Machine-learning-assisted single-vessel analysis of nanoparticle permeability in tumour vasculatures

Three-Dimensional Label-Free Observing of the Self-Assembled Nanoparticles inside a Single Cell at Nanoscale Resolution

Understanding the intracellular behavior of nanoparticles (NPs) plays a key role in optimizing the self-assembly performance of nanomedicine. However, conducting the 3D, label-free, quantitative observation of self-assembled NPs within intact single cells remains a substantial challenge in complicated intracellular environments. Here, we propose a deep learning combined synchrotron radiation hard X-ray nanotomography approach to visualize the self-assembled ultrasmall iron oxide (USIO) NPs in a single cell. The method allows us to explore comprehensive information on NPs, such as their distribution, morphology, location, and interaction with cell organelles, and provides quantitative analysis of the heterogeneous size and morphologies of USIO NPs under diverse conditions. This label-free, in situ method provides a tool for precise characterization of intracellular self-assembled NPs to improve the evaluation and design of a bioresponsive nanomedicine.

Intravitreal therapeutic nanoparticles for age-related macular degeneration: Design principles, progress and opportunities

Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly. The current standard treatment for AMD involves frequent intravitreal administrations of therapeutic agents. While effective, this approach presents challenges, including patient discomfort, inconvenience, and the risk of adverse complications. Nanoparticle-based intravitreal drug delivery platforms offer a promising solution to overcome these limitations. These platforms are engineered to target the retina specifically and control drug release, which enhances drug retention, improves drug concentration and bioavailability at the retinal site, and reduces the frequency of injections. This review aims to uncover the design principles guiding the development of highly effective nanoparticle-based intravitreal drug delivery platforms for AMD treatment. By gaining a deeper understanding of the physiology of ocular barriers and the physicochemical properties of nanoparticles, we establish a basis for designing intravitreal nanoparticles to optimize drug delivery and drug retention in the retina. Furthermore, we review recent nanoparticle-based intravitreal therapeutic strategies to highlight their potential in improving AMD treatment efficiency. Lastly, we address the challenges and opportunities in this field, providing insights into the future of nanoparticle-based drug delivery to improve therapeutic outcomes for AMD patients.

Does the surface charge of the nanoparticles drive nanoparticle-cell membrane interactions?

Classical Coulombic interaction, characterized by electrostatic interactions mediated through surface charges, is often regarded as the primary determinant in nanoparticles' (NPs) cellular association and internalization. However, the intricate physicochemical properties of particle surfaces, biomolecular coronas, and cell surfaces defy this oversimplified perspective. Moreover, the nanometrological techniques employed to characterize NPs in complex physiological fluids often exhibit limited accuracy and reproducibility. A more comprehensive understanding of nanoparticle-cell membrane interactions, extending beyond attractive forces between oppositely charged surfaces, necessitates the establishment of databases through rigorous physical, chemical, and biological characterization supported by nanoscale analytics. Additionally, computational approaches, such as in silico modeling and machine learning, play a crucial role in unraveling the complexities of these interactions.

Self-assembly of peptides in living cells for disease theranostics

The past few decades have witnessed substantial progress in biomedical materials for addressing health concerns and improving disease therapeutic and diagnostic efficacy. Conventional biomedical materials are typically created through an ex vivo approach and are usually utilized under physiological environments via transfer from preparative media. This transfer potentially gives rise to challenges for the efficient preservation of the bioactivity and implementation of theranostic goals on site. To overcome these issues, the in situ synthesis of biomedical materials on site has attracted great attention in the past few years. Peptides, which exhibit remarkable biocompability and reliable noncovalent interactions, can be tailored via tunable assembly to precisely create biomedical materials. In this review, we summarize the progress in the self-assembly of peptides in living cells for disease diagnosis and therapy. After a brief introduction to the basic design principles of peptide assembly systems in living cells, the applications of peptide assemblies for bioimaging and disease treatment are highlighted. The challenges in the field of peptide self-assembly in living cells and the prospects for novel peptide assembly systems towards next-generation biomaterials are also discussed, which will hopefully help elucidate the great potential of peptide assembly in living cells for future healthcare applications.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Self-Assembly of Heterogeneous Ferritin Nanocages for Tumor Uptake and Penetration

