ArticleClinical pharmacology and therapeutics2011
Predicting adverse drug reactions using publicly available PubChem BioAssay data.
Article in Clinical pharmacology and therapeutics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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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
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning methods for predicting adverse drug events: A systematic review.British journal of clinical pharmacology · 2026Pooled it
- An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.Artificial intelligence review · 2023Article
- A Data-Driven Medical Decision Framework for Associating Adverse Drug Events with Drug-Drug Interaction Mechanisms.Journal of healthcare engineering · 2022Article
- Evaluating Drug Risk Using GAN and SMOTE Based on CFDA's Spontaneous Reporting Data.Journal of healthcare engineering · 2021Article
- Predicting potential adverse events using safety data from marketed drugs.BMC bioinformatics · 2020Article
- CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials.Journal of chemical information and modeling · 2020Article
- Identifying Herbal Adverse Events From Spontaneous Reporting Systems Using Taxonomic Name Resolution Approach.Bioinformatics and biology insights · 2020Article
- Discovering Links Between Side Effects and Drugs Using a Diffusion Based Method.Scientific reports · 2019Article
- Predicting adverse drug reactions through interpretable deep learning framework.BMC bioinformatics · 2018Article
- A chronological pharmacovigilance network analytics approach for predicting adverse drug events.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Translational systems pharmacology-based predictive assessment of drug-induced cardiomyopathy.CPT: pharmacometrics & systems pharmacology · 2018Article
- Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles.Scientific reports · 2018Article
- Review
- Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.Briefings in bioinformatics · 2017Article
- Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Getting the most out of PubChem for virtual screening.Expert opinion on drug discovery · 2016Review
- Drug Repurposing Is a New Opportunity for Developing Drugs against Neuropsychiatric Disorders.Schizophrenia research and treatment · 2016Review
- Cheminformatics analysis of the AR agonist and antagonist datasets in PubChem.Journal of cheminformatics · 2016Article
- Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform.Current pharmaceutical design · 2016Review
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.
Funding
Abstract
Adverse drug reactions (ADRs) can have severe consequences, and therefore the ability to predict ADRs prior to market introduction of a drug is desirable. Computational approaches applied to preclinical data could be one way to inform drug labeling and marketing with respect to potential ADRs. Based on the premise that some of the molecular actors of ADRs involve interactions that are detectable in large, and increasingly public, compound screening campaigns, we generated logistic regression models that correlate postmarketing ADRs with screening data from the PubChem BioAssay database. These models analyze ADRs at the level of organ systems, using the system organ classes (SOCs). Of the 19 SOCs under consideration, nine were found to be significantly correlated with preclinical screening data. With regard to six of the eight established drugs for which we could retropredict SOC-specific ADRs, prior knowledge was found that supports these predictions. We conclude this paper by predicting that SOC-specific ADRs will be associated with three unapproved or recently introduced drugs.
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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.