ArticleMolecules (Basel, Switzerland)2019
Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation.
Article in Molecules (Basel, Switzerland), 2019. 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.
What it found
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.Frontiers in pharmacology · 2024Pooled it
- Computational Prediction of the Severity of Adverse Drug Reactions Caused by Drug-Drug Interactions.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.International journal of molecular sciences · 2026Article
- Optoelectronic Interfaces for Nongenetic Modulation of Excitable Tissues.Small methods · 2026Review
- A green TLC-densitometric bioanalytical method for concurrent determination of duloxetine and risperidone in human plasma with in-silico DDI evaluation.BMC chemistry · 2026Article
- A biodegradable and flexible neural interface for transdermal optoelectronic modulation and regeneration of peripheral nerves.Nature communications · 2024Article
- Utility of sustainable ratio derivative spectrophotometry for the concurrent assay of synergistic repurposed drugs for COVID-19 infections; Insilico pharmacokinetics proof.BMC chemistry · 2024Article
- Bioresorbable thin-film silicon diodes for the optoelectronic excitation and inhibition of neural activities.Nature biomedical engineering · 2023Article
- In Silico Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 Isoforms.Pharmaceutics · 2021Article
- Recent Trends in Enzyme Inhibition and Activation in Drug Design.Molecules (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter-mediated disposition of the victim drug. Accurate assessment of clinically relevant DDIs for novel drug candidates represents one of the significant tasks of contemporary drug research and development and is important for practicing physicians. This work is a development of our previous investigations and aimed to create a model for the severity of DDIs prediction. PASS program and PoSMNA descriptors were implemented for prediction of all five classes of DDIs severity according to OpeRational ClassificAtion (ORCA) system: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). Prediction can be carried out both for known drugs and for new, not yet synthesized substances using only their structural formulas. Created model provides an assessment of DDIs severity by prediction of different ORCA classes from the first most dangerous class to the fifth class when DDIs do not take place in the human organism. The average accuracy of DDIs class prediction is about 0.75.
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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.