Modelling the biodistribution of inhaled gold nanoparticles in rats with interspecies extrapolation to humans

Overview of in vitro-in vivo extrapolation approaches for the risk assessment of nanomaterial toxicity

Nanomaterials are increasingly used in many applications due to their enhanced properties. To ensure their safety for humans and the environment, nanomaterials need to be evaluated for their potential risk. The risk assessment analysis on the nanomaterials based on animal or in vivo studies is accompanied by several concerns, including animal welfare, time and cost needed for the studies. Therefore, incorporating in vitro studies in the risk assessment process is increasingly considered. To be able to analyze the potential risk of nanomaterial to human health, there are factors to take into account. Utilizing in vitro data in the risk assessment analysis requires methods that can be used to translate in vitro data to predict in vivo phenomena (in vitro-in vivo extrapolation (IVIVE) methods) to be incorporated, to obtain a more accurate result. Apart from the experiments and species conversion (for example, translation between the cell culture, animal and human), the challenge also includes the unique properties of nanomaterials that might cause them to behave differently compared to the same materials in a bulk form. This overview presents the IVIVE techniques that are developed to extrapolate pharmacokinetics data or doses. A brief example of the IVIVE methods for chemicals is provided, followed by a more detailed summary of available IVIVE methods applied to nanomaterials. The IVIVE techniques discussed include the comparison between in vitro and in vivo studies, methods to rene the dose metric or the in vitro models, allometric approach, mechanistic modeling, Multiple-Path Particle Dosimetry (MPPD), methods using organ burden data and also approaches that are currently being developed.

Recent Progress on Physiologically Based Pharmacokinetic (PBPK) Model: A Review Based on Bibliometrics

Physiologically based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are designed to elucidate the mechanism of chemical compound action in organisms based on the physiological, biochemical, anatomical, and thermodynamic properties of organisms. After nearly a century of research and practice, good results have been achieved in the fields of medicine, environmental science, and ecology. However, there is currently a lack of a more systematic review of progress in the main research directions of PBPK models, especially a more comprehensive understanding of the application in aquatic environmental research. In this review, a total of 3974 articles related to PBPK models from 1996 to 24 March 2024 were collected. Then, the main research areas of the PBPK model were categorized based on the keyword co-occurrence maps and cluster maps obtained by CiteSpace. The results showed that research related to medicine is the main application area of PBPK. Four major research directions included in the medical field were "drug assessment", "cross-species prediction", "drug-drug interactions", and "pediatrics and pregnancy drug development", in which "drug assessment" accounted for 55% of the total publication volume. In addition, bibliometric analyses indicated a rapid growth trend in the application in the field of environmental research, especially in predicting the residual levels in organisms and revealing the relationship between internal and external exposure. Despite facing the limitation of insufficient species-specific parameters, the PBPK model is still an effective tool for improving the understanding of chemical-biological effectiveness and will provide a theoretical basis for accurately assessing potential risks to ecosystems and human health. The combination with the quantitative structure-activity relationship model, Bayesian method, and machine learning technology are potential solutions to the previous research gaps.

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.

A Mitochondrial Nanoguard Modulates Redox Homeostasis and Bioenergy Metabolism in Diabetic Peripheral Neuropathy

As a major late complication of diabetes, diabetic peripheral neuropathy (DPN) is the primary reason for amputation. Nevertheless, there are no wonder drugs available. Regulating dysfunctional mitochondria is a key therapeutic target for DPN. Resveratrol (RSV) is widely proven to guard mitochondria, yet the unsatisfactory bioavailability restricts its clinical application. Tetrahedral framework nucleic acids (tFNAs) are promising carriers due to their excellent cell entrance efficiency, biological safety, and structure editability. Here, RSV was intercalated into tFNAs to form the tFNAs-RSV complexes. tFNAs-RSV achieved enhanced stability, bioavailability, and biocompatibility compared with tFNAs and RSV alone. With its treatment, reactive oxygen species (ROS) production was minimized and reductases were activated in an in vitro model of DPN. Besides, respiratory function and adenosine triphosphate (ATP) production were enhanced. tFNAs-RSV also exhibited favorable therapeutic effects on sensory dysfunction, neurovascular deterioration, demyelination, and neuroapoptosis in DPN mice. Metabolomics analysis revealed that redox regulation and energy metabolism were two principal mechanisms that were impacted during the process. Comprehensive inspections indicated that tFNAs-RSV inhibited nitrosation and oxidation and activated reductase and respiratory chain. In sum, tFNAs-RSV served as a mitochondrial nanoguard (mito-guard), representing a viable drilling target for clinical drug development of DPN.

A structure-activity approach towards the toxicity assessment of multicomponent metal oxide nanomaterials

The increase of human and environmental exposure to engineered nanomaterials (ENMs) due to the emergence of nanotechnology has raised concerns over their safety. The challenging nature of in vivo and in vitro toxicity assessment methods for ENMs, has led to emerging in silico techniques for ENM toxicity assessment, such as structure-activity relationship (SAR) models. Although such approaches have been extensively developed for the case of single-component nanomaterials, the case of multicomponent nanomaterials (MCNMs) has not been thoroughly addressed. In this paper, we present a SAR approach for the case metal and metal oxide MCNMs. The developed SAR framework is built using a dataset of 796 individual toxicity measurements for 340 different MCNMs, towards human cells, mammalian cells, and bacteria. The novelty of the approach lies in the multicomponent nature of the nanomaterials, as well as the size, diversity and heterogeneous nature of the dataset used. Furthermore, the approach used to calculate descriptors for surface loaded MCNMs, and the mechanistic insight provided by the model results can assist the understanding of MCNM toxicity. The developed models are able to correctly predict the toxic class of the MCNMs in the heterogeneous dataset, towards a wide range of human cells, mammalian cells and bacteria. Using the abovementioned approach, the principal toxicity pathways and mechanisms are identified, allowing a more holistic understanding of metal oxide MCNM toxicity.