Well-defined nanostructures are crucial for precisely understanding nano-bio interactions. However, nanoparticles (NPs) fabricated through conventional synthesis approaches often lack poor controllability and reproducibility. Herein, a synthetic biology-based strategy is introduced to fabricate uniformly reproducible protein-based NPs, achieving precise control over heterogeneous components of the NPs. Specifically, a ferritin assembly toolbox system is developed that enables intracellular assembly of ferritin subunits/variants in Escherichia coli. Using this strategy, a proof-of-concept study is provided to explore the interplay between ligand density of NPs and their tumor targets/penetration. Various ferritin hybrid nanocages (FHn) containing human ferritin heavy chains (FH) and light chains are accurately assembled, leveraging their intrinsic binding with tumor cells and prolonged circulation time in blood, respectively. Further studies reveal that tumor cell uptake is FH density-dependent through active binding with transferrin receptor 1, whereas in vivo tumor accumulation and tissue penetration are found to be correlated to heterogeneous assembly of FHn and vascular permeability of tumors. Densities of 3.7 FH/100 nm2 on the nanoparticle surface exhibit the highest degree of tumor accumulation and penetration, particularly in tumors with high permeability compared to those with low permeability. This study underscores the significance of nanoparticle heterogeneity in determining particle fate in biological systems.

Exploiting Nanotechnology for Drug Delivery: Advancing the Anti-Cancer Effects of Autophagy-Modulating Compounds in Traditional Chinese Medicine

Background

Cancer continues to be a prominent issue in the field of medicine, as demonstrated by recent studies emphasizing the significant role of autophagy in the development of cancer. Traditional Chinese Medicine (TCM) provides a variety of anti-tumor agents capable of regulating autophagy. However, the clinical application of autophagy-modulating compounds derived from TCM is impeded by their restricted water solubility and bioavailability. To overcome this challenge, the utilization of nanotechnology has been suggested as a potential solution. Nonetheless, the current body of literature on nanoparticles delivering TCM-derived autophagy-modulating anti-tumor compounds for cancer treatment is limited, lacking comprehensive summaries and detailed descriptions.

Methods

Up to November 2023, a comprehensive research study was conducted to gather relevant data using a variety of databases, including PubMed, ScienceDirect, Springer Link, Web of Science, and CNKI. The keywords utilized in this investigation included "autophagy", "nanoparticles", "traditional Chinese medicine" and "anticancer".

Results

This review provides a comprehensive analysis of the potential of nanotechnology in overcoming delivery challenges and enhancing the anti-cancer properties of autophagy-modulating compounds in TCM. The evaluation is based on a synthesis of different classes of autophagy-modulating compounds in TCM, their mechanisms of action in cancer treatment, and their potential benefits as reported in various scholarly sources. The findings indicate that nanotechnology shows potential in enhancing the availability of autophagy-modulating agents in TCM, thereby opening up a plethora of potential therapeutic avenues.

Conclusion

Nanotechnology has the potential to enhance the anti-tumor efficacy of autophagy-modulating compounds in traditional TCM, through regulation of autophagy.

A landscape of recent advances in lipid nanoparticles and their translational potential for the treatment of solid tumors

Lipid nanoparticles (LNPs) are biocompatible drug delivery systems that have found numerous applications in medicine. Their versatile nature enables the encapsulation and targeting of various types of medically relevant molecular cargo, including oligonucleotides, proteins, and small molecules for the treatment of diseases, such as cancer. Cancers that form solid tumors are particularly relevant for LNP-based therapeutics due to the enhanced permeation and retention effect that allows nanoparticles to accumulate within the tumor tissue. Additionally, LNPs can be formulated for both locoregional and systemic delivery depending on the tumor type and stage. To date, LNPs have been used extensively in the clinic to reduce systemic toxicity and improve outcomes in cancer patients by encapsulating chemotherapeutic drugs. Next-generation lipid nanoparticles are currently being developed to expand their use in gene therapy and immunotherapy, as well as to enable the co-encapsulation of multiple drugs in a single system. Other developments include the design of targeted LNPs to specific cells and tissues, and triggerable release systems to control cargo delivery at the tumor site. This review paper highlights recent developments in LNP drug delivery formulations and focuses on the treatment of solid tumors, while also discussing some of their current translational limitations and potential opportunities in the field.

