the intrinsic characteristics of nano materials, nanozymes have potential widespread applications within the fields of biosensing, [2] antibacterials, [3] environ mental pollution, [4] and disease therapy. [5] Since our discovery of ferromagnetic nanoparticles with intrinsic peroxidase like activity in 2007, [6] there have been thousands of publications that reported on enzymemimicking activities of nanoma terials, which involve at least six classes of enzymemimicry. [7] According to the litera ture, different nanomaterials can intrinsi cally possess the same enzymemimicking activities, [8] and certain types of nano materials tend to exhibit differential enzymelike catalytic activities. [9] The het erogeneous results reveal the complexity and diversity of nanozymes in terms of catalytic capacity. [10] Indeed, the particle property relationship of nanozymes is complicated, with a current lack of fun damental understanding. Furthermore, the synthesis of nanozymes with desired characteristics are generally determined by trial and error, and based on intui tion and experience, which are timeconsuming, laborious and resourceintensive.As a branch of artificial intelligence, machine learning aims to develop computational algorithms to infer mathematical An abundant number of nanomaterials have been discovered to possess enzyme-like catalytic activity, termed nanozymes. It is identified that a variety of internal and external factors influence the catalytic activity of nanozymes. However, there is a lack of essential methodologies to uncover the hidden mechanisms between nanozyme features and enzyme-like activity. Here, a data-driven approach is demonstrated that utilizes machine-learning algorithms to understand particle-property relationships, allowing for classification and quantitative predictions of enzyme-like activity exhibited by nanozymes. High consistency between predicted outputs and the observations is confirmed by accuracy (90.6%) and R 2 (up to 0.80). Furthermore, sensitive analysis of the models reveals the central roles of transition metals in determining nanozyme activity. As an example, the models are successfully applied to predict or design desirable nanozymes by uncovering the hidden relationship between different periods of transition metals and their enzyme-like performance. This study offers a promising strategy to develop nanozymes with desirable catalytic activity and demonstrates the potential of machine learning within the field of material science.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.202201736.
Scheme 1. Schematic illustration of the construction of DOX-loaded MXene-DNA hydrogel and its application as a NIR-responsive injectable platform for the photothermal-chemo synergistic treatment of tumor.
Rationale: Employing in situ bioorthogonal catalysis within subcellular organelles, such as lysosomes, remains a challenge. Lysosomal membranes pose an intracellular barrier for drug sequestration, thereby greatly limiting drug accumulation and concentrations at intended targets. Here, we provide a proof-of-concept report of a nanozyme-based strategy that mediates in situ bioorthogonal uncaging reactions within lysosomes, followed by lysosomal escape and the release of uncaged drugs into the cytoplasm. Methods: A model system composed of a protein-based nanozyme platform (based on the transition metals Co, Fe, Mn, Rh, Ir, Pt, Au, Ru and Pd) and caged compound fluorophores was designed to screen for nanozyme/protecting group pairings. The optimized nanozyme/protecting group pairing was then selected for utilization in the design of anti-cancer pro-drugs and drug delivery systems. Results: Our screening system identified Pd nanozymes that mimic mutant P450 BM3 activity and specifically cleave propargylic ether groups. We found that the intrinsic peroxidase-like activity of Pd nanozymes induced the production of free radicals under acid conditions, resulting in lysosomal membrane leakage of uncaged molecules into the cytoplasm. Using a multienzyme synergistic approach, our Pd nanozymes achieved in situ bioorthogonal catalysis and nanozyme-mediated lysosomal membrane leakage, which were successfully applied to the design of model pro-drugs for anti-cancer therapy. The extension of our nanozyme system to the construction of a liposome-based “all-in-one” delivery system offers promise for realizing efficacious in vivo tumor-targeted therapies. Conclusions: This strategy shows a promising new direction by utilizing nanotechnology for drug development through in situ catalyzing bioorthogonal chemistry within specific subcellular organelles.
Enzymes are an important component for bottom‐up building of synthetic/artificial cells. Nanozymes are nanomaterials with intrinsic enzyme‐like properties, however, the construction of synthetic cells using nanozymes is difficult owing to their high surface energy or large size. Herein, the authors show a protein‐based general platform that biomimetically integrates various ultrasmall metal nanozymes into protein shells. Specifically, eight metal‐based ultrasmall nano‐particles/clusters are in situ incorporated into ferritin nanocages that are self‐assembled by 24 subunits of ferritin heavy chain. As a nanozyme generator, such a platform is suitable for screening the desired enzyme‐like activities, including peroxidase (POD), oxidase (OXD), catalase (CAT) and superoxide dismutase (SOD). After screening, it is found that Ru intrinsically possesses the highest POD‐like and CAT‐like activities, while Mn and Pt show the highest OXD‐like and SOD‐like activities, respectively. Additionally, the inducers/inhibitors of various nanozymes are screened from more than 50 compounds to improve or inhibit their enzyme‐like activities. Based on the screened nanozymes and their inhibitors, a proof‐of‐conceptually constructs cell‐mimicking catalytic vesicles to mimic or modulate the events of redox homeostasis in living cells. This study offers a type of artificial metalloenzyme based on nanotechnology and shows a choice for bottom‐up enzyme‐based synthetic cell systems in a fully synthetic manner.
Reports on the comprehensive factors for design considerations of hypoxia-activated prodrugs (HAPs) are rare. We introduced a new model system composed of a series of highly water-soluble HAPs, providing a platform to comprehensively understand the interaction between HAPs and hypoxic biosystems. Specifically, four kinds of new HAPs were designed and synthesized, containing the same biologically active moiety but masked by different bioreductive groups. Our results demonstrated that the activity of the prodrugs was strongly dependent on not only the molecular structure but also the hypoxic tumor microenvironment. We found the presence of a direct linear relationship between cytotoxicity of the HAPs and the reduction potential of whole molecule/oxygen concentration/reductase expression. Moreover, limited blood vasculature in hypoxic regions was also a critical barrier for effective activation of the HAPs. This study offers a comprehensive insight into understanding the design factors required for HAPs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.