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.

Optimizing the standardized assays for determining the catalytic activity and kinetics of peroxidase-like nanozymes.

Nanozymes are nanomaterials with enzyme-like catalytic properties. They are attractive reagents because they do not have the same limitations of natural enzymes (e.g., high cost, low stability and difficult storage). To test, optimize and compare nanozymes, it is important to establish fundamental principles and systematic standards to fully characterize their catalytic performance. Our 2018 protocol describes how to characterize the catalytic activity and kinetics of peroxidase nanozymes, the most widely used type of nanozyme. This approach was based on Michaelis-Menten enzyme kinetics and is now updated to take into account the unique physicochemical properties of nanomaterials that determine the catalytic kinetics of nanozymes. The updated procedure describes how to determine the number of active sites as well as other physicochemical properties such as surface area, shape and size. It also outlines how to calculate the hydroxyl adsorption energy from the crystal structure using the density functional theory method. The calculations now incorporate these measurements and computations to better characterize the catalytic kinetics of peroxidase nanozymes that have different shapes, sizes and compositions. This updated protocol better describes the catalytic performance of nanozymes and benefits the development of nanozyme research since further nanozyme development requires precise control of activity by engineering the electronic, geometric structure and atomic configuration of the catalytic sites of nanozymes. The characterization of the catalytic activity of peroxidase nanozymes and the evaluation of their kinetics can be performed in 4 h. The procedure is suitable for users with expertise in nano- and materials technology.

Computer-aided nanodrug discovery: recent progress and future prospects.

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

A phosphatase-like nanomaterial promotes autophagy and reprograms macrophages for cancer immunotherapy.

Macrophages are plastic and play a key role in the maintenance of tissue homeostasis. In cancer progression, macrophages also take part in all processes, from initiation to progression, to final tumor metastasis. Although energy deprivation and autophagy are widely used for cancer therapy, most of these strategies do not target macrophages, resulting in undesired effects and unsatisfactory outcomes for cancer immunotherapy. Herein, we developed a lanthanum nickel oxide (LNO) nanozyme with phosphatase-like activity for ATP hydrolysis. Meanwhile, the autophagy of macrophages induced by LNO promotes the polarization of macrophages from M2-like macrophages (M2) to M1-like macrophages (M1) and reduces tumor-associated macrophages in tumor-bearing mice, exhibiting the capability of killing tumor-associated macrophages and antitumor effects in vivo. Furthermore, pre-coating the surface of LNO with a myeloid cell membrane significantly enhanced antitumor immunity. Our findings demonstrate that phosphatase-like nanozyme LNO can specifically induce macrophage autophagy, which improves therapeutic efficacy and offers valuable strategies for cancer immunotherapy.

Elucidating the catalytic mechanism of Prussian blue nanozymes with self-increasing catalytic activity.

Although Prussian blue nanozymes (PBNZ) are widely applied in various fields, their catalytic mechanisms remain elusive. Here, we investigate the long-term catalytic performance of PBNZ as peroxidase (POD) and catalase (CAT) mimetics to elucidate their lifespan and underlying mechanisms. Unlike our previously reported Fe3O4 nanozymes, which exhibit depletable POD-like activity, the POD and CAT-like activities of PBNZ not only persist but slightly enhance over prolonged catalysis. We demonstrate that the irreversible oxidation of PBNZ significantly promotes catalysis, leading to self-increasing catalytic activities. The catalytic process of the pre-oxidized PBNZ can be initiated through either the conduction band pathway or the valence band pathway. In summary, we reveal that PBNZ follows a dual-path electron transfer mechanism during the POD and CAT-like catalysis, offering the advantage of a long service life.

Injectable Hydrogel Mucosal Vaccine Elicits Protective Immunity against Respiratory Viruses.

