The Journal of Toxicological Sciences
Online ISSN : 1880-3989
Print ISSN : 0388-1350
ISSN-L : 0388-1350
Current issue
Displaying 1-4 of 4 articles from this issue
Review
  • Nasir Khan, Jean Sathish, Cynthia M Rohde
    2024 Volume 49 Issue 3 Pages 79-94
    Published: 2024
    Released on J-STAGE: March 01, 2024
    JOURNAL FREE ACCESS FULL-TEXT HTML

    The development and regulatory review of BNT162b2, a COVID-19 vaccine, and PaxlovidTM (nirmatrelvir tablets/ritonavir tablets), a COVID-19 therapeutic, are benchmarks for accelerated innovation during a global pandemic. Rapid choice of the SARS-CoV-2 spike protein and main protease (Mpro) as targets for the vaccine and therapeutic, respectively, leveraged the available knowledge of the biology of SARS-CoV-2 and related viruses. The nonclinical immunogenicity and safety of BNT162b2 was rigorously assessed. Likewise, a comprehensive nonclinical safety assessment was conducted for the therapeutic candidates, lufotrelvir (PF-07304814) and nirmatrelvir (PF-07321332). The development and regulatory review of BNT162b2 and Paxlovid was enabled through close collaboration of the pharmaceutical industry with regulatory agencies and public health organizations. This experience highlights approaches that could be adopted for pandemic preparedness including risk-based investment strategies, conduct of activities in parallel that normally are conducted sequentially, quick kill decisions, simultaneous evaluation of multiple candidates, and use of flexible, established vaccine platforms.

Original Article
  • Toshihisa Koga, Yuko Sahara, Tadaaki Ohtani, Kaneko Yosuke, Ken Umehar ...
    2024 Volume 49 Issue 3 Pages 95-103
    Published: 2024
    Released on J-STAGE: March 01, 2024
    JOURNAL FREE ACCESS FULL-TEXT HTML
    Supplementary material

    This study was conducted as part of an investigation into the cause of vesnarinone-associated agranulocytosis. When HL-60 cells were exposed to vesnarinone for 48 hr, little cytotoxicity was observed, although reduced glutathione (GSH) content decreased in a concentration-dependent manner. Significant cytotoxicity and reactive oxygen species (ROS) production were observed when intracellular GSH content was reduced by treatment with L-buthionine-(S, R)-sulphoximine. The involvement of myeloperoxidase (MPO) metabolism was suggested, as when HL-60 cells were exposed to a reaction mixture of vesnarinone–MPO/H2O2/Cl, cytotoxicity was also observed. In contrast, the presence of GSH (1 mM) protected against these cytotoxic effects. Liquid chromatography–mass spectrometry analysis of the MPO/H2O2/Cl reaction mixture revealed that vesnarinone was converted into two metabolites, (4-(3,4-dimethoxybenzoyl)piperazine [Metabolite 1: M1] and 1-chloro-4-(3,4-dimethoxybenzoyl)piperazine [Metabolite 2: M2]). M2 was identified as the N-chloramine form, a reactive metabolite of M1. Interestingly, M2 was converted to M1, which was accompanied by the conversion of GSH to oxidized GSH (GSSG). Furthermore, when HL-60 cells were exposed to synthetic M1 and M2 for 24 hr, M2 caused dose-dependent cytotoxicity, whereas M1 did not. Cells were protected from M2-derived cytotoxicity by the presence of GSH. In conclusion, we present the first demonstration of the cytotoxic effects and ROS production resulting from the MPO/H2O2/Cl metabolic reaction of vesnarinone and newly identified the causative metabolite, M2, as the N-chloramine metabolite of M1, which induces cytotoxicity in HL-60 cells. Moreover, a protective role of GSH against the cytotoxicity was revealed. These findings suggest a possible nonimmunological cause of vesnarinone agranulocytosis.

Original Article
  • Takeshi Hase, Samik Ghosh, Ken-ichi Aisaki, Satoshi Kitajima, Jun Kann ...
    2024 Volume 49 Issue 3 Pages 105-115
    Published: 2024
    Released on J-STAGE: March 01, 2024
    JOURNAL FREE ACCESS FULL-TEXT HTML
    Supplementary material

    With the advancement of large-scale omics technologies, particularly transcriptomics data sets on drug and treatment response repositories available in public domain, toxicogenomics has emerged as a key field in safety pharmacology and chemical risk assessment. Traditional statistics-based bioinformatics analysis poses challenges in its application across multidimensional toxicogenomic data, including administration time, dosage, and gene expression levels. Motivated by the visual inspection workflow of field experts to augment their efficiency of screening significant genes to derive meaningful insights, together with the ability of deep neural architectures to learn the image signals, we developed DTox, a deep neural network-based in visio approach. Using the Percellome toxicogenomics database, instead of utilizing the numerical gene expression values of the transcripts (gene probes of the microarray) for dose-time combinations, DTox learned the image representation of 3D surface plots of distinct time and dosage data points to train the classifier on the experts’ labels of gene probe significance. DTox outperformed statistical threshold-based bioinformatics and machine learning approaches based on numerical expression values. This result shows the ability of image-driven neural networks to overcome the limitations of classical numeric value-based approaches. Further, by augmenting the model with explainability modules, our study showed the potential to reveal the visual analysis process of human experts in toxicogenomics through the model weights. While the current work demonstrates the application of the DTox model in toxicogenomic studies, it can be further generalized as an in visio approach for multi-dimensional numeric data with applications in various fields in medical data sciences.

Original Article
  • Yoshinobu Igarashi, Ryosuke Kojima, Shigeyuki Matsumoto, Hiroaki Iwata ...
    2024 Volume 49 Issue 3 Pages 117-126
    Published: 2024
    Released on J-STAGE: March 01, 2024
    JOURNAL FREE ACCESS FULL-TEXT HTML

    Mitochondrial toxicity has been implicated in the development of various toxicities, including hepatotoxicity. Therefore, mitochondrial toxicity has become a major screening factor in the early discovery phase of drug development. Several models have been developed to predict mitochondrial toxicity based on chemical structures. However, they only provide a binary classification of positive or negative results and do not provide the substructures that contribute to a positive decision. Therefore, we developed an artificial intelligence (AI) model to predict mitochondrial toxicity and visualize structural alerts. To construct the model, we used the open-source software library kMoL, which employs a graph neural network approach that allows learning from chemical structure data. We also utilized the integrated gradient method, which enables the visualization of substructures that contribute to positive results. The dataset used to construct the AI model exhibited a significant imbalance, with significantly more negative than positive data. To address this, we employed the bagging method, which resulted in a model with high predictive performance, as evidenced by an F1 score of 0.839. This model can also be used to visualize substructures that contribute to mitochondrial toxicity using the integrated gradient method. Our AI model predicts mitochondrial toxicity based on chemical structures and may contribute to screening mitochondrial toxicity in the early stages of drug discovery.

feedback
Top