In silico prediction of parkinsonian motor deficits-related neurotoxicants based on the adverse outcome pathway concept

Kan, Hung-Lin; Tung, Chun Wei; Chang, Shao-En; Lin, Ying

doi:10.1007/s00204-022-03376-1

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…The approach to utilize AOPs can relate an exposure to a molecular initiating event, which links to a key event that can form an AOP network (Hemmerich and Ecker, 2020). Some examples have been successfully applied to neurotoxicity studies in producing models that predict xenobiotics agents that can cause deleterious effects, such as Parkinsonian motor deficits (Kan et al, 2022;Dong et al, 2023). While AOPs tend to focus mainly on mechanistic understandings for prediction, the IATA represent a broader framework that integrates data and methods from various sources, including AOPs.…”

Section: Risk Assessmentmentioning

confidence: 99%

Machine learning in toxicological sciences: opportunities for assessing drug toxicity

Tonoyan,

Siraki

2024

Front. Drug Discov.

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Machine learning (ML) in toxicological sciences is growing exponentially, which presents unprecedented opportunities and brings up important considerations for using ML in this field. This review discusses supervised, unsupervised, and reinforcement learning and their applications to toxicology. The application of the scientific method is central to the development of a ML model. These steps involve defining the ML problem, constructing the dataset, transforming the data and feature selection, choosing and training a ML model, validation, and prediction. The need for rigorous models is becoming more of a requirement due to the vast number of chemicals and their interaction with biota. Large datasets make this task possible, though selecting databases with overlapping chemical spaces, amongst other things, is an important consideration. Predicting toxicity through machine learning can have significant societal impacts, including enhancements in assessing risks, determining clinical toxicities, evaluating carcinogenic properties, and detecting harmful side effects of medications. We provide a concise overview of the current state of this topic, focusing on the potential benefits and challenges related to the availability of extensive datasets, the methodologies for analyzing these datasets, and the ethical implications involved in applying such models.

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Section: Risk Assessmentmentioning

confidence: 99%

Machine learning in toxicological sciences: opportunities for assessing drug toxicity

Tonoyan,

Siraki

2024

Front. Drug Discov.

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“…Complex I malfunctions in mitochondria: In Parkinson's disease, genetic polymorphisms attributed to the mitochondria, as well as environmental toxins such as rotenone and MPTP, cause breakdowns in the electron transport system, resulting in higher oxidative stress, cellular Ca 2+ buildup, glutamate excitotoxicity, and a decline in producing energy, all of which contribute to neuronal injuries and loss. Because a minor amount of superoxide anion is formed during the reduction of molecular oxygen to water, the ETC is the main cause of ROS.Through oxidative phosphorylation, the ETC generates ATP 32 . Various blockers of complex I that cause dopaminergic neurotoxicities, such as rotenone, trichloroethylene, and fenpyroximate, show that mitochondrial impairment plays a significant role in PD.…”

Section: Parkinson's Disorder Pathogenesis and Mitochondrial Malfunctionmentioning

confidence: 99%

“…Through oxidative phosphorylation, the ETC generates ATP. 32 Various blockers of complex I that cause dopaminergic neurotoxicities, such as rotenone, trichloroethylene, and fenpyroximate, show that mitochondrial impairment plays a significant role in PD. Such contaminants lead to failures in the operation of the mitochondrial ETC, minimize mitochondrial mobility, 33 enhance the mitochondrial permeation transition, boost the production of ROS, and increase the expression of nitric oxide (NO) synthase in the mitochondria.…”

Section: Mitochondrial Abnormalities Involved With Neurodegenerative ...mentioning

confidence: 99%

Potential applications of mitochondrial therapy with a focus on Parkinson's disease and mitochondrial transplantation

Wal,

Vig

et al. 2023

Adv Pharm Bull

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Purpose: Both aging and neurodegenerative illnesses are thought to be influenced by mitochondrial malfunction and free radical formation. Deformities of the energy metabolism, mitochondrial genome polymorphisms, nuclear DNA genetic abnormalities associated with mitochondria, modifications of mitochondrial fusion or fission, variations in shape and size, variations in transit, modified mobility of mitochondria, transcription defects, and the emergence of misfolded proteins associated with mitochondria are all linked to Parkinson's disease. Method: This review is a condensed compilation of data from research that have been published between the years of 2014 and 2022, using search engines like Google Scholar, PubMed, and Scopus. Result: Mitochondrial transplantation is a one-of-a-kind treatment for mitochondrial diseases and deficits in mitochondrial biogenesis. The replacement of malfunctioning mitochondria with transplanted viable mitochondria using innovative methodologies has shown promising outcomes as a cure for Parkinson's, involving tissue sparing coupled with enhanced energy generation and lower oxidative damage. Numerous mitochondria-targeted therapies, including mitochondrial gene therapy, redox therapy, and others, have been investigated for their effectiveness and potency. Conclusion: The development of innovative therapeutics for mitochondria-directed treatments in Parkinson's disease may be aided by optimising mitochondrial dynamics. Many neurological diseases have been studied in animal and cellular models, and it has been found that mitochondrial maintenance can slow the death of neuronal cells. It has been hypothesised that drug therapies for neurodegenerative diseases that focus on mitochondrial dysfunction will help to delay the onset of neuronal dysfunction.

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Evaluation of QSAR models for tissue-specific predictive toxicology and risk assessment of military-relevant chemical exposures: A systematic review

Fisher,

Williams,

Schneider

et al. 2024

Computational Toxicology

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In silico prediction of parkinsonian motor deficits-related neurotoxicants based on the adverse outcome pathway concept

Cited by 7 publications

References 42 publications

Machine learning in toxicological sciences: opportunities for assessing drug toxicity

Machine learning in toxicological sciences: opportunities for assessing drug toxicity

Potential applications of mitochondrial therapy with a focus on Parkinson's disease and mitochondrial transplantation

Evaluation of QSAR models for tissue-specific predictive toxicology and risk assessment of military-relevant chemical exposures: A systematic review

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