A novel graph mining approach to predict and evaluate food-drug interactions

Rahman, Md. Mostafizur; Vadrev, Srinivas Mukund; Magana-Mora, Arturo; Levman, Jacob; Soufan, Othman

doi:10.1038/s41598-022-05132-y

Cited by 14 publications

(13 citation statements)

References 92 publications

(84 reference statements)

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“…Increasing our understanding on PK and PD interaction mechanisms and utilization of novel experimental procedures will allow the gap on the available evidence regarding food effects on drugs to decrease. The incorporation of biomedical methods will allow the better prediction of potential interactions even through in silico methods during drug development or through big-data analysis [192][193][194]. From the citizen's perspective, the harnessing of digital evolution in relation to health promotion also cannot go unnoticed.…”

Section: Discussionmentioning

confidence: 99%

Drug-Food Interactions with a Focus on Mediterranean Diet

Spanakis

Patelarou

2022

Applied Sciences

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There is a growing interest among people in western countries for adoption of healthier lifestyle habits and diet behaviors with one of the most known ones to be Mediterranean diet (Med-D). Med-D is linked with daily consumption of food products such as vegetables, fruits, whole grains, seafood, beans, nuts, olive oil, low-fat food derivatives and limited consumption of meat or full fat food products. Med-D is well-known to promote well-being and lower the risk of chronic conditions such as cardiovascular diseases, diabetes, and metabolic syndrome. On the other hand bioactive constituents in foods may interfere with drugs’ pharmacological mechanisms, modulating the clinical outcome leading to drug-food interactions (DFIs). This review discusses current evidence for food products that are included within the Med-Dand available scientific data suggest a potential contribution in DFIs with impact on therapeutic outcome. Most cases refer to potential modulation of drugs’ absorption and metabolism such as foods’ impact on drugs’ carrier-mediated transport and enzymatic metabolism as well as potential synergistic or antagonistic effects that enhance or reduce the pharmacological effect for some drugs. Adherence to Med-D can improve disease management and overall well-being, but specific foods should be consumed with caution so as to not hinder therapy outcome. Proper patient education and consultation from healthcare providers is important to avoid any conflicts and side effects due to clinically significant DFIs.

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Section: Discussionmentioning

confidence: 99%

Drug-Food Interactions with a Focus on Mediterranean Diet

Spanakis

Patelarou

2022

Applied Sciences

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“…A systematic framework called FDMine was proposed to predict new FDIs and examine the possible pharmacological effects of food compounds . The raw data extracted from the FooDB and DrugBank database were used to build a homogeneous network based on a structural similarity profile, with nodes representing drugs, foods, and compounds. , Link prediction algorithms can predict the existence of a link between two entities in a network.…”

Section: Applicationsmentioning

confidence: 99%

When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry

2023

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Since the first food database was released over one hundred years ago, food databases have become more diversified, including food composition databases, food flavor databases, and food chemical compound databases. These databases provide detailed information about the nutritional compositions, flavor molecules, and chemical properties of various food compounds. As artificial intelligence (AI) is becoming popular in every field, AI methods can also be applied to food industry research and molecular chemistry. Machine learning and deep learning are valuable tools for analyzing big data sources such as food databases. Studies investigating food compositions, flavors, and chemical compounds with AI concepts and learning methods have emerged in the past few years. This review illustrates several well-known food databases, focusing on their primary contents, interfaces, and other essential features. We also introduce some of the most common machine learning and deep learning methods. Furthermore, a few studies related to food databases are given as examples, demonstrating their applications in food pairing, food–drug interactions, and molecular modeling. Based on the results of these applications, it is expected that the combination of food databases and AI will play an essential role in food science and food chemistry.

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“…For example, dietary supplementation with histidine enhances the sensitivity of leukemia xenografts to methotrexate (95), and, for a brief period after consumption, grapefruit juice slows the metabolism of cyclosporine in transplant patients (84). To systematically screen all potential food-drug interactions (FDIs) that could enhance or inhibit drug activity, Rahman et al (158) developed a novel framework, FDMine, that integrates the databases FooDB and DrugBank (191) to establish similarity profiles and link prediction algorithms. This methodology identified novel FDIs in which dietary components can induce anti-inflammatory effects on the basis of known biological pathways or pharmacological effects.…”

Section: Discovery Of Dietary Molecules For Targeted Therapiesmentioning

confidence: 99%

The Role of Artificial Intelligence in Deciphering Diet–Disease Relationships: Case Studies

2023

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Modernization of society from a rural, hunter-gatherer setting into an urban and industrial one, with the associated dietary changes, has led to an increased prevalence of cardiometabolic and additional noncommunicable diseases, such as cancer, inflammatory bowel disease, and neurodegenerative and autoimmune disorders. However, while dietary sciences have been rapidly evolving to meet these challenges, validation and translation of experimental results into clinical practice remain limited for multiple reasons, including inherent ethnic, gender, and cultural interindividual variability, among other methodological, dietary reporting–related, and analytical issues. Recently, large clinical cohorts with artificial intelligence analytics have introduced new precision and personalized nutrition concepts that enable one to successfully bridge these gaps in a real-life setting. In this review, we highlight selected examples of case studies at the intersection between diet–disease research and artificial intelligence. We discuss their potential and challenges and offer an outlook toward the transformation of dietary sciences into individualized clinical translation. Expected final online publication date for the Annual Review of Nutrition, Volume 43 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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A novel graph mining approach to predict and evaluate food-drug interactions

Cited by 14 publications

References 92 publications

Drug-Food Interactions with a Focus on Mediterranean Diet

Drug-Food Interactions with a Focus on Mediterranean Diet

When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry

The Role of Artificial Intelligence in Deciphering Diet–Disease Relationships: Case Studies

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