ArticleCPT: pharmacometrics & systems pharmacology2018
Target-Adverse Event Profiles to Augment Pharmacovigilance: A Pilot Study With Six New Molecular Entities.
Article in CPT: pharmacometrics & systems pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database.Experimental biology and medicine (Maywood, N.J.) · 2023Pooled it
- Rewiring Drug Research and Development through Human Data-Driven Discovery (HDPharmaceutics · 2023Review
- Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data.Drug safety · 2023Article
- RxNorm for drug name normalization: a case study of prescription opioids in the FDA adverse events reporting system.Frontiers in bioinformatics · 2023Article
- Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system.Scientific reports · 2022Article
- Application of a patient-centered reverse translational systems-based approach to understand mechanisms of an adverse drug reaction of immune checkpoint inhibitors.Clinical and translational science · 2022Article
- A case study of a patient-centered reverse translational systems-based approach to understand adverse event profiles in drug development.Clinical and translational science · 2022Article
- New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science.Frontiers in medicine · 2022Review
- Target Adverse Event Profiles for Predictive Safety in the Postmarket Setting.Clinical pharmacology and therapeutics · 2021Article
- Aggregation and analysis of indication-symptom relationships for drugs approved in the USA.European journal of clinical pharmacology · 2020Article
- Relevance of physicochemical properties and functional pharmacology data to predict the clinical safety profile of direct oral anticoagulants.Pharmacology research & perspectives · 2020Article
- Predicting potential adverse events using safety data from marketed drugs.BMC bioinformatics · 2020Article
- Towards Automating Adverse Event Review: A Prediction Model for Case Report Utility.Drug safety · 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
6 authors.
Funding
Abstract
Clinical trials can fail to detect rare adverse events (AEs). We assessed the ability of pharmacological target adverse-event (TAE) profiles to predict AEs on US Food and Drug Administration (FDA) drug labels at least 4 years after approval. TAE profiles were generated by aggregating AEs from the FDA adverse event reporting system (FAERS) reports and the FDA drug labels for drugs that hit a common target. A genetic algorithm (GA) was used to choose the adverse event (AE) case count (N), disproportionality score in FAERS (proportional reporting ratio (PRR)), and percent of comparator drug labels with an AE to maximize F-measure. With FAERS data alone, precision, recall, and specificity were 0.57, 0.78, and 0.61, respectively. After including FDA drug label data, precision, recall, and specificity improved to 0.67, 0.81, and 0.71, respectively. Eighteen of 23 (78%) postmarket label changes were identified correctly. TAE analysis shows promise as a method to predict AEs at the time of drug approval.
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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.