Fe-Doped ZnO nanoparticle toxicity: assessment by a new generation of nanodescriptors

Biomimetic Nanoplatform-Mediated Protective Autophagy Blockage Enhancing Sonodynamic and Ca2+-Overload Combined Therapy for Colon Cancer

The application of a multimodal combination therapy based on a targeted nanodelivery system has been demonstrated to be more valuable in the treatment of cancer. In this work, a hollow polydopamine delivery system (CCC@HP@M) was designed to achieve sonodynamic and calcium-overload combined therapy for colon cancer. The CCC@HP@M exhibits both homologous tumour-targeting ability and pH-responsive drug release properties, enabling the simultaneous targeted delivery of CaO2 nanoparticles/sonosensitizer Ce6/autophagy inhibitor CQ. The CaO2 nanoparticles as calcium agents capable of triggering Ca2+ overload in tumor cells. The oxidative stress produced by sonodynamic therapy is facilitated by the disruption of calcium homeostasis to enhance the effect of Ca2+ overload-induced apoptosis. Furthermore, the O2 produced by CaO2 augments the sensitization of sonodynamic therapy. The autophagy inhibitor CQ can inhibit protective cellular autophagy, which is activated by sonodynamic therapy and Ca2+ overload. Consequently, autophagy blockage can ensure the therapeutic effect of sonodynamic and Ca2+-overload combined therapy for colon cancer. The results of experiments in vitro and in vivo demonstrate that the stimulus-responsive targeted delivery system achieves autophagy blockage augmented sonodynamic and Ca2+-overload combined therapy of colon cancer. This work offers a promising theoretical basis for optimizing combined treatment strategies for tumors and clinical translational applications.

In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation

The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.

Silver nanoparticle induced immunogenic cell death can improve immunotherapy

Cancer immunotherapy is often hindered by an immunosuppressive tumor microenvironment (TME). Various strategies are being evaluated to shift the TME from an immunologically 'cold' to 'hot' tumor and hereby improve current immune checkpoint blockades (ICB). One particular hot topic is the use of combination therapies. Here, we set out to screen a variety of metallic nanoparticles and explored their in vitro toxicity against a series of tumor and non-tumor cell lines. For silver nanoparticles, we also explored the effects of core size and surface chemistry on cytotoxicity. Ag-citrate-5 nm nanoparticles were found to induce high cytotoxicity in Renca cells through excessive generation of reactive oxygen species (ROS) and significantly increased cytokine production. The induced toxicity resulted in a shift of the immunogenic cell death (ICD) marker calreticulin to the cell surface in vitro and in vivo. Subcutaneous Renca tumors were treated with anti-PD1 or in combination with Ag-citrate-5 nm. The combination group resulted in significant reduction in tumor size, increased necrosis, and immune cell infiltration at the tumor site. Inhibition of cytotoxic CD8 + T cells confirmed the involvement of these cells in the observed therapeutic effects. Our results suggest that Ag-citrate-5 nm is able to promote immune cell influx and increase tumor responsiveness to ICB therapies.

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.

Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study

The wide variation of nanomaterial (NM) characters (size, shape, and properties) and the related impacts on living organisms make it virtually impossible to assess their safety; the need for modeling has been urged for long. We here investigate the custom-designed 1-10% Fe-doped CuO NM library. Effects were assessed using the soil ecotoxicology model Enchytraeus crypticus (Oligochaeta) in the standard 21 days plus its extension (49 days). Results showed that 10%Fe-CuO was the most toxic (21 days reproduction EC50 = 650 mg NM/kg soil) and Fe3O4 NM was the least toxic (no effects up to 3200 mg NM/kg soil). All other NMs caused similar effects to E. crypticus (21 days reproduction EC50 ranging from 875 to 1923 mg NM/kg soil, with overlapping confidence intervals). Aiming to identify the key NM characteristics responsible for the toxicity, machine learning (ML) modeling was used to analyze the large data set [9 NMs, 68 descriptors, 6 concentrations, 2 exposure times (21 and 49 days), 2 endpoints (survival and reproduction)]. ML allowed us to separate experimental related parameters (e.g., zeta potential) from particle-specific descriptors (e.g., force vectors) for the best identification of important descriptors. We observed that concentration-dependent descriptors (environmental parameters, e.g., zeta potential) were the most important under standard test duration (21 day) but not for longer exposure (closer representation of real-world conditions). In the longer exposure (49 days), the particle-specific descriptors were more important than the concentration-dependent parameters. The longer-term exposure showed that the steepness of the concentration-response decreased with an increased Fe content in the NMs. Longer-term exposure should be a requirement in the hazard assessment of NMs in addition to the standard in OECD guidelines for chemicals. The progress toward ML analysis is desirable given its need for such large data sets and significant power to link NM descriptors to effects in animals. This is beyond the current univariate and concentration-response modeling analysis.

