Current opinion in toxicology最新文献

Replacement, reduction, and refinement, known as the 3Rs, is a well-known and broadly applied concept in biomedical science. Since its formal introduction in 1959, application of the 3Rs have refined approaches to reduce or eliminate distress, reduced overall use through repeated measures approaches and use of appropriate numbers of animals, or have replaced animals altogether. However, adoption of the 3Rs is not always easy, due in part to initial lack of awareness of the importance of animal well-being to the outcome of scientific investigation, a lack of understanding of use of results from in vivo model verses in vitro models, lack of effective communication about model benefits, or other technical issues with the development or use of an assay. In understanding the history of the 3Rs, we can learn from or avoid previous challenges as we look to the future of the application of the 3Rs. For instance, there is little doubt that awareness and use of the 3Rs influenced the concept of new approach methods (NAMs), a term describing assays that include traditional in vitro cultures, microphysiological systems, and in silico approaches, and is certainly part of the 3Rs future and the ultimate replacement of an animal-based assay.

Developmental toxicity outcomes in humans and animals often exhibit variability; hence, the demand for predictive non-animal alternatives, particularly human cell-based models, are increasing. Despite advancements in genomic toxicology, which have facilitated the identification of toxicity mechanisms and potential biomarkers, existing transcriptome analysis-based methods have yet to yield highly predictive in vitro developmental toxicity assays. One possible reason is that assays at a single time point could not capture the entire dynamic signal network during developmental processes. This article addresses the challenges in comprehensive gene expression analysis and introduces novel in vitro developmental toxicity assays focused on the time-dependent dynamics of signaling pathway responses crucial to human development.

A variety of new approach methodologies (NAMs) have already been developed for acute systemic and short-term toxicity, including in vitro, in silico, and omics methods. To advance their regulatory implementation, we suggest that beta testing of these methods in regulatory settings is urgently needed. There are several limitations to the use of NAMs for acute systemic and short-term toxicity, such as the lack of definitions for applicability domains, skewed reference data for validation, and the absence of representation of kinetic processes and multi-organ complexity. These limitations may lead to risks associated with the ordinary regulatory implementation, such as the application of methods to substances outside of their intended applicability domain or reduced predictivity due to a lack of mechanistic information or consideration of kinetics. We argue that this could be avoided by beta testing. Further benefits of beta testing would be the filling of in vivo data gaps and potentially improved validation with regard to human relevance of methods. In order to enhance the improvement, familiarisation, and acceptance of NAMs in the near future, it is essential for such concept of beta testing to rely on feedback loops between method testers and developers.

Recent developments of novel single-cell analysis techniques have rapidly advanced the fields of immunotoxicology and immunometabolism. Single-cell analyses enable the characterization of immune cells, unraveling heterogeneity, and population dynamics in response to cellular perturbations, including toxicant insults and changes in cellular metabolism. This review provides an overview of current technologies and recent discoveries, illustrating an emerging role of single-cell analyses in the field of immunotoxicology and immunometabolism. Various single-cell techniques, including flow cytometry, mass cytometry, multiplexed imaging, and sequencing, together with their applications to studying immunotoxicology and immunometabolism are discussed. This review emphasizes the potential for single-cell analyses to revolutionize our understanding of immune cell heterogeneity, uncover novel cellular therapeutic targets, and pave the way for novel mechanistic insights.

Exposure to environmental chemicals has been associated with increased risks for various diseases, but our understanding of their molecular targets and how they drive disease progression remains limited. Environmental toxicants can trigger a multitude of effects on the epigenome, transcriptome, proteome, and other molecular entities in individual cells and tissues. The recent advances in high throughput single cell multiomics technologies are enabling a deeper understanding of these complex molecular alterations and interactions underlying exposure mode of action at a single cell resolution. Accompanying the increased capacity to generate single cell multiomics data is the rapid advancement in computational tools for data analysis of individual omics layers, multimodal data integration and molecular network modeling. Recent applications of single cell omics technologies and analytical methods have enabled the elucidation of cell type specific genes and pathways affected by various environmental exposures. Further adoption of advanced single cell multiomics methodologies in the molecular toxicology field promises a more comprehensive understanding of the regulatory networks within and between cell types underlying the perturbations in physiological systems and disease risks posed by environmental toxicants.

The discovery of cancer-specific therapeutics and determining their sensitivity is a critical step in preventing drug-induced toxicity. Drug sensitivity varies among cancer patients due to intra-tumor heterogeneity. It demands rational drug design, target identification, and novel treatment modalities. This review discusses the use of network medicine in targeted therapy and AI-based drug response prediction for personalized cancer therapy. The network medicine is successfully implemented to integrate multiple omics data to identify the disease modules in cancer. The cancer-specific disease modules are utilized for drug screening and targeted therapy. Additionally, the model developed using AI, and genomic data shows superior performance and also reveals relationships between the genomic variability of cancer and their response to drugs. There is significant promise for network medicine and AI to handle large-scale omics data, leading to the identification of a novel cancer-specific treatment strategy and improved patient care.

The application and analysis of single-cell transcriptomics in toxicology presents unique challenges. These include identifying cell sub-populations sensitive to perturbation; interpreting dynamic shifts in cell type proportions in response to chemical exposures; and performing differential expression analysis in dose–response studies spanning multiple treatment conditions. This review examines these challenges while presenting best practices for critical single cell analysis tasks. This covers areas such as cell type identification; analysis of differential cell type abundance; differential gene expression; and cellular trajectories. Towards enhancing the use of single-cell transcriptomics in toxicology, this review aims to address key challenges in this field and offer practical analytical solutions. Overall, applying appropriate bioinformatic techniques to single-cell transcriptomic data can yield valuable insights into cellular responses to toxic exposures.