Nanotechnology's frontier in combatting infectious and inflammatory diseases: prevention and treatment

Inflammation-associated diseases encompass a range of infectious diseases and non-infectious inflammatory diseases, which continuously pose one of the most serious threats to human health, attributed to factors such as the emergence of new pathogens, increasing drug resistance, changes in living environments and lifestyles, and the aging population. Despite rapid advancements in mechanistic research and drug development for these diseases, current treatments often have limited efficacy and notable side effects, necessitating the development of more effective and targeted anti-inflammatory therapies. In recent years, the rapid development of nanotechnology has provided crucial technological support for the prevention, treatment, and detection of inflammation-associated diseases. Various types of nanoparticles (NPs) play significant roles, serving as vaccine vehicles to enhance immunogenicity and as drug carriers to improve targeting and bioavailability. NPs can also directly combat pathogens and inflammation. In addition, nanotechnology has facilitated the development of biosensors for pathogen detection and imaging techniques for inflammatory diseases. This review categorizes and characterizes different types of NPs, summarizes their applications in the prevention, treatment, and detection of infectious and inflammatory diseases. It also discusses the challenges associated with clinical translation in this field and explores the latest developments and prospects. In conclusion, nanotechnology opens up new possibilities for the comprehensive management of infectious and inflammatory diseases.

Applying artificial intelligence and computational modeling to nanomedicine

Achieving specific and targeted delivery of nanomedicines to diseased tissues is a major challenge. This is because the process of designing, formulating, testing, and selecting a nanoparticle delivery vehicle for a specific disease target is governed by complex multivariate interactions. Computational modeling and artificial intelligence are well-suited for analyzing and modeling large multivariate datasets in short periods of time. Computational approaches can be applied to help design nanomedicine formulations, interpret nanoparticle-biological interactions, and create models from high-throughput screening techniques to improve the selection of the ideal nanoparticle carrier. In the future, many steps in the nanomedicine development process will be done computationally, reducing the number of experiments and time needed to select the ideal nanomedicine formulation.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

Nanodelivery Systems as a Novel Strategy to Overcome Treatment Failure of Cancer

Despite the tremendous progress in cancer treatment in recent decades, cancers often become resistant due to multiple mechanisms, such as intrinsic or acquired multidrug resistance, which leads to unsatisfactory treatment effects or accompanying metastasis and recurrence, ultimately to treatment failure. With a deeper understanding of the molecular mechanisms of tumors, researchers have realized that treatment designs targeting tumor resistance mechanisms would be a promising strategy to break the therapeutic deadlock. Nanodelivery systems have excellent physicochemical properties, including highly efficient tissue-specific delivery, substantial specific surface area, and controllable surface chemistry, which endow nanodelivery systems with capabilities such as precise targeting, deep penetration, responsive drug release, multidrug codelivery, and multimodal synergy, which are currently widely used in biomedical researches and bring a new dawn for overcoming cancer resistance. Based on the mechanisms of tumor therapeutic resistance, this review summarizes the research progress of nanodelivery systems for overcoming tumor resistance to improve therapeutic efficacy in recent years and offers prospects and challenges of the application of nanodelivery systems for overcoming cancer resistance.

Promoting Intratumoral Drug Accumulation by Bio-Membrane Regulated Active Targeting for Tumor Photothermal Therapy

Introduction

Insufficient tumor permeability and inadequate nanoparticle retention continue to be significant limitations in the efficacy of anti-tumor drug therapy. Numerous studies have focused on enhancing tumor perfusion by improvement of tumor-induced endothelial leakage, often known as the enhanced permeability and retention (EPR) effect. However, these approaches have produced suboptimal therapeutic outcomes and have been associated with significant side effects. Therefore, in this study, we prepared tumor cell membrane-coated gold nanorods (GNR@TM) to enhance drug delivery in tumors through homogeneous targeting of tumor cell membranes and in situ real-time photo-controlled therapy.

Methods

Here, we fabricated GNR@TM, and characterized it using various techniques including Ultraviolet-Visible (UV-Vis) spectrophotometer, particle size analysis, potential measurement, and transmission electron microscopy (TEM). The cellular uptake and cytotoxicity of GNR@TM were analyzed by flow cytometry, confocal laser scanning microscopy (CLSM), TEM, CCK8 assay and live/dead staining. Tissue drug distribution was determined by inductively coupled plasma mass spectrometry (ICP-MS) and immunofluorescence staining. Furthermore, to evaluate the therapeutic effect, mice bearing MB49 tumors were intravenously administered with GNR@TM. Subsequently, near-infrared (NIR) laser therapy was performed, and the mice's tumor growth and body weight were monitored.

