ReviewClinical pharmacokinetics2022
HDI Highlighter, The First Intelligent Tool to Screen the Literature on Herb-Drug Interactions.
Review in Clinical pharmacokinetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Pooled it
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Toxicity Derived from Interaction between Natural Compounds and Cancer Therapeutic Drugs Metabolized by CYP3A4: Lessons Learned from Two Clinical Case Reports.International journal of molecular sciences · 2023Article
- Evaluation and clinical implications of interactions between compound Danshen dropping pill and warfarin associated with the epoxide hydrolase gene.Frontiers in pharmacology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Herbal food supplements are commonly used and can be an important part of patient self-care. Like all other bio-active and therapeutic products, they have a benefit/risk balance. These products are not without adverse effects and potentially interact with other therapies. Educating patients and providing information for health professionals about the risk of herb-drug interactions is key. One of the purposes of the biomedical literature is to inform prescribers. Scientific literature accessible on databases such as PubMed is dense and careful reading is time consuming. We propose a reading aid tool named "HDI highlighter" to help readers to find key information in clinical studies and case reports describing herb-drug interactions. It uses natural language processing algorithms (artificial intelligence) with a pharmaceutical focus. Semantic relation extraction for herb-drug interactions from the biomedical literature are overexpressed using keywords. We have tested it to review 120 published articles over the last 10 years. In these articles, we have shown that case reports often involved long-term or semi-long-term treatments such as cancer or human immunodeficiency virus therapies, antiepileptic drugs, or central nervous system drugs. Similarly, these classes of drugs are more extensively targeted by clinical studies. Herb-drug interactions described in case reports are identified in medicinal, recreational, and alimentary uses. They also usually lack a rigorous description of the herb(s) involved. Typically, clinical studies provide a complete description of protocols and dosages, with a few exceptions explained by patients' needs. Clinical studies on herbs are nevertheless conducted on a limited number of patients. All these limitations make the interpretation of herb-drug interactions complicated, but the HDI highlighter provides a quick overview of the herb-drug interaction literature.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.