The Journal of Toxicological Sciences
Online ISSN : 1880-3989
Print ISSN : 0388-1350
ISSN-L : 0388-1350
Volume 47, Issue 10
Displaying 1-4 of 4 articles from this issue
Original Article
  • ZheZhe Guan, YaLan Li, ShaoCong Hu, CaiFeng Mo, DongLing He, Zhi Huang ...
    2022 Volume 47 Issue 10 Pages 389-407
    Published: 2022
    Released on J-STAGE: September 14, 2022
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    Trimeresurus stejnegeri is one of the top ten venomous snakes in China, and its bite causes acute and severe diseases. Elucidating the metabolic changes of the body caused by Trimeresurus stejnegeri bite will be beneficial to the diagnosis and treatment of snakebite. Thus, an animal pig model of Trimeresurus stejnegeri bite was established, and then the metabolites of serum and urine were subsequently screened and identified in both ESI+ and ESI- modes identified by ultra-performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS) methods. There are 9 differential metabolites in serum, including Oleic acid, Lithocholic acid, Deoxycholic acid, Hypoxanthine, etc. There are 11 differential metabolites in urine, including Dopamine, Thiocysteine, Arginine, Indoleacetaldehyde, etc. Serum enrichment pathway analysis showed that 5 metabolic pathways, including Tryptophanuria, Liver disease due to cystic fibrosis, Hartnup disease, Hyperbaric oxygen exposure and Biliary cirrhosis, the core metabolites in these pathways, including deoxycholic acid, lithocholic acid, tryptophan and hypoxanthine, changed significantly. Urine enrichment pathway analysis showed that 4 metabolic pathways, including Aromatic L-Amino Acid Decarboxylase, Vitiligo, Blue Diaper Syndrome and Hyperargininemia, the core metabolites in these pathways including dopamine, 5-hydroxyindole acetic acid and arginine. Taken together, the current study has successfully established an animal model of Trimeresurus stejnegeri bite, and identified the metabolic markers and metabolic pathways of Trimeresurus stejnegeri bite. These metabolites and pathways may have potential application value and provide a therapeutic basis for the treatment of Trimeresurus stejnegeri bite.

Original Article
  • Takuya Kikuchi, Shunta Shigemura, Yuichi Ito, Kazutoshi Saito
    2022 Volume 47 Issue 10 Pages 409-420
    Published: 2022
    Released on J-STAGE: October 01, 2022
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    Certain polyphenols exhibit low permeability; precise prediction of their intestinal absorption is important for understanding internal exposure in humans. Intestinal availability, which represents the fraction of administered compounds that reach the portal blood (FaFg), is calculated by dividing bioavailability (F) by hepatic availability (Fh), and F is obtained from pharmacokinetic data from both intravenous (i.v.) and oral (p.o.) administration. However, human FaFg of polyphenols is hardly reported, as human i.v. data are extremely scarce. In this study, we developed an estimation method for FaFg of polyphenols in humans based on the extrapolation of rat clearance using allometric scaling (allometric scaling-based FaFg calculation method, AS- FaFgCM). First, for quercetin, for which human i.v. data have been reported, we compared the FaFg obtained by AS-FaFgCM with the traditional approach using human i.v. and p.o. data. Less than two-fold difference in FaFg values was observed between the two approaches. Next, we obtained FaFg of structurally diverse polyphenols (genistein, baicalein, resveratrol, and epicatechin) using AS-FaFgCM, demonstrating that all of them were poorly absorbable. Furthermore, to utilize the pharmacokinetic data of the total concentration, including aglycones and metabolites, we modified the AS-FaFgCM to focus on their excretion. The FaFg value of naringenin was obtained using modified AS-FaFgCM and was nearly equal to that of baicalein, a structural isomer of naringenin. This study provides quantitative information on the intestinal absorption of polyphenols using comprehensive estimation methods.

Letter
  • Chihiro Morita, Yuki Tokunaga, Yuto Ueda, Masateru Ono, Hideki Kinoshi ...
    2022 Volume 47 Issue 10 Pages 421-428
    Published: 2022
    Released on J-STAGE: October 01, 2022
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    Acetaminophen (APAP) and p-aminophenol (p-AP) are the analogous simple phenolic compounds that undergo sulfate conjugation (sulfation) by cytosolic sulfotransferases. Sulfation is generally thought to lead to the inactivation and disposal of endogenous as well as xenobiotic compounds. This study aimed to investigate the antioxidative effects of O-sulfated form of APAP and p-AP, i.e., APAPS and p-APS, in comparison with their unsulfated counterparts. Using a 1,1-diphenyl-2-picrylhydrazyl radical scavenging assay, the antioxidant capacity of APAPS was shown to be approximately 126-times lower than that of APAP. In contrast, p-APS displayed comparable activity as unsulfated p-AP. Similar trends concerning the suppressive effects of these chemicals on cellular O2- radical generation were found using an activated granulocytic neutrophil cell model. Collectively, these results indicated that, depending on the presence of an additional “active site”, sulfation may not always decrease the antioxidant activities of phenolic compounds.

Letter
  • Yuto Ishibashi, Shingo Kimura, Ikuro Suzuki
    2022 Volume 47 Issue 10 Pages 429-437
    Published: 2022
    Released on J-STAGE: October 01, 2022
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    Antibiotic-associated encephalopathy (AAE) is a central nervous system disorder caused by antibiotics administration and classified into three types based on clinical symptoms. Type 1 AAE causes seizures and myoclonus, type 2 causes psychiatric symptoms, and type 3 is characterized by cerebellar ataxia. In this study, we investigated whether the electrical activity of in vitro human iPSC-derived neurons to antibiotics could be classified based on the 3 types of AAEs classified by clinical symptoms. Glutamatergic, GABAergic neurons and astrocytes differentiated from human iPS cells were seeded on micro-electrode array (MEA). The cumulative administration of 13 different antimicrobials detected changes in neural activity that differed according to AAE type. Next, we classified the antimicrobials by principal component analysis (PCA) and confirmed the AAE type of each agent. We found that Types 1–3 AAE agents were distributed separately. The classification of antibiotics depending on electrophysiological response characteristics was consistent with the clinical practice classification of AAEs. In conclusion, the combination of electrophysiological responses of human iPS cell-derived neural networks measured by MEA plus multivariate analysis methods will effectively detect and classify antibiotics developmental risks.

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