Results

The tumor cell membrane coating endowed GNR@TM with extended circulation time in vivo and homotypic targeting to tumor, thereby enhancing the accumulation of GNR@TM within tumors. Upon 780 nm laser, GNR@TM exhibited excellent photothermal conversion capability, leading to increased tumor vascular leakage. This magnification of the EPR effect induced by NIR laser further increased the accumulation of GNR@TM at the tumor site, demonstrating strong antitumor effects in vivo.

Conclusion

In this study, we successfully developed a NIR-triggered nanomedicine that increased drug accumulation in tumor through photo-controlled therapy and homotypic targeting of the tumor cell membrane. GNR@TM has been demonstrated effective suppression of tumor growth, excellent biocompatibility, and significant potential for clinical applications.

The spatiotemporal journey of nanomedicines in solid tumors on their therapeutic efficacy

The rapid development of nanomedicines is revolutionizing the landscape of cancer treatment, while effectively delivering them into solid tumors remains a formidable challenge. Currently, there is a huge disconnect on therapeutic response between regulatory approved nanomedicines and laboratory reported nanoparticles. The discrepancy is mainly resulted from the failure of using the classic overall pharmacokinetics behaviors of nanomedicines in tumors to predict the antitumor efficacy. Increasing evidence has revealed that the therapeutic efficacy predominantly relies on the intratumoral spatiotemporal distribution of nanomedicines. This review focuses on the spatiotemporal distribution of systemically administered chemotherapeutic nanomedicines in solid tumor. Firstly, the intratumoral biological barriers that regulate the spatiotemporal distribution of nanomedicines are described in detail. Next, the influences on antitumor efficacy caused by the spatial distribution and temporal drug release of nanomedicines are emphatically analyzed. Then, current methodologies for evaluating the spatiotemporal distribution of nanomedicines are summarized. Finally, the advanced strategies to positively modulate the spatiotemporal distribution of nanomedicines for an optimal tumor therapy are comprehensively reviewed.

Spatiotemporally-Programmed Dual-Acid-Sensitive Nanoworms of Albumin-Poly(tertiary amine)-Doxorubicin Conjugates for Enhanced Cancer Chemotherapy

Nanomedicines are potentially useful for targeted cancer chemotherapy; however, it is difficult to design nanomedicines with controllable structures and functions to overcome a series of biological and pathological barriers to efficiently kill cancer cells in vivo. Here, this work reports in situ growth of dual-acid-sensitive poly(tertiary amine)-doxorubicin conjugates from albumin to form dual-acid-sensitive albumin-poly(tertiary amine)-doxorubicin conjugates that self-assemble into nanospheres and nanoworms in a controlled manner. Both nanospheres and nanoworms rapidly dissociate into positively-charged unimers at pH < 6.9 and quickly releases the conjugated drug of doxorubicin at pH < 5.6, leading to enhanced penetration in tumor cell spheroids as well as improved uptake and cytotoxicity to tumor cells at pH < 6.9. Notably, nanoworms are less taken up by endothelial cells than nanospheres and doxorubicin, leading to improved pharmacokinetics. In a mouse model of triple negative breast cancer, nanoworms accumulate and penetrate into tumors more efficiently than nanospheres and doxorubicin, leading to enhanced tumor accumulation and penetration. As a result, nanoworms outperform nanospheres and doxorubicin in suppressing tumor growth and elongating the animal survival time, without observed side effects. These findings demonstrate that intelligent nanoworms with spatiotemporally programmed dual-acid-sensitive properties are promising as next-generation nanomedicines for targeted cancer chemotherapy.

