Development of method using language processing techniques for extracting information on drug-health food product interactions.
BioWordVec
drug-food interactions
drug-herb interactions
health food
health product
Journal
British journal of clinical pharmacology
ISSN: 1365-2125
Titre abrégé: Br J Clin Pharmacol
Pays: England
ID NLM: 7503323
Informations de publication
Date de publication:
20 Mar 2024
20 Mar 2024
Historique:
revised:
25
12
2023
received:
20
10
2023
accepted:
22
01
2024
medline:
20
3
2024
pubmed:
20
3
2024
entrez:
20
3
2024
Statut:
aheadofprint
Résumé
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. 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). 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. 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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Japan Science and Technology Agency
ID : JPMJFS2130
Informations de copyright
© 2024 British Pharmacological Society.
Références
Jagdale YD, Mahale SV, Zohra B, et al. Nutritional profile and potential health benefits of super foods: a review. Sustainability. 2021;13(16):9240. doi:10.3390/su13169240
Hsieh MH, Chan P, Sue YM, et al. Efficacy and tolerability of oral stevioside in patients with mild essential hypertension: a two-year, randomized, placebo-controlled study. Clin Ther. 2003;25(11):2797-2808. doi:10.1016/s0149-2918(03)80334-x
Meding B. Normal standards for dermatological health screening at places of work. Contact Dermatitis. 1992;27(4):269-270. doi:10.1111/j.1600-0536.1992.tb03270.x
Melis MS, Sainati AR. Effect of calcium and verapamil on renal function of rats during treatment with stevioside. J Ethnopharmacol. 1991;33(3):257-262. doi:10.1016/0378-8741(91)90086-s
Chan P, Tomlinson B, Chen YJ, Liu JC, Hsieh MH, Cheng JT. A double-blind placebo-controlled study of the effectiveness and tolerability of oral stevioside in human hypertension. Br J Clin Pharmacol. 2000;50(3):215-220. doi:10.1046/j.1365-2125.2000.00260.x
Asher GN, Corbett AH, Hawke RL. Common herbal dietary supplement-drug interactions. Am Fam Physician. 2017;96(2):101-107.
Roy R, Marakkar S, Vayalil MP, et al. Drug-food interactions in the era of molecular big data, machine intelligence, and personalized health. Recent Adv Food Nutr Agric. 2022;13(1):27-50. doi:10.2174/2212798412666220620104809
Ziani K, Negrei C, Ioniță-Mîndrican C-B, et al. Drug-food interactions: the influence on the patient's therapeutic plan. Farmacia. 2022;70(5):785-797. doi:10.31925/farmacia.2022.5.3
Rein MJ, Renouf M, Cruz-Hernandez C, Actis-Goretta L, Thakkar SK, da Silva Pinto M. Bioavailability of bioactive food compounds: a challenging journey to bioefficacy. Br J Clin Pharmacol. 2013;75(3):588-602. doi:10.1111/j.1365-2125.2012.04425.x
Sorrenti V, Burò I, Consoli V, Vanella L. Recent advances in health benefits of bioactive compounds from food wastes and by-products: biochemical aspects. Int J Mol Sci. 2023;24(3):2019. doi:10.3390/ijms24032019
Jiamjariyatam R, Phucharoenrak P, Samosorn S, et al. Influence of different extraction methods on the changes in bioactive compound composition and antioxidant properties of solid-state fermented coffee husk extracts. Sci World J. 2023;2023:6698056. doi:10.1155/2023/6698056
Mounika A, Ilangovan B, Mandal S, Shraddha Yashwant W, Priya Gali S, Shanmugam A. Prospects of ultrasonically extracted food bioactives in the field of non-invasive biomedical applications-a review. Ultrason Sonochem. 2022;89:106121. doi:10.1016/j.ultsonch.2022.106121
Almazroo OA, Miah MK, Venkataramanan R. Drug metabolism in the liver. Clin Liver Dis. 2017;21(1):1-20. doi:10.1016/j.cld.2016.08.001
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):D353-D361. doi:10.1093/nar/gkw1092
Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27-30. doi:10.1093/nar/28.1.27
Tanabe M, Kanehisa M. Using the KEGG database resource. Curr Protoc Bioinformatics. 2012;1(1):1.12.1-1.12.43. doi:10.1002/0471250953.bi0112s38
Kanehisa Laboratories. (2023). Ethical Drugs: Lorazepam. KEGG URL:https://www.kegg.jp/medicus-bin/japic_med?japic_code=00062316
Dwyer JT, Coates PM, Smith MJ. Dietary supplements: regulatory challenges and research resources. Nutrients. 2018;10(1):41. doi:10.3390/nu10010041
Garcia-Cazarin ML, Wambogo EA, Regan KS, Davis CD. Dietary supplement research portfolio at the NIH, 2009-2011. J Nutr. 2014;144(4):414-418. doi:10.3945/jn.113.189803
Japan Health Food and Supplement Information Center. (2022). All About Health Foods and Supplements [Ingredients] (7th ed.).
Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074-D1082. doi:10.1093/nar/gkx1037
Motschall E, Falck-Ytter Y. Searching the MEDLINE literature database through PubMed: a short guide. Onkologie. 2005;28(10):517-522. doi:10.1159/000087186
White J. PubMed 2.0. Med Ref Serv Q. 2020;39(4):382-387. doi:10.1080/02763869.2020.1826228
Fleuren WW, Alkema W. Application of text mining in the biomedical domain. Methods. 2015;74:97-106. doi:10.1016/j.ymeth.2015.01.015
Junge A, Jensen LJ. CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision. Bioinformatics. 2020;36(1):264-271. doi:10.1093/bioinformatics/btz490
Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ. DISEASES: text mining and data integration of disease-gene associations. Methods. 2015;74:83-89. doi:10.1016/j.ymeth.2014.11.020
Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607-D613. doi:10.1093/nar/gky1131
Wang T, Yang J, Xiao Y, et al. DFinder: a novel end-to-end graph embedding-based method to identify drug-food interactions. Bioinformatics. 2023;39(1):btac837. doi:10.1093/bioinformatics/btac837
Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-2504. doi:10.1101/gr.1239303
Le NQK, Ho QT, Nguyen VN, Chang JS. BERT-promoter: an improved sequence-based predictor of DNA promoter using BERT pre-trained model and SHAP feature selection. Comput Biol Chem. 2022;99:107732. doi:10.1016/j.compbiolchem.2022.107732
Le NQK. Leveraging transformers-based language models in proteome bioinformatics. Review Proteomics. Advance online publication. 2023;23(23-24):e2300011. doi:10.1002/pmic.202300011
Gaudry A, Huber F, Nothias L-F, et al. MEMO: mass spectrometry-based sample vectorization to explore chemodiverse datasets. Front Bioinform. 2022;2:842964. doi:10.3389/fbinf.2022.842964
Zhang Y, Chen Q, Yang Z, Lin H, Lu Z. BioWordVec, improving biomedical word embeddings with subword information and MeSH. Scientific Data. 2019;6(1):52. doi:10.1038/s41597-019-0055-0
Campello RJGB, Moulavi D, Sander J. Density-based clustering based on hierarchical density estimates. In: Advances in knowledge discovery and data mining; 2013:160-172. doi:10.1007/978-3-642-37456-2_14
McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv, arXiv:1802.03426. 2018.
Melvin RL, Godwin RC, Xiao J, Thompson WG, Berenhaut KS, Salsbury FR Jr. Uncovering large-scale conformational change in molecular dynamics without prior knowledge. J Chem Theory Comput. 2016;12(12):6130-6146. doi:10.1021/acs.jctc.6b00757
Alexander SPH, Fabbro D, Kelly E, et al. The Concise Guide To Pharmacology 2019/20: enzymes. Br J Pharmacol 2019;S297-S396. doi:10.1111/bph.14752
Han J, Kamber M, Pei J, Wei D. Data mining: concepts and techniques. 3rd ed; 2012.
Gonzalez-Covarrubias V, Kalabus JL, Blanco JG. Inhibition of polymorphic human carbonyl reductase 1 (CBR1) by the cardioprotectant flavonoid 7-monohydroxyethyl rutoside (monoHER). Pharm Res. 2008;25(7):1730-1734. doi:10.1007/s11095-008-9592-5
Jankovic J, Clarence-Smith K. Tetrabenazine for the treatment of chorea and other hyperkinetic movement disorders. Expert Rev Neurother. 2011;11(11):1509-1523. doi:10.1586/ern.11.149
Hui S, Gong QH, Yuan M, Smith SS. Short-term steroid treatment increases d GABAA receptor subunit expression in rat CA1 hippocampus: pharmacological and behavioral effects. Neuropharmacology. 2005;49(5):573-586. doi:10.1016/j.neuropharm.2005.04.026
Nebert DW, Gonzalez FJ. P450 genes: structure, evolution, and regulation. Annu Rev Biochem. 1987;56(1):945-993. doi:10.1146/annurev.bi.56.070187.004501