Development of an evaluation method for addictive compounds based on electrical activity of human iPS cell‐derived dopaminergic neurons using microelectrode array

Addiction is known to occur through the consumption of substances such as pharmaceuticals, illicit drugs, food, alcohol and tobacco. These addictions can be viewed as drug addiction, resulting from the ingestion of chemical substances contained in them. Multiple neural networks, including the reward system, anti-reward/stress system and central immune system in the brain, are believed to be involved in the onset of drug addiction. Although various compound evaluations using microelectrode array (MEA) as an in vitro testing methods to evaluate neural activities have been conducted, methods for assessing addiction have not been established. In this study, we aimed to develop an in vitro method for assessing the addiction of compounds, as an alternative to animal experiments, using human iPS cell-derived dopaminergic neurons with MEA measurements. MEA data before and after chronic exposure revealed specific changes in addictive compounds compared to non-addictive compounds, demonstrating the ability to estimate addiction of compound. Additionally, conducting gene expression analysis on cultured samples after the tests revealed changes in the expression levels of various receptors (nicotine, dopamine and GABA) due to chronic administration of addictive compounds, suggesting the potential interpretation of these expression changes as addiction-like responses in MEA measurements. The addiction assessment method using MEA measurements in human iPS cell-derived dopaminergic neurons conducted in this study proves effective in evaluating addiction of compounds on human neural networks.

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Publication Details

Journal
Addiction Biology
Published
2024-10-01
DOI
https://doi.org/10.1111/adb.13443
Citations
3
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.80

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article

Development of an evaluation method for addictive compounds based on electrical activity of human iPS cell‐derived dopaminergic neurons using microelectrode array

Nami Nagafuku, Xiaobo Han, Ikuro Suzuki, Shingo Kimura et al.
3 citations
Addiction Biology
Neuroscience and Neural Engineering
0.80
article

Development of an evaluation method for addictive compounds based on electrical activity of human iPS cell‐derived dopaminergic neurons using microelectrode array

Nami Nagafuku, Xiaobo Han, Ikuro Suzuki, Shingo Kimura, Yuto Ishibashi
article en
3 citations

Abstract

Addiction is known to occur through the consumption of substances such as pharmaceuticals, illicit drugs, food, alcohol and tobacco. These addictions can be viewed as drug addiction, resulting from the ingestion of chemical substances contained in them. Multiple neural networks, including the reward system, anti-reward/stress system and central immune system in the brain, are believed to be involved in the onset of drug addiction. Although various compound evaluations using microelectrode array (MEA) as an in vitro testing methods to evaluate neural activities have been conducted, methods for assessing addiction have not been established. In this study, we aimed to develop an in vitro method for assessing the addiction of compounds, as an alternative to animal experiments, using human iPS cell-derived dopaminergic neurons with MEA measurements. MEA data before and after chronic exposure revealed specific changes in addictive compounds compared to non-addictive compounds, demonstrating the ability to estimate addiction of compound. Additionally, conducting gene expression analysis on cultured samples after the tests revealed changes in the expression levels of various receptors (nicotine, dopamine and GABA) due to chronic administration of addictive compounds, suggesting the potential interpretation of these expression changes as addiction-like responses in MEA measurements. The addiction assessment method using MEA measurements in human iPS cell-derived dopaminergic neurons conducted in this study proves effective in evaluating addiction of compounds on human neural networks.

Addiction BiologyVol. 29(10)
Tohoku Institute of Technology (JP)
Japan Science and Technology Corporation
Good health and well-being
Openalex Percentile: Top 29%
Neuroscience and Neural Engineering
0.80
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