Assessing Tumor Microenvironment Characteristics and Stratifying EPR with a Nanobubble Companion Nanoparticle via Contrast-Enhanced Ultrasound Imaging

The tumor microenvironment is characterized by dysfunctional endothelial cells, resulting in heightened vascular permeability. Many nanoparticle-based drug delivery systems attempt to use this enhanced permeability combined with impaired lymphatic drainage (a concept known as the 'enhanced permeability and retention effect' or EPR effect) as the primary strategy for drug delivery, but this has not proven to be as clinically effective as anticipated. The specific mechanisms behind the inconsistent clinical outcomes of nanotherapeutics have not been clearly articulated, and the field has been hampered by a lack of accessible tools to study EPR-associated phenomena in clinically relevant scenarios. While medical imaging has tremendous potential to contribute to this area, it has not been broadly explored. This work examines, for the first time, the use of multiparametric dynamic contrast-enhanced ultrasound (CEUS) with a novel nanoscale contrast agent to examine tumor microenvironment characteristics noninvasively and in real-time. We demonstrate that CEUS imaging can: (1) evaluate tumor microenvironment features and (2) be used to help predict the distribution of doxorubicin-loaded liposomes in the tumor parenchyma. CEUS using nanobubbles (NBs) was carried out in two tumor types of high (LS174T) and low (U87) vascular permeability, and time-intensity curve (TIC) parameters were evaluated in both models prior to injection of doxorubicin liposomes. Consistently, LS174T tumors showed significantly different TIC parameters, including area under the rising curve (2.7x), time to peak intensity (1.9x) and decorrelation time (DT, 1.9x) compared to U87 tumors. Importantly, the DT parameter successfully predicted tumoral nanoparticle distribution (r = 0.86 ± 0.13). Ultimately, substantial differences in NB-CEUS generated parameters between LS174T and U87 tumors suggest that this method may be useful in determining tumor vascular permeability and could be used as a biomarker for identifying tumor characteristics and predicting sensitivity to nanoparticle-based therapies. These findings could ultimately be applied to predicting treatment efficacy and to evaluating EPR in other diseases with pathologically permeable vasculature.

Smart nanoparticles for cancer therapy

Smart nanoparticles, which can respond to biological cues or be guided by them, are emerging as a promising drug delivery platform for precise cancer treatment. The field of oncology, nanotechnology, and biomedicine has witnessed rapid progress, leading to innovative developments in smart nanoparticles for safer and more effective cancer therapy. In this review, we will highlight recent advancements in smart nanoparticles, including polymeric nanoparticles, dendrimers, micelles, liposomes, protein nanoparticles, cell membrane nanoparticles, mesoporous silica nanoparticles, gold nanoparticles, iron oxide nanoparticles, quantum dots, carbon nanotubes, black phosphorus, MOF nanoparticles, and others. We will focus on their classification, structures, synthesis, and intelligent features. These smart nanoparticles possess the ability to respond to various external and internal stimuli, such as enzymes, pH, temperature, optics, and magnetism, making them intelligent systems. Additionally, this review will explore the latest studies on tumor targeting by functionalizing the surfaces of smart nanoparticles with tumor-specific ligands like antibodies, peptides, transferrin, and folic acid. We will also summarize different types of drug delivery options, including small molecules, peptides, proteins, nucleic acids, and even living cells, for their potential use in cancer therapy. While the potential of smart nanoparticles is promising, we will also acknowledge the challenges and clinical prospects associated with their use. Finally, we will propose a blueprint that involves the use of artificial intelligence-powered nanoparticles in cancer treatment applications. By harnessing the potential of smart nanoparticles, this review aims to usher in a new era of precise and personalized cancer therapy, providing patients with individualized treatment options.

Interpretable XGBoost-SHAP Model Predicts Nanoparticles Delivery Efficiency Based on Tumor Genomic Mutations and Nanoparticle Properties

Understanding the complex interaction between nanoparticles (NPs) and tumors in vivo and how it dominates the delivery efficiency of NPs is critical for the translation of nanomedicine. Herein, we proposed an interpretable XGBoost-SHAP model by integrating the information on NPs physicochemical properties and tumor genomic profile to predict the delivery efficiency. The correlation coefficients were 0.66, 0.75, and 0.54 for the prediction of maximum delivery efficiency, delivery efficiency at 24 and 168 h postinjection for test sets. The analysis of the feature importance revealed that the tumor genomic mutations and their interaction with NPs properties played important roles in the delivery of NPs. The biological pathways of the NP-delivery-related genes were further explored through gene ontology enrichment analysis. Our work provides a pipeline to predict and explain the delivery efficiency of NPs to heterogeneous tumors and highlights the power of simultaneously using omics data and interpretable machine learning algorithms for discovering interactions between NPs and individual tumors, which is important for the development of personalized precision nanomedicine.