Atomistic insights into the morphological dynamics of gold and platinum nanoparticles: MD simulations in vacuum and aqueous media

The thermal response of gold and platinum spherical nanoparticles (NPs) upon cooling is studied through atomistic molecular dynamics simulations. The goal is to identify the morphological transformations occurring in the nanomaterials as well as to quantify their dependence on temperature, chemistry, and NP size. For diameters smaller than 3 nm, the transition temperature from a melted/amorphous to a highly crystalline state varies considerably with NP size. For larger NPs, the transition temperature is almost diameter-independent, yet it differs considerably from the transition temperature of the respective bulk materials. The platinum NPs possess a higher level of crystallinity than the gold counterparts under the same conditions because of the stronger cohesive forces that drive the crystallization process. This observation is also supported by the simulated X-ray powder diffraction patterns of the nanomaterials. The larger NPs have a multifaceted crystal surface, and their shape remains almost constant regardless of temperature variations. The smaller NPs have a smoother and more spherical surface, and their shape varies greatly with temperature. By studying the variation of nano-descriptors commonly employed in QSAR models, a qualitative picture of the NPs' toxicity and reactivity emerges: Small/hot NPs are likely more toxic than their large/cold counterparts. Because of the small size of the NPs considered, the observed structural modifications are challenging to be studied by experimental techniques. The present approach can be readily employed to study other metallic and metal oxide nanomaterials.

An ancestral molecular response to nanomaterial particulates

The varied transcriptomic response to nanoparticles has hampered the understanding of the mechanism of action. Here, by performing a meta-analysis of a large collection of transcriptomics data from various engineered nanoparticle exposure studies, we identify common patterns of gene regulation that impact the transcriptomic response. Analysis identifies deregulation of immune functions as a prominent response across different exposure studies. Looking at the promoter regions of these genes, a set of binding sites for zinc finger transcription factors C2H2, involved in cell stress responses, protein misfolding and chromatin remodelling and immunomodulation, is identified. The model can be used to explain the outcomes of mechanism of action and is observed across a range of species indicating this is a conserved part of the innate immune system.

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.

TiO2 nanoparticles' library toxicity (UV and non-UV exposure) - High-throughput in vivo transcriptomics reveals mechanisms

The hazards of nanomaterials/nanoparticles (NMs/NPs) are mostly assessed using individual NMs, and a more systematic approach, using many NMs, is needed to evaluate its risks in the environment. Libraries of NMs, with a range of identified different but related characters/descriptors allow the comparison of effects across many NMs. The effects of a custom designed Fe-doped TiO₂ NMs library containing 11 NMs was assessed on the soil model Enchytraeus crypticus (Oligochaeta), both with and without UV (standard fluorescent) radiation. Effects were analyzed at organism (phenotypic, survival and reproduction) and gene expression level (transcriptomics, high-throughput 4x44K microarray) to understand the underlying mechanisms. A total of 48 microarrays (20 test conditions) were done plus controls (UV and non-UV). Unique mechanisms induced by TiO₂ NPs exposure included the impairment in RNA processing for TiO₂_10nm, or deregulated apoptosis for 2%FeTiO₂_10nm. Strikingly apparent was the size dependent effects such as induction of reproductive effects via smaller TiO₂ NPs (≤12 nm) - embryo interaction, while larger particles (27 nm) caused reproductive effects through different mechanisms. Also, phagocytosis was affected by 12 and 27 nm NPs, but not by ≤11 nm. The organism level study shows the integrated response, i.e. the result after a cascade of events. While uni-cell models offer key mechanistic information, we here deliver a combined biological system level (phenotype and genotype), seldom available, especially for environmental models.

