Evidence map›Paper›PMID 36173075›Full record

ReviewRecent advances in food, nutrition & agriculture2022

Drug-food Interactions in the Era of Molecular Big Data, Machine Intelligence, and Personalized Health.

Romy Roy, Shamsudheen Marakkar, Munawar Peringadi Vayalil, Alisha Shahanaz, Athira Panicker Anil, Shameer Kunnathpeedikayil, Ishaan Rawal, Kavya Shetty, Zahrah Shameer, Saraswathi Sathees and 5 more

Open access · hybridAbstract readReview
In one paragraph

Review in Recent advances in food, nutrition & agriculture, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
4.9field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 7 institutions in 3 countries.

Romy RoyMolecular Robotics, Cochin, Kerala, India.
Shamsudheen MarakkarMolecular Robotics, Cochin, Kerala, India.
Munawar Peringadi VayalilMolecular Robotics, Cochin, Kerala, India.
Alisha ShahanazMolecular Robotics, Cochin, Kerala, India.
Athira Panicker AnilMolecular Robotics, Cochin, Kerala, India.
Shameer KunnathpeedikayilMolecular Robotics, Cochin, Kerala, India.
Ishaan RawalMolecular Robotics, Cochin, Kerala, India.
Kavya ShettyMolecular Robotics, Cochin, Kerala, India.
Zahrah ShameerUrbana Middle School, Urbana, MD 21754, USA.
Saraswathi SatheesMolecular Robotics, Cochin, Kerala, India.
Adarsh Pooradan PrasannakumarMolecular Robotics, Cochin, Kerala, India.
Oommen Kaleeckal MathewIndian Institute of Information Technology (IIIT), Kottayam, Kerala, India.
Lakshminarayanan SubramanianDepartment of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.
Khader ShameerNorthwell Health, New York, NY, USA and Faculty of Medicine, Imperial College London, London, UK.
Kamlesh K YadavSchool of Engineering Medicine, Center for Genomic and Precision Medicine, Texas A&M University, Houston, TX 77030, USA.
Cochin University of Science and Technology · INCourant Institute of Mathematical Sciences · USIndian Institute of Information Technology and Management, Kerala · INNorthwell Health · USTexas A&M University · USUniversity of Washington · USUrbana University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The drug-food interaction brings forth changes in the clinical effects of drugs. While favourable interactions bring positive clinical outcomes, unfavourable interactions may lead to toxicity. This article reviews the impact of food intake on drug-food interactions, the clinical effects of drugs, and the effect of drug-food in correlation with diet and precision medicine. Emerging areas in drug-food interactions are the food-genome interface (nutrigenomics) and nutrigenetics. Understanding the molecular basis of food ingredients, including genomic sequencing and pharmacological implications of food molecules, helps to reduce the impact of drug-food interactions. Various strategies are being leveraged to alleviate drug-food interactions; measures including patient engagement, digital health, approaches involving machine intelligence, and big data are a few of them. Furthermore, delineating the molecular communications across dietmicrobiome- drug-food-drug interactions in a pharmacomicrobiome framework may also play a vital role in personalized nutrition. Determining nutrient-gene interactions aids in making nutrition deeply personalized and helps mitigate unwanted drug-food interactions, chronic diseases, and adverse events from their onset. Translational bioinformatics approaches could play an essential role in the next generation of drug-food interaction research. In this landscape review, we discuss important tools, databases, and approaches along with key challenges and opportunities in drug-food interaction and its immediate impact on precision medicine.

Indexed as

Big DataFood-Drug InteractionsArtificial IntelligenceDietHumansNutrigenomicsbig dataDrug-food interactionsmachine intelligencenutrigenomicspharmacomicrobiome.precision medicine

Identifiers

PMID36173075
PMCPMC10258917
OpenAlexW4283210296

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.