Development of method using language processing techniques for extracting information on drug-health food product interactions
© 2024 British Pharmacological Society..
AIMS: Health food products (HFPs) are foods and products related to maintaining and promoting health. HFPs may sometimes cause unforeseen adverse health effects by interacting with drugs. Considering the importance of information on the interactions between HFPs and drugs, this study aimed to establish a workflow to extract information on Drug-HFP Interactions (DHIs) from open resources.
METHODS: First, Information on drugs, enzymes, their interactions, and known DHIs was collected from multiple public databases and literature sources. Next, a network consisted of enzymes, HFP, and drugs was constructed, assuming enzymes as candidates for hubs in Drug-HFP interactions (Method 1). Furthermore, we developed methods to analyze the biomedical context of each drug and HFP to predict potential DHIs out of the DHIs obtained in Method 1 by applying BioWordVec, a widely used biomedical terminology quantifier (Method 2-1 and 2-2).
RESULTS: 44,965 DHIs (30% known) were identified in Method 1, including 38 metabolic enzymes, 157 HFPs, and 1256 drugs. Method 2-1 selected 7401 DHIs (17% known) from the DHIs of Method 1, while Method 2-2 chose 2819 DHIs (30% known). Based on the different assumptions in these methods where Method 2-1 specifically selects HFPs interacting with specific enzymes and Method 2-2 specifically selects HFPs with similar function with drugs, the propsed methods resulted in extracting a wide variety of DHIs.
CONCLUSIONS: By integrating the results of language processing techniques with those of the network analysis, a workflow to efficiently extract unknown and known DHIs was constructed.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
British journal of clinical pharmacology - (2024) vom: 20. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yoshizaki, Mari [VerfasserIn] |
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| Links: |
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| Themen: |
BioWordVec |
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| Anmerkungen: |
Date Revised 20.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1111/bcp.16032 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM36994206X |
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| 245 | 1 | 0 | |a Development of method using language processing techniques for extracting information on drug-health food product interactions |
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| 500 | |a Date Revised 20.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
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| 520 | |a © 2024 British Pharmacological Society. | ||
| 520 | |a AIMS: Health food products (HFPs) are foods and products related to maintaining and promoting health. HFPs may sometimes cause unforeseen adverse health effects by interacting with drugs. Considering the importance of information on the interactions between HFPs and drugs, this study aimed to establish a workflow to extract information on Drug-HFP Interactions (DHIs) from open resources | ||
| 520 | |a METHODS: First, Information on drugs, enzymes, their interactions, and known DHIs was collected from multiple public databases and literature sources. Next, a network consisted of enzymes, HFP, and drugs was constructed, assuming enzymes as candidates for hubs in Drug-HFP interactions (Method 1). Furthermore, we developed methods to analyze the biomedical context of each drug and HFP to predict potential DHIs out of the DHIs obtained in Method 1 by applying BioWordVec, a widely used biomedical terminology quantifier (Method 2-1 and 2-2) | ||
| 520 | |a RESULTS: 44,965 DHIs (30% known) were identified in Method 1, including 38 metabolic enzymes, 157 HFPs, and 1256 drugs. Method 2-1 selected 7401 DHIs (17% known) from the DHIs of Method 1, while Method 2-2 chose 2819 DHIs (30% known). Based on the different assumptions in these methods where Method 2-1 specifically selects HFPs interacting with specific enzymes and Method 2-2 specifically selects HFPs with similar function with drugs, the propsed methods resulted in extracting a wide variety of DHIs | ||
| 520 | |a CONCLUSIONS: By integrating the results of language processing techniques with those of the network analysis, a workflow to efficiently extract unknown and known DHIs was constructed | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a BioWordVec | |
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| 700 | 1 | |a Kuriya, Yuki |e verfasserin |4 aut | |
| 700 | 1 | |a Yamamoto, Masaki |e verfasserin |4 aut | |
| 700 | 1 | |a Watanabe, Naoki |e verfasserin |4 aut | |
| 700 | 1 | |a Araki, Michihiro |e verfasserin |4 aut | |
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