher ZP and EE. Particularly, a cationic ionizable lipid with an N/P ratio ≥6 provided a higher EE. ANN showed better predictive ability (R²=0.7269-0.9946), while XGBoost demonstrated better RASE (0.2833-2.9817). The ANN-DOE model outperformed both optimized ML models by R²=1.21% and RASE=43.51% (PS prediction), R²=0.23% and RASE=3.47% (PDI prediction), R²=5.73% and RASE=27.95% (ZP prediction), and R²=0.87% and RASE=36.95% (EE prediction), respectively, which demonstrated that ANN-DOE model was superior in predicting the bioprocess compared to independent models.

Corosolic acid-modified lipid nanoparticles as delivery carriers for DNA vaccines against avian influenza

Cholesterol (CHOL) is essential for developing lipid nanoparticles (LNPs) for gene delivery because it enhances membrane fusion and improves the delivery efficiency of gene cargos. An attractive pDNA carrier, corosolic acid (CA)-modified lipid nanoparticles (CLNPs), was developed by replacing CHOL in LNPs to deliver pDNA at various ratios of nitrogen groups to phosphate groups (N/P). The resultant CLNPs with a higher CHOL/CA ratio exhibited similar mean particle size, zeta potential, and encapsulation efficiency to those of LNPs. In comparison with LNPs, CLNPs (CHOL:CA ratio = 2:1) achieved increased cellular uptake and enhanced transfection efficacy while maintaining low cytotoxicity. In vivo results from chicken experiments demonstrated that CLNPs encapsulating DNA vaccines against avian influenza at a N/P ratio of 3 could elicit similar-level humoral and cellular immune responses compared with those of LNPs at a higher N/P ratio, thereby suggesting the induction of desirable immune effects using less ionizable lipids. Our study provides a reference for further research on the application of CA in LNPs for gene delivery, and the development of novel delivery systems for DNA vaccines against avian influenza.

Machine learning in accelerating microsphere formulation development

Microspheres have gained much attention from pharmaceutical and medical industry due to the excellent biodegradable and long controlled-release characteristics. However, the drug release behavior of microspheres is influenced by complicated formulation and manufacturing factors. The traditional formulation development of microspheres is intractable and inefficient by the experimentally trial-and-error methods. This research aims to build a prediction model to accelerate microspheres product development for small-molecule drugs by machine learning (ML) techniques. Two hundred eighty-six microsphere formulations with small-molecule drugs were collected from the publications and pharmaceutical company, including the dissolution temperature at both 37 ℃ and 45 ℃. After the comparison of fourteen ML approaches, the consensus model achieved accurate predictions for the validation set at 37 ℃ and 45 ℃ (R² = 0.880 vs. R² = 0.958), indicating the good performance to predict the in vitro drug release profiles at both 37 ℃ and 45 ℃. Meanwhile, the models revealed the feature importance of formulations, which offered meaningful insights to the microspheres development. Experiments of microsphere formulations further validated the accuracy of the consensus model. Furthermore, molecular dynamics (MD) simulation provided a microscopic view of the preparation process of microspheres. In conclusion, the prediction model of microsphere formulations for small-molecule drugs was successfully built with high accuracy, which is able to accelerate microspheres product development and promote the quality control of microspheres for the pharmaceutical industry.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

INTRODUCTION

Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

AREAS COVERED

the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

EXPERT OPINION

The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

Dendritic cell vaccines improve the glioma microenvironment: Influence, challenges, and future directions

INTRODUCTION

Gliomas, especially the glioblastomas, are one of the most aggressive intracranial tumors with poor prognosis. This might be explained by the heterogeneity of tumor cells and the inhibitory immunological microenvironment. Dendritic cells (DCs), as the most potent in vivo functional antigen-presenting cells, link innate immunity with adaptive immunity. However, their function is suppressed in gliomas. Therefore, overcoming the dysfunction of DCs in the TME might be critical to treat gliomas.

METHOD

In this paper we proposed the specificity of the glioma microenvironment, analyzed the pathways leading to the dysfunction of DCs in tumor microenvironment of patients with glioma, summarized influence of DC-based immunotherapy on the tumor microenvironment and proposed new development directions and possible challenges of DC vaccines.

RESULT

DC vaccines can improve the immunosuppressive microenvironment of glioma patients. It will bring good treatment prospects to patients. We also proposed new development directions and possible challenges of DC vaccines, thus providing an integrated understanding of efficacy on DC vaccines for glioma treatment.

Trends and patterns in cancer nanotechnology research: A survey of NCI's caNanoLab and nanotechnology characterization laboratory

Cancer nanotechnologies possess immense potential as therapeutic and diagnostic treatment modalities and have undergone significant and rapid advancement in recent years. With this emergence, the complexities of data standards in the field are on the rise. Data sharing and reanalysis is essential to more fully utilize this complex, interdisciplinary information to answer research questions, promote the technologies, optimize use of funding, and maximize the return on scientific investments. In order to support this, various data-sharing portals and repositories have been developed which not only provide searchable nanomaterial characterization data, but also provide access to standardized protocols for synthesis and characterization of nanomaterials as well as cutting-edge publications. The National Cancer Institute's (NCI) caNanoLab is a dedicated repository for all aspects pertaining to cancer-related nanotechnology data. The searchable database provides a unique opportunity for data mining and the use of artificial intelligence and machine learning, which aims to be an essential arm of future research studies, potentially speeding the design and optimization of next-generation therapies. It also provides an opportunity to track the latest trends and patterns in nanomedicine research. This manuscript provides the first look at such trends extracted from caNanoLab and compares these to similar metrics from the NCI's Nanotechnology Characterization Laboratory, a laboratory providing preclinical characterization of cancer nanotechnologies to researchers around the globe. Together, these analyses provide insight into the emerging interests of the research community and rise of promising nanoparticle technologies.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Sodium alginate coating simultaneously increases the biosafety and immunotherapeutic activity of the cationic mRNA nanovaccine

The extraordinary advantages associated with mRNA vaccines, including their high efficiency, relatively low severity of side effects, and ease of manufacture, have enabled them to be a promising immunotherapy approach against various infectious diseases and cancers. Nevertheless, most mRNA delivery carriers have many disadvantages, such as high toxicity, poor biocompatibility, and low efficiency in vivo, which have hindered the widespread use of mRNA vaccines. To further characterize and solve these problems and develop a new type of safe and efficient mRNA delivery carrier, a negatively charged SA@DOTAP-mRNA nanovaccine was prepared in this study by coating DOTAP-mRNA with the natural anionic polymer sodium alginate (SA). Intriguingly, the transfection efficiency of SA@DOTAP-mRNA was significantly higher than that of DOTAP-mRNA, which was not due to the increase in cellular uptake but was associated with changes in the endocytosis pathway and the strong lysosome escape ability of SA@DOTAP-mRNA. In addition, we found that SA significantly increased the expression of LUC-mRNA in mice and achieved certain spleen targeting. Finally, we confirmed that SA@DOTAP-mRNA had a stronger antigen-presenting ability in E. G7-OVA tumor-bearing mice, dramatically inducing the proliferation of OVA-specific CLTs and ameliorating the antitumor effect. Therefore, we firmly believe that the coating strategy applied to cationic liposome/mRNA complexes is of potential research value in the field of mRNA delivery and has promising clinical application prospects.