Intranasal vaccines, eliciting mucosal immune responses, can prevent early invasion, replication, and transmission of pathogens in the respiratory tract. However, the effective delivery of antigens through the nasal barrier and boosting of a robust systematic and mucosal immune remain challenges in intranasal vaccine development. Here, we describe an intranasally administered self-healing hydrogel vaccine with a reversible strain-dependent sol-gel transition by precisely modulating the self-assembly processes between the natural drug rhein and aluminum ions. The highly bioadhesive hydrogel vaccine enhances antigen stability and prolongs residence time in the nasal cavity and lungs by confining the antigen to the surface of the nasal mucosa, acting as a "mucosal mask". The hydrogel also stimulates superior immunoenhancing properties, including antigen internalization, cross-presentation, and dendritic cell maturation. Furthermore, the formulation recruits immunocytes to the nasal mucosa and nasal-associated lymphoid tissue (NALT) while enhancing antigen-specific humoral, cellular, and mucosal immune responses. Our findings present a promising strategy for preparing intranasal vaccines for infectious diseases or cancer.

Ultrasmall metal alloy nanozymes mimicking neutrophil enzymatic cascades for tumor catalytic therapy.

Developing strategies that emulate the killing mechanism of neutrophils, which involves the enzymatic cascade of superoxide dismutase (SOD) and myeloperoxidase (MPO), shows potential as a viable approach for cancer therapy. Nonetheless, utilizing natural enzymes as therapeutics is hindered by various challenges. While nanozymes have emerged for cancer treatment, developing SOD-MPO cascade in one nanozyme remains a challenge. Here, we develop nanozymes possessing both SOD- and MPO-like activities through alloying Au and Pd, which exhibits the highest cascade activity when the ratio of Au and Pd is 1:3, attributing to the high d-band center and adsorption energy for superoxide anions, as determined through theoretical calculations. The Au1Pd3 alloy nanozymes exhibit excellent tumor therapeutic performance and safety in female tumor-bearing mice, with safety attributed to their tumor-specific killing ability and renal clearance ability caused by ultrasmall size. Together, this work develops ultrasmall AuPd alloy nanozymes that mimic neutrophil enzymatic cascades for catalytic treatment of tumors.

A natural biogenic nanozyme for scavenging superoxide radicals.

Biominerals, the inorganic minerals of organisms, are known mainly for their physical property-related functions in modern living organisms. Our recent discovery of the enzyme-like activities of nanomaterials, coined as nanozyme, inspires the hypothesis that nano-biominerals might function as enzyme-like catalyzers in cells. Here we report that the iron cores of biogenic ferritins act as natural nanozymes to scavenge superoxide radicals. Through analyzing eighteen representative ferritins from three living kingdoms, we find that the iron core of prokaryote ferritin possesses higher superoxide-diminishing activity than that of eukaryotes. Further investigation reveals that the differences in catalytic capability result from the iron/phosphate ratio changes in the iron core, which is mainly determined by the structures of ferritins. The phosphate in the iron core switches the iron core from single crystalline to amorphous iron phosphate-like structure, resulting in decreased affinity to the hydrogen proton of the ferrihydrite-like core that facilitates its reaction with superoxide in a manner different from that of ferric ions. Furthermore, overexpression of ferritins with high superoxide-diminishing activities in E. coli increases the resistance to superoxide, whereas bacterioferritin knockout or human ferritin knock-in diminishes free radical tolerance, highlighting the physiological antioxidant role of this type of nanozymes.

Integrated Computational and Experimental Framework for Inverse Screening of Candidate Antibacterial Nanomedicine.

