The effect of biocide chloromethylisothiazolinone/methylisothiazolinone (CMIT/MIT) mixture on C2C12 muscle cell damage attributed to mitochondrial reactive oxygen species overproduction and autophagy activation DOI
Dong‐Min Kim, Yusun Shin, Yong-Wook Baek

et al.

Journal of Toxicology and Environmental Health, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 15

Published: Oct. 24, 2024

The mixture of 5-chloro-2-methyl-4-isothiazolin-3-one and 2-methyl-4-isothiazolin-3-one (CMIT/MIT) is a biocide widely used as preservative in various commercial products. This has also been an active ingredient humidifier disinfectants South Korea, resulting serious health effects among users. Recent evidence suggests that the underlying mechanism CMIT/MIT-initiated toxicity might be associated with defects mitochondrial functions. aim this study was to utilize C2C12 skeletal muscle model investigate CMIT/MIT on function relevant molecular pathways dysfunction. Data demonstrated exposure during myogenic differentiation induced significant excess production reactive oxygen species (ROS) decrease intracellular ATP levels. Notably, significantly inhibited oxidative phosphorylation (Oxphos) reduced mass at lower concentration than amount, which diminished viability myotubes. activation autophagy flux decreased protein expression levels myosin heavy chain (MHC). Taken together, produced damage myotubes by impairing bioenergetics activating autophagy. Our findings contribute increased understanding mechanisms CMIT/MIT-induced adverse effects.

Language: Английский

Mathematical Model for Combined Toxicity Prediction DOI Open Access

Fasiha Javaid,

De‐Sheng Pei

Published: Jan. 14, 2025

This chapter delves into the vital realm of combined toxicity prediction, crucial for environmental health and risk assessment. It outlines significance predicting toxicity, exploring different types interactions like synergistic, antagonistic, additive effects their impact on The provides an extensive overview mathematical models used in categorizing them concentration addition (CA), response (RA), independent action (IA), others. evaluates commonly models, such as assessment (RA) interaction-based machine learning/AI-based detailing mechanisms applications. Moreover, it discusses evaluation selection criteria, guiding readers choosing most appropriate model specific scenarios. Future directions research needs are also addressed, highlighting emerging trends potential integration computational approaches prediction. In conclusion, this offers a comprehensive insight aiding hazard across various domains.

Language: Английский

Citations

0
Assessing the environmental risks of sulfonylurea pollutants: Insights into the risk priority and structure-toxicity relationships DOI Creative Commons

Zhi-Cong He,

Tao Zhang,

Xin-Fang Lu

et al.

Ecotoxicology and Environmental Safety, Journal Year: 2025, Volume and Issue: 292, P. 117973 - 117973

Published: Feb. 27, 2025

Language: Английский

Citations

0
Predicting non-chemotherapy drug-induced agranulocytosis toxicity through ensemble machine learning approaches DOI Creative Commons

Xiaojie Huang,

Xiaochun Xie,

Shaokai Huang

et al.

Frontiers in Pharmacology, Journal Year: 2024, Volume and Issue: 15

Published: Aug. 14, 2024

Agranulocytosis, induced by non-chemotherapy drugs, is a serious medical condition that presents formidable challenge in predictive toxicology due to its idiosyncratic nature and complex mechanisms. In this study, we assembled dataset of 759 compounds applied rigorous feature selection process prior employing ensemble machine learning classifiers forecast drug-induced agranulocytosis (NCDIA) toxicity. The balanced bagging classifier combined with gradient boosting decision tree (BBC + GBDT), utilizing the descriptor set DS RDKit comprising 237 features, emerged as top-performing model, an external validation AUC 0.9164, ACC 83.55%, MCC 0.6095. model's reliability was further substantiated applicability domain analysis. Feature importance, assessed through permutation importance within BBC GBDT highlighted key molecular properties significantly influence NCDIA Additionally, 16 structural alerts identified SARpy software revealed potential signatures associated toxicity, enriching our understanding underlying We also constructed models assess toxicity novel drugs approved FDA. This study advances providing framework mitigate risks, ensuring safety pharmaceutical development facilitating post-market surveillance new drugs.

Language: Английский

Citations

2
Assessing the Environmental Risks of Sulfonylurea Pollutants: Insights into the Risk Priority Ranking and Structure-Toxicity Relationships Explorations DOI

Wei Peng,

Zhi-Cong He,

Tao Zhang

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
AI in Predictive Toxicology DOI
Bancha Yingngam

Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 79 - 134

Published: Sept. 14, 2024

The field of toxicology is undergoing a significant transformation due to the integration artificial intelligence (AI). In addition traditional reliance on empirical studies and animal testing, AI-powered predictive now used predict toxic effects chemicals drugs. This chapter examines role AI in enhancing accuracy, efficiency, breadth toxicological assessments by bridging gap between approaches advanced techniques. It explores various methodologies, such as machine learning, deep neural networks, focusing their application toxicity prediction. Furthermore, this investigates with databases development validation models. also addresses challenges associated toxicology, including data quality, model interpretability, scalability. concludes that despite facing challenges, powerful tool modern analysis.

Language: Английский

Citations

0
Assessing the Environmental Risks of Sulfonylurea Pollutants: Insights into the Risk Priority and Structure-Toxicity Relationships Explorations DOI

Zhi-Cong He,

Tao Zhang,

Xin-Fang Lu

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
The effect of biocide chloromethylisothiazolinone/methylisothiazolinone (CMIT/MIT) mixture on C2C12 muscle cell damage attributed to mitochondrial reactive oxygen species overproduction and autophagy activation DOI
Dong‐Min Kim, Yusun Shin, Yong-Wook Baek

et al.

Journal of Toxicology and Environmental Health, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 15

Published: Oct. 24, 2024

The mixture of 5-chloro-2-methyl-4-isothiazolin-3-one and 2-methyl-4-isothiazolin-3-one (CMIT/MIT) is a biocide widely used as preservative in various commercial products. This has also been an active ingredient humidifier disinfectants South Korea, resulting serious health effects among users. Recent evidence suggests that the underlying mechanism CMIT/MIT-initiated toxicity might be associated with defects mitochondrial functions. aim this study was to utilize C2C12 skeletal muscle model investigate CMIT/MIT on function relevant molecular pathways dysfunction. Data demonstrated exposure during myogenic differentiation induced significant excess production reactive oxygen species (ROS) decrease intracellular ATP levels. Notably, significantly inhibited oxidative phosphorylation (Oxphos) reduced mass at lower concentration than amount, which diminished viability myotubes. activation autophagy flux decreased protein expression levels myosin heavy chain (MHC). Taken together, produced damage myotubes by impairing bioenergetics activating autophagy. Our findings contribute increased understanding mechanisms CMIT/MIT-induced adverse effects.

Language: Английский

Citations

0