Basic and advanced spectrometric methods for complete nanoparticles characterization in bio/eco systems: current status and future prospects

The use of engineered nanoparticles in the environment and human life has increased in the last 20 years. The risk assessment concerning application of nanomaterials in biological systems requires their thorough characterization. Understanding the correlations between physicochemical properties of nanoparticles concerning not only the size, particle size distribution, number concentration, degree of aggregation, or agglomeration but also solubility, stability, binding affinity, surface activity, chemical composition, and nanoparticle synthesis yield allows their reliable characterization. Thus, to find the structure-function/property relationship of nanoparticles, multifaceted characterization approach based on more than one analytical technique is required. On the other hand, the increasing demand for identification and characterization of nanomaterials has contributed to the continuous development of spectrometric techniques which enables for their qualitative and quantitative analysis in complex matrices giving reproducible and reliable results. This review is aimed at providing a discussion concerning four main aspects of nanoparticle characterization: nanoparticle synthesis yield, particle size and number concentration, elemental and isotopic composition of nanoparticles, and their surface properties. The conventional and non-conventional spectrometric techniques such as spectrophotometry UV-Vis, mass spectrometric techniques working in conventional and single-particle mode, or those based on optical emission detection systems are described with special emphasis paid on their advantages and drawbacks. The application and recent advances of these methods are also comprehensively reviewed and critically discussed.

Multifunctional Metal Complex-based Gene Delivery for tumor immune checkpoint blockade combination therapy

Immune checkpoint blocking based on the PD-1/PD-L1 pathway has shown exciting results in various types of cancer. However, due to the off-target effect of PD-1/PD-L1 blocker, low tumor immunogenicity and tumor immunosuppressive microenvironment, a significant proportion of patients do not benefit from this treatment. Here, we constructed a novel multifunctional metal complex Fe/PEI-Tn by the coordination of polyethyleneimine (PEI) with Fe³⁺ and the modification of bifunctional peptides Tn containing the cell penetrating peptide (TAT) and nuclear localization signal peptide (NLS), which was coated with hyaluronic acid (HA) to prolong the circulation time in vivo. Fe/PEI-Tn can condensate PD-L1 trap plasmid (pPD-L1 trap) and mediate PD-L1 trap protein expression in tumor tissues in situ, thus blocking the PD-1/PD-L1 pathway. Besides, Fe/PEI-Tn metal complex itself can act as an immune adjuvant to activate macrophages, reverse the phenotype of pro-tumor M2-type macrophages, and promote anti-tumor immunity. Meanwhile, Fe/PEI-Tn treatment can induce damage in tumor cells and release tumor-specific antigens into tumor microenvironment, thus stimulating anti-tumor immune response. Studies showed that HA/Fe/PEI-Tn/pPD-L1 trap complexes could promote the immune activation of tumor tissues and effectively delay tumor growth. This strategy provides a new direction for tumor combination therapy based on PD-1/PD-L1 blockade.

Nano-pesticides: the lunch-box principle-deadly goodies (semio-chemical functionalised nanoparticles that deliver pesticide only to target species)

Nature contains many examples of "fake promises" to attract "prey", e.g., predatory spiders that emit the same sex-attractant-signals as moths to catch them at close range and male spiders that make empty silk-wrapped gifts in order to mate with a female. Nano-pesticides should ideally mimic nature by luring a target and killing it without harming other organisms/species. Here, we present such an approach, called the lunch-box or deadly-goodies approach. The lunch-box consists of three main elements (1) the lure (semio-chemicals anchored on the box), (2) the box (palatable nano-carrier), and (3) the kill (advanced targeted pesticide). To implement this approach, one needs to draw on the vast amount of chemical ecological knowledge available, combine this with recent nanomaterial techniques, and use novel advanced pesticides. Precision nano-pesticides can increase crop protection and food production whilst lowering environmental impacts.