Nanomedicine is promising for disease prevention and treatment, but there are still many challenges that hinder its rapid development. A major challenge is to efficiently seek candidates with the desired therapeutic functions from tremendously available materials. Here, we report an integrated computational and experimental framework to seek alloy nanoparticles from the Materials Project library for antibacterial applications, aiming to learn the inverse screening concept from traditional medicine for nanomedicine. Because strong peroxidase-like catalytic activity and weak toxicity to normal cells are the desired material properties for antibacterial usage, computational screening implementing theoretical prediction models of catalytic activity and cytotoxicity is first conducted to select the candidates. Then, experimental screening based on scanning probe block copolymer lithography is used to verify and refine the computational screening results. Finally, the best candidate AuCu3 is synthesized in solution and its antibacterial performance over other nanoparticles against S. aureus and E. coli. is experimentally confirmed. The results show the power of inverse screening in accelerating the research and development of antibacterial nanomedicine, which may inspire similar strategies for other nanomedicines in the future.

Surface Ligand Engineering Ruthenium Nanozyme Superior to Horseradish Peroxidase for Enhanced Immunoassay.

Nanozymes have great potential to be used as an alternative to natural enzymes in a variety of fields. However, low catalytic activity compared with natural enzymes limits their practical use. It is still challenging to design nanozymes comparable to their natural counterparts in terms of the specific activity. In this study, a surface engineering strategy is employed to improve the specific activity of Ru nanozymes using charge-transferrable ligands such as polystyrene sulfonate (PSS). PSS-modified Ru nanozyme exhibits a peroxidase-like specific activity of up to 2820 U mg-1 , which is twice that of horseradish peroxidase (1305 U mg-1 ). Mechanism studies suggest that PSS readily accepts negative charge from Ru, thus reducing the affinity between Ru and ·OH. Importantly, the modified Ru-peroxidase nanozyme is successfully used to develop an immunoassay for human alpha-fetoprotein and achieves a 140-fold increase in detection sensitivity compared with traditional horseradish-peroxidase-based enzyme-linked immunosorbent assay. Therefore, this work provides a feasible route to design nanozymes with high specific activity that meets the practical use as an alternative to natural enzymes.

Reaction Mechanisms and Kinetics of Nanozymes: Insights from Theory and Computation.

"Nanozymes" usually refers to inorganic nanomaterials with enzyme-like catalytic activities. The research into nanozymes is one of the hot topics on the horizon of interdisciplinary science involving materials, chemistry, and biology. Although great progress has been made in the design, synthesis, characterization, and application of nanozymes, the study of the underlying microscopic mechanisms and kinetics is still not straightforward. Density functional theory (DFT) calculations compute the potential energy surfaces along the reaction coordinates for chemical reactions, which can give atomistic-level insights into the micro-mechanisms and kinetics for nanozymes. Therefore, DFT calculations have been playing an increasingly important role in exploring the mechanisms and kinetics for nanozymes in the past years. The calculations either predict the microscopic details for the catalytic processes to complement the experiments or further develop theoretical models to depict the physicochemical rules. In this review, the corresponding research progress is summarized. Particularly, the review focuses on the computational studies that closely interplay with the experiments. The relevant experimental results without DFT calculations will be also briefly discussed to offer a historic overview of how the computations promote the understanding of the microscopic mechanisms and kinetics of nanozymes.

Intrinsic Strain-Mediated Ultrathin Ceria Nanoantioxidant.

Metal oxide nanozymes have emerged as the most efficient and promising candidates to mimic antioxidant enzymes for treatment of oxidative stress-mediated pathophysiological disorders, but the current effectiveness is unsatisfactory due to insufficient catalytic performance. Here, we report for the first time an intrinsic strain-mediated ultrathin ceria nanoantioxidant. Surface strain in ceria with variable thicknesses and coordinatively unsaturated Ce sites was investigated by theoretical calculation analysis and then was validated by preparing ∼1.2 nm ultrathin nanoplates with ∼3.0% tensile strain in plane/∼10.0% tensile strain out of plane. Compared with nanocubes, surface strain in ultrathin nanoplates could enhance the covalency of the Ce-O bond, leading to increasing superoxide dismutase (SOD)-mimetic activity by ∼2.6-fold (1533 U/mg, in close proximity to that of natural SOD) and total antioxidant activity by ∼2.5-fold. As a proof of concept, intrinsic strain-mediated ultrathin ceria nanoplates could boost antioxidation for improved ischemic stroke treatment in vivo, significantly better than edaravone, a commonly used clinical drug.

