ArticleClinical pharmacology and therapeutics2021
Target Adverse Event Profiles for Predictive Safety in the Postmarket Setting.
Article in Clinical pharmacology and therapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
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.
- Article
- 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
- Identifying new drugs associated with pulmonary arterial hypertension: A WHO pharmacovigilance database disproportionality analysis.British journal of clinical pharmacology · 2022Article
- 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
- Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports.CPT: pharmacometrics & systems pharmacology · 2022Review
- 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
- The COVID-19 explorer-An integrated, whole patient knowledge model of COVID-19 disease.Frontiers in molecular medicine · 2022Article
- A Data-Driven Medical Decision Framework for Associating Adverse Drug Events with Drug-Drug Interaction Mechanisms.Journal of healthcare engineering · 2022Article
- New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science.Frontiers in medicine · 2022Review
- Whole patient knowledge modeling of COVID-19 symptomatology reveals common molecular mechanisms.Frontiers in molecular medicine · 2022Article
- Regulatory Safety Learning Driven by the Mechanism of Action: The Case of TNF-α Inhibitors.Clinical pharmacology and therapeutics · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We improved a previous pharmacological target adverse-event (TAE) profile model to predict adverse events (AEs) on US Food and Drug Administration (FDA) drug labels at the time of approval. The new model uses more drugs and features for learning as well as a new algorithm. Comparator drugs sharing similar target activities to a drug of interest were evaluated by aggregating AEs from the FDA Adverse Event Reporting System (FAERS), FDA drug labels, and medical literature. An ensemble machine learning model was used to evaluate FAERS case count, disproportionality scores, percent of comparator drug labels with a specific AE, and percent of comparator drugs with the reports of the event in the literature. Overall classifier performance was F1 of 0.71, area under the precision-recall curve of 0.78, and area under the receiver operating characteristic curve of 0.87. TAE analysis continues to show promise as a method to predict adverse events at the time of 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.