Machine learning and materials modelling interpretation of in vivo toxicological response to TiO2 nanoparticles library (UV and non-UV exposure)

Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability. In the present study we assessed the effects of a custom design Fe-doped TiO₂ NPs library, using the soil invertebrate Enchytraeus crypticus (Oligochaeta), via a 5-day pulse via aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO₂, realistic conditions should include UV exposure. The 11 Fe-TiO₂ library contains NPs of size range between 5-27 nm with varying %Fe (enabling the photoactivation of TiO₂ at energy wavelengths in the visible-light range). The NPs were each described by 122 descriptors, being a mixture of measured and atomistic model descriptors. The data were explored using single and univariate statistical methods, combined with machine learning and multiscale modelling techniques. An iterative pruning process was adopted for identifying automatically the most significant descriptors. TiO₂ NPs toxicity decreased when combined with UV. Notably, the short-term water exposure induced lasting biological responses even after longer-term recovery in clean exposure. The correspondence with Fe-content correlated with the band-gap hence the reduction of UV oxidative stress. The inclusion of both measured and modelled materials data benefitted the explanation of the results, when combined with machine learning.

Ecotoxicological and regulatory aspects of environmental sustainability of nanopesticides

Recent years have seen the development of various colloidal formulations of pesticides and other agrochemicals aimed at use in sustainable agriculture. These formulations include inorganic, organic or hybrid particulates, or nanocarriers composed of biodegradable polymers, that can provide a better control of the release of active ingredients. The very small particle sizes and high surface areas of nanopesticides may however also lead to some unintended (eco)toxicological effects due to the way in which they interact with the target and non-target species and the environment. The current level of knowledge on ecotoxicological effects of nanopesticides is scarce, especially in regard to the fate and behaviour of such formulations in the environment. Nanopesticides will however have to cross a stringent regulatory scrutiny before marketing in most countries for health and environmental risks under a range of regulatory frameworks that require pre-market notification, risk assessment and approval, followed by labelling, post-market monitoring and surveillance. This review provides an overview of the key regulatory and ecotoxicological aspects relating to nanopesticides that will need to be considered for environmentally-sustainable use in agriculture.

Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity

Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.

Understanding Nanoparticle Toxicity to Direct a Safe-by-Design Approach in Cancer Nanomedicine

Nanomedicine is a rapidly growing field that uses nanomaterials for the diagnosis, treatment and prevention of various diseases, including cancer. Various biocompatible nanoplatforms with diversified capabilities for tumor targeting, imaging, and therapy have materialized to yield individualized therapy. However, due to their unique properties brought about by their small size, safety concerns have emerged as their physicochemical properties can lead to altered pharmacokinetics, with the potential to cross biological barriers. In addition, the intrinsic toxicity of some of the inorganic materials (i.e., heavy metals) and their ability to accumulate and persist in the human body has been a challenge to their translation. Successful clinical translation of these nanoparticles is heavily dependent on their stability, circulation time, access and bioavailability to disease sites, and their safety profile. This review covers preclinical and clinical inorganic-nanoparticle based nanomaterial utilized for cancer imaging and therapeutics. A special emphasis is put on the rational design to develop non-toxic/safe inorganic nanoparticle constructs to increase their viability as translatable nanomedicine for cancer therapies.

