Catalytic Signal Transduction Theory Enabled Virtual Screening of Nanomaterials for Medical Functions

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.

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.

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.

Harnessing Pb-S Interactions for Long-Term Water Stability in Cesium Lead Halide Perovskite Nanocrystals

Lead halide perovskite nanocrystals (LHP NCs) have garnered attention as promising light-harvesting materials for optoelectronics and photovoltaic devices, attributed to their impressive optoelectronic properties. However, their susceptibility to moisture-induced degradation has hindered their practical applications. Despite various encapsulation strategies, challenges persist in maintaining their stability and optoelectronic performance simultaneously. Here, a ligand exchange approach is proposed using (11-mercaptoundecyl)-N,N,N-trimethylammonium bromide (MUTAB) to enhance the stability and dispersibility of CsPbBr3 (CPB) NCs in aqueous environments. MUTAB enables effective surface passivation of the CPB NCs via robust Pb-S interactions at the S-terminal while concurrently directing water molecules through the unbound cationic N-terminal or vice versa, ensuring water dispersibility and stability. Spectroscopic analysis confirms retained structural and optical integrity post-ligand exchange. Crucially, MUTAB-bound CPB NCs exhibit sustained charge transfer properties, demonstrated by aqueous colloidal oxidation reactions. This ligand exchange strategy offers a promising pathway for advancing LHP NCs toward practical optoelectronic and photocatalytic applications.

Copper regulation of PtRhRuCu nanozyme targeted boosting peroxidase-like activity for ultrasensitive smartphone-assisted colorimetric sensing of glucose

Point-of-care testing (POCT) is promising for biodetection in home healthcare due to advantages of simplicity, rapidity, low cost, portability, high sensitivity and accuracy, and object-oriented POCT platform can be developed by nanozyme-based biosensing. However, designing high-performance nanozymes with targeted regulated catalytic activity remains challenging. Herein, advanced PtRhRuCu quaternary alloy nanozymes (QANs) were rationally designed and successfully synthesized. Cu atoms induced mechanisms of hydrogen peroxide (H2O2) activation and d-band center regulation, achieving high enhancement of peroxide (POD)-like activity and inhibition of oxidase (OXD)-like activity. Inspired by this, a smartphone-assisted colorimetric platform integrated with test strips was established for glucose detection of soft drinks, with a detection limit of 0.021 mM and a recovery rate of 97.87 to 103.36 %. This work not only provides a novel path for tuning specific enzyme-like activities of metal nanozymes, but also shows the potential feasibility for rational design of POCT sensors in actual samples.

Progress in the Computer-Aided Analysis in Multiple Aspects of Nanocatalysis Research

Making the utmost of the differences and advantages of multiple disciplines, interdisciplinary integration breaks the science boundaries and accelerates the progress in mutual quests. As an organic connection of material science, enzymology, and biomedicine, nanozyme-related research is further supported by computer technology, which injects in new vitality, and contributes to in-depth understanding, unprecedented insights, and broadened application possibilities. Utilizing computer-aided first-principles method, high-speed and high-throughput mathematic, physic, and chemic models are introduced to perform atomic-level kinetic analysis for nanocatalytic reaction process, and theoretically illustrate the underlying nanozymetic mechanism and structure-function relationship. On this basis, nanozymes with desirable properties can be designed and demand-oriented synthesized without repeated trial-and-error experiments. Besides that, computational analysis and device also play an indispensable role in nanozyme-based detecting methods to realize automatic readouts with improved accuracy and reproducibility. Here, this work focuses on the crossing of nanocatalysis research and computational technology, to inspire the research in computer-aided analysis in nanozyme field to a greater extent.

Nonradical Surface Chemistry Mechanisms for Catalytic Nanoparticles

All radical-detecting methods using trapping agents, which are originally developed for homogeneous reaction systems, may not be applicable to systems with solid surfaces. This is because false radical signals can be generated in the presence of solid surfaces. An extra selectivity study following the trapping agent experiment may help in distinguishing between the true and false radical signals. Surface chemistry mechanisms are superior to free-radical mechanisms in not only correctly understanding the reaction selectivity previously reported for catalytic nanoparticles but also developing theoretical models for the computational design of solid catalysts in the future.

Sensitive and Selective Dual-Mode Responses to Reactive Oxygen Species by Chiral Manganese Dioxide Nanoparticles for Antiaging Skin

Excessive accumulation of reactive oxygen species (ROS) can lead to oxidative stress and oxidative damage, which is one of the important factors for aging and age-related diseases. Therefore, real-time monitoring and the moderate elimination of ROS is extremely important. In this study, a ROS-responsive circular dichroic (CD) at 553 nm and magnetic resonance imaging (MRI) dual-signals chiral manganese oxide (MnO2 ) nanoparticles (NPs) are designed and synthesized. Both the CD and MRI signals show excellent linear ranges for intracellular hydrogen peroxide (H2 O2 ) concentrations, with limits of detection (LOD) of 0.0027 nmol/106 cells and 0.016 nmol/106 cells, respectively. The lower LOD achieved with CD detection may be attributable to its higher anti-interference capability from the intracellular matrix. Importantly, ROS-induced cell aging is intervened by chiral MnO2 NPs via redox reactions with excessive intracellular ROS. In vivo experiments confirm that chiral MnO2 NPs effectively eliminate ROS in skin tissue, reduce oxidative stress levels, and alleviate skin aging. This approach provides a new strategy for the diagnosis and treatment of age-related diseases.

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.