Elastic Polymer Coated Nanoparticles with Fast Clearance for 19 F MR Imaging

19 F magnetic resonance imaging (MRI) is a powerful molecular imaging technique that enables high-resolution imaging of deep tissues without background signal interference. However, the use of nanoparticles (NPs) as 19 F MRI probes has been limited by the immediate trapping and accumulation of stiff NPs, typically of around 100 nm in size, in the mononuclear phagocyte system, particularly in the liver. To address this issue, elastic nanomaterials have emerged as promising candidates for improving delivery efficacy in vivo. Nevertheless, the impact of elasticity on NP elimination has remained unclear due to the lack of suitable probes for real-time and long-term monitoring. In this study, we present the development of perfluorocarbon-encapsulated polymer NPs as a novel 19 F MRI contrast agent, with the aim of suppressing long-term accumulation. The polymer NPs have high elasticity and exhibit robust sensitivity in 19 F MRI imaging. Importantly, our 19 F MRI data demonstrate a gradual decline in the signal intensity of the polymer NPs after administration, which contrasts starkly with the behavior observed for stiff silica NPs. This innovative polymer-coated NP system represents a groundbreaking nanomaterial that successfully overcomes the challenges associated with long-term accumulation, while enabling tracking of biodistribution over extended periods.

STING agonist-boosted mRNA immunization via intelligent design of nanovaccines for enhancing cancer immunotherapy

Messenger RNA (mRNA) vaccine is revolutionizing the methodology of immunization in cancer. However, mRNA immunization is drastically limited by multistage biological barriers including poor lymphatic transport, rapid clearance, catalytic hydrolysis, insufficient cellular entry and endosome entrapment. Herein, we design a mRNA nanovaccine based on intelligent design to overcome these obstacles. Highly efficient nanovaccines are carried out with machine learning techniques from datasets of various nanocarriers, ensuring successful delivery of mRNA antigen and cyclic guanosine monophosphate-adenosine monophosphate (cGAMP) to targets. It activates stimulator of interferon genes (STING), promotes mRNA-encoded antigen presentation and boosts antitumour immunity in vivo, thus inhibiting tumour growth and ensuring long-term survival of tumour-bearing mice. This work provides a feasible and safe strategy to facilitate STING agonist-synergized mRNA immunization, with great translational potential for enhancing cancer immunotherapy.

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.

Delivery of Chemotherapy Agents and Nucleic Acids with pH-Dependent Nanoparticles

With less than one percent of systemically injected nanoparticles accumulating in tumors, several novel approaches have been spurred to direct and release the therapy in or near tumors. One such approach depends on the acidic pH of the extracellular matrix and endosomes of the tumor. With an average pH of 6.8, the extracellular tumor matrix provides a gradient for pH-responsive particles to accumulate, enabling greater specificity. Upon uptake by tumor cells, nanoparticles are further exposed to lower pHs, reaching a pH of 5 in late endosomes. Based on these two acidic environments in the tumor, various pH-dependent targeting strategies have been employed to release chemotherapy or the combination of chemotherapy and nucleic acids from macromolecules such as the keratin protein or polymeric nanoparticles. We will review these release strategies, including pH-sensitive linkages between the carrier and hydrophobic chemotherapy agent, the protonation and disruption of polymeric nanoparticles, an amalgam of these first two approaches, and the release of polymers shielding drug-loaded nanoparticles. While several pH-sensitive strategies have demonstrated marked antitumor efficacy in preclinical trials, many studies are early in their development with several obstacles that may limit their clinical use.

Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery

Research and development (R&D) of nanodrugs is a long, complex and uncertain process. Since the 1960s, computing has been used as an auxiliary tool in the field of drug discovery. Many cases have proven the practicability and efficiency of computing in drug discovery. Over the past decade, computing, especially model prediction and molecular simulation, has been gradually applied to nanodrug R&D, providing substantive solutions to many problems. Computing has made important contributions to promoting data-driven decision-making and reducing failure rates and time costs in discovery and development of nanodrugs. However, there are still a few articles to examine, and it is necessary to summarize the development of the research direction. In the review, we summarize application of computing in various stages of nanodrug R&D, including physicochemical properties and biological activities prediction, pharmacokinetics analysis, toxicological assessment and other related applications. Moreover, current challenges and future perspectives of the computing methods are also discussed, with a view to help computing become a high-practicability and -efficiency auxiliary tool in nanodrugs discovery and development.