Nano-Impact Electrochemistry Reveals Kinetics Information of Metal-Ion Battery Materials with Multiple Redox Centers.

Prussian blue (PB) has emerged as a promising cathode material in aqueous batteries. It possesses two distinct redox centers, and the potassium ions (K+ ) are unevenly distributed throughout the compound, adding complexity to the interpretation of the K+ insertion/de-insertion kinetic mechanism. Traditional ensemble-averaged measurements are limited in uncovering the precise kinetic information of the PB particles, as the results are influenced by the construction of the porous composite electrode and the redox behavior from different particles. In this study, the electrochemical processes of individual PB particles were investigated using nano-impact electrochemistry. By varying the potentials, different types of transient current signals were obtained that revealed the kinetic mechanism of each oxidation/reduction reaction in combination with theoretical simulation. Additionally, a partially contradictory conclusion between single-particle analysis and the ensemble-averaged measurement was discussed. These findings contribute to a better understanding of the electrochemical processes of cathode materials with multiple redox centers, which facilitates the development of effective strategies to optimize these materials.

A new capacity of gut microbiota: Fermentation of engineered inorganic carbon nanomaterials into endogenous organic metabolites.

Carbon-based nanomaterials (CNMs) have recently been found in humans raising a great concern over their adverse roles in the hosts. However, our knowledge of the in vivo behavior and fate of CNMs, especially their biological processes elicited by the gut microbiota, remains poor. Here, we uncovered the integration of CNMs (single-walled carbon nanotubes and graphene oxide) into the endogenous carbon flow through degradation and fermentation, mediated by the gut microbiota of mice using isotope tracing and gene sequencing. As a newly available carbon source for the gut microbiota, microbial fermentation leads to the incorporation of inorganic carbon from the CNMs into organic butyrate through the pyruvate pathway. Furthermore, the butyrate-producing bacteria are identified to show a preference for the CNMs as their favorable source, and excessive butyrate derived from microbial CNMs fermentation further impacts on the function (proliferation and differentiation) of intestinal stem cells in mouse and intestinal organoid models. Collectively, our results unlock the unknown fermentation processes of CNMs in the gut of hosts and underscore an urgent need for assessing the transformation of CNMs and their health risk via the gut-centric physiological and anatomical pathways.

Conjugated Nonplanar Copper-Catecholate Conductive Metal-Organic Frameworks via Contorted Hexabenzocoronene Ligands for Electrical Conduction.

Conductive metal-organic frameworks (c-MOFs) with outstanding electrical conductivities and high charge carrier mobilities are promising candidates for electronics and optoelectronics. However, the poor solubility of planar ligands greatly hinders the synthesis and widespread applications of c-MOFs. Nonplanar ligands with excellent solubility in organic solvents are ideal alternatives to construct c-MOFs. Herein, contorted hexabenzocoronene (c-HBC) derivatives with good solubility are adopted to synthesize c-MOFs. Three c-MOFs (c-HBC-6O-Cu, c-HBC-8O-Cu, and c-HBC-12O-Cu) with substantially different geometries and packing modes have been synthesized using three multitopic catechol-based c-HBC ligands with different symmetries and coordination numbers, respectively. With more metal coordination centers and increased charge transport pathways, c-HBC-12O-Cu exhibits the highest intrinsic electrical conductivity of 3.31 S m-1. Time-resolved terahertz spectroscopy reveals high charge carrier mobilities in c-HBC-based c-MOFs, ranging from 38 to 64 cm2 V-1 s-1. This work provides a systematic and modular approach to fine-tune the structure and enrich the c-MOF family with excellent charge transport properties using nonplanar and highly soluble ligands.