The investigation of the parameters affecting the ZnO nanoparticle cytotoxicity behaviour: a tutorial review

In the last 30 years the research about zinc oxide nanoparticles (ZnO NPs) and their related toxicity has shown a boom. ZnO NPs show cytotoxicity for both prokaryotic and eukaryotic cells and many studies demonstrated their selective toxicity towards cancer cells. However, with the increasing number of publications, it is observed an increase in the discrepancies obtained between the various results. Soon the scientific community understood that the ZnO NC toxicity behaviour is affected by many factors, related not only to the ZnO NPs themselves, but also to the experimental conditions used. Many recent reviews discussed these parameters by reporting experimental evidence and tried to assess the general statements about the ZnO NP cytotoxicity. This information is extremely useful for the evaluation of which type of ZnO NPs is more or less suitable for a specific study or application. However, despite that, a deep comprehension of the ZnO NP behaviour in relation to the different experimental conditions is still lacking. Actually, a full understanding of the reasons behind the NP behaviour is essential to better assess their biological activity and in particular their therapeutic application, avoiding undesired effects both in the experimental and clinical contexts. This tutorial review aims to be an experimental and practical guide for scientists that faced with the use of ZnO NPs for biomedical applications and, in particular, for their therapeutic purposes. The driving idea is to not simply summarize the results reported in the literature, but to provide instruments for a deep comprehension of the mechanisms affecting the ZnO NP cytotoxicity and behavior. This review also aims to point out the critical experimental parameters to be considered when working with these NPs, as well as the main related risks and limitations that scientists have to face.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

Fe2+/Fe3+ Ions Chelated with Ultrasmall Polydopamine Nanoparticles Induce Ferroptosis for Cancer Therapy

Ferroptosis, a promising mechanism of killing cancer cells, has become a research hotspot in cancer therapy. Besides, advantages of polymeric nanomaterials in improving anticancer efficacy and reducing side effect are widely accepted. In this work, based on the property of polypodamine to chelate metal ions, ultrasmall poly(ethylene glycol)-modified polydopamine nanoparticles, (UPDA-PEG)@Fe^2+/3+ nanoparticles, a novel ferroptosis agent, was rationally designed by chelating iron ions on ultrasmall polydopamine nanoparticles modified by PEG. This treatment led to a bigger specific surface area, which could support more reactive sites to chelate large number of iron ions, which is beneficial for exploring the detailed mechanism of ferroptosis-induced tumor cell death by iron ions. Also, the pH-dependent release of iron ions can reach approximately 70% at pH 5.0, providing the advantage of application in tumor microenvironment. The in vitro tests showed that the as-prepared NPs exhibit an effective anticancer effect on tumor cells including 4T1 and U87MG cells, yet ferric ions show a stronger ability of killing cancer cells than ferrous ions. Differences between ferrous ions and ferric ions in the ferroptosis pathway were monitored by the change of marker, including reactive oxygen species (ROS), glutathione peroxidase 4, and lipid peroxide (LPO), as well as the promoter and inhibitor of ferroptosis pathway. UPDA-PEG@Fe²⁺ nanoparticles induce ferroptosis that depends more on ROS; however, a more LPO-dependent ferroptosis is induced by UPDA-PEG@Fe³⁺ nanoparticles. Additionally, the in vivo studies using tumor-bearing Balb/c mice demonstrated that the as-prepared NPs could significantly inhibit tumor progression. UPDA-PEG@Fe^2+/3+ nanoparticles reported herein represent the nanoparticles related to iron ions for chemotherapy against cancer through the ferroptosis pathway.

In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches

Rational nanomaterial design is urgently demanded for new nanomaterial development with desired properties. However, computational nanomaterial modeling and virtual nanomaterial screening are not applicable for this purpose due to the complexity of nanomaterial structures. To address this challenge, a new computational workflow is established in this study to virtually profile nanoparticles by (1) constructing a structurally diverse virtual gold nanoparticle (GNP) library and (2) developing novel universal nanodescriptors. The emphasis of this study is the second task by developing geometrical nanodescriptors that are suitable for the quantitative modeling of GNPs and virtual screening purposes. The feasibility, rigor and applicability of this novel computational method are validated by testing seven GNP datasets consisting of 191 unique GNPs of various nano-bioactivities and physicochemical properties. The high predictability of the developed GNP models suggests that this workflow can be used as a universal tool for nanomaterial profiling and rational nanomaterial design.