Evidence map›Paper›PMID 35188639›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2022

In Silico Models for Hepatotoxicity.

Claire Ellison, Mark Hewitt, Katarzyna Przybylak

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. The 3Rs in Experimental Liver Disease.Animals : an open access journal from MDPI · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Claire EllisonHuman and Natural Sciences Directorate, School of Science, Engineering and Environment, University of Salford, Manchester, UK.
Mark HewittSchool of Pharmacy, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK. M.Hewitt@wlv.ac.uk.
Katarzyna PrzybylakUnilever Safety and Environmental Assurance Centre, Bedfordshire, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.

Indexed as

Chemical and Drug Induced Liver InjuryDrug-Related Side Effects and Adverse ReactionsComputer SimulationForecastingHumansQuantitative Structure-Activity RelationshipExpert systemHepatotoxicityIn silico or computational predictionLiverQSAR

Identifiers

PMID35188639

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.