Evidence map›Paper›PMID 29667370›Full record

ArticleCPT: pharmacometrics & systems pharmacology2018

Systems Toxicology Approach to Identifying Paracetamol Overdose.

Chantelle L Mason, Joseph Leedale, Sotiris Tasoulis, Ian Jarman, Daniel J Antoine, Steven D Webb

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. A systems pharmacology model for paracetamol overdose and treatment.Journal of pharmacokinetics and pharmacodynamics · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Chantelle L MasonDepartment of Applied Mathematics, Liverpool John Moores University, Liverpool, UK.
Joseph LeedaleEPSRC Liverpool Centre for Mathematics in Healthcare, Department of Mathematical Sciences, University of Liverpool, Liverpool, UK.
Sotiris TasoulisDepartment of Applied Mathematics, Liverpool John Moores University, Liverpool, UK.
Ian JarmanDepartment of Applied Mathematics, Liverpool John Moores University, Liverpool, UK.
Daniel J AntoineMRC Centre for Inflammation Research, Queens Medical Research Institute, University of Edinburgh, Edinburgh, UK.
Steven D WebbDepartment of Applied Mathematics, Liverpool John Moores University, Liverpool, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Paracetamol (acetaminophen (APAP)) is one of the most commonly used analgesics in the United Kingdom and the United States. However, exceeding the maximum recommended dose can cause serious liver injury and even death. Promising APAP toxicity biomarkers are thought to add value to those used currently and clarification of the functional relationships between these biomarkers and liver injury would aid clinical implementation of an improved APAP toxicity identification framework. The framework currently used to define an APAP overdose is highly dependent upon time since ingestion and initial dose; information that is often highly unpredictable. A pharmacokinetic/pharmacodynamic (PK/PD) APAP model has been built in order to understand the relationships between a panel of biomarkers and APAP dose. Visualization and statistical tools have been used to predict initial APAP dose and time since administration. Additionally, logistic regression analysis has been applied to histology data to provide a prediction of the probability of liver injury.

Indexed as

AcetaminophenAnimalsBiomarkersChemical and Drug Induced Liver InjuryDisease Models, AnimalDrug OverdoseHumansLogistic ModelsMaleMiceModels, StatisticalModels, TheoreticalAcetaminophenBiomarkers

Identifiers

PMID29667370
PMCPMC6027737

What OpenQuestion holds

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Registered trials

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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.