Deciphering the catalytic mechanism of superoxide dismutase activity of carbon dot nanozyme.

Nanozymes with superoxide dismutase (SOD)-like activity have attracted increasing interest due to their ability to scavenge superoxide anion, the origin of most reactive oxygen species in vivo. However, SOD nanozymes reported thus far have yet to approach the activity of natural enzymes. Here, we report a carbon dot (C-dot) SOD nanozyme with a catalytic activity of over 10,000 U/mg, comparable to that of natural enzymes. Through selected chemical modifications and theoretical calculations, we show that the SOD-like activity of C-dots relies on the hydroxyl and carboxyl groups for binding superoxide anions and the carbonyl groups conjugated with the π-system for electron transfer. Moreover, C-dot SOD nanozymes exhibit intrinsic targeting ability to oxidation-damaged cells and effectively protect neuron cells in the ischemic stroke male mice model. Together, our study sheds light on the structure-activity relationship of C-dot SOD nanozymes, and demonstrates their potential for treating of oxidation stress related diseases.

Targeted Synthesis of Isomeric Naphthalene-Based 2D Kagome Covalent Organic Frameworks.

Targeted synthesis of kagome (kgm) topologic 2D covalent organic frameworks remains challenging, presumably due to the severe dependence on building units and synthetic conditions. Herein, two isomeric "two-in-one" monomers with different lengths of substituted arms based on naphthalene core (p-Naph and m-Naph) are elaborately designed and utilized for the defined synthesis of isomeric kgm Naph-COFs. The two isomeric frameworks exhibit splendid crystallinity and showcase the same chemical composition and topologic structure with, however, different pore channels. Interestingly, C60 is able to uniformly be encapsulated into the triangle channels of m-Naph-COF via in situ incorporation method, while not the isomeric p-Naph-COF, likely due to the different pore structures of the two isomeric COFs. The resulting stable C60 @m-Naph-COF composite exhibits much higher photoconductivity than the m-Naph-COF owing to charge transfer between the conjugated skeletons and C60 guests.

Clear-Box Machine Learning for Virtual Screening of 2D Nanozymes to Target Tumor Hydrogen Peroxide.

Targeting tumor hydrogen peroxide (H2 O2 ) with catalytic materials has provided a novel chemotherapy strategy against solid tumors. Because numerous materials have been fabricated so far, there is an urgent need for an efficient in silico method, which can automatically screen out appropriate candidates from materials libraries for further therapeutic evaluation. In this work, adsorption-energy-based descriptors and criteria are developed for the catalase-like activities of materials surfaces. The result enables a comprehensive prediction of H2 O2 -targeted catalytic activities of materials by density functional theory (DFT) calculations. To expedite the prediction, machine learning models, which efficiently calculate the adsorption energies for 2D materials without DFT, are further developed. The finally obtained method takes advantage of both interpretability of physics model and high efficiency of machine learning. It provides an efficient approach for in silico screening of 2D materials toward tumor catalytic therapy, and it will greatly promote the development of catalytic nanomaterials for medical applications.

eg Occupancy as a Predictive Descriptor for Spinel Oxide Nanozymes.

Functional nanomaterials offer an attractive strategy to mimic the catalysis of natural enzymes, which are collectively called nanozymes. Although the development of nanozymes shows a trend of diversification of materials with enzyme-like activity, most nanozymes have been discovered via trial-and-error methods, largely due to the lack of predictive descriptors. To fill this gap, this work identified eg occupancy as an effective descriptor for spinel oxides with peroxidase-like activity and successfully predicted that the eg value of spinel oxide nanozymes with the highest activity is close to 0.6. The LiCo2O4 with the highest activity, which is finally predicted, has achieved more than an order of magnitude improvement in activity. Density functional theory provides a rationale for the reaction path. This work contributes to the rational design of high performance nanozymes by using activity descriptors and provides a methodology to identify other descriptors for nanozymes.