Evidence map›Paper›PMID 41602273›Full record

ReviewFrontiers in toxicology2025

Bridging science and curriculum: preparing future leaders in computational toxicology.

Frances Hall, Candice Johnson

Abstract readReview
In one paragraph

Review in Frontiers in toxicology, 2025. 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. 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

2 authors.

Frances HallInstem, Stone, United Kingdom.
Candice JohnsonInstem, Stone, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational toxicology plays an important role in chemical safety assessments. Computational methods are applied to early-stage screening in drug discovery, hazard identification, and regulatory safety assessment. This article presents an overview of the foundational skills, technical capabilities and regulatory literacy recommended to successfully apply and evaluate (Q)SAR ((Quantitative) Structure-Activity Relationship) methodologies (e.g., statistical and alert-based approaches) and read-across within established frameworks such as the (Q)SAR Assessment Framework (QAF), OECD validation principles and context-specific regulatory frameworks; for example, ICH M7. Additionally, the manuscript covers strategies that can be used to integrate theoretical and practical experience with foundational skills (e.g., internships, case studies, regulatory simulations). An overall educational framework that emphasises competency-based education through interdisciplinary exposure is presented. The framework outlines the progression from foundational knowledge to methodological understanding, context of use application and the ability to assess the reliability of outcomes. Although the integrated framework is applicable to both regulatory and non-regulatory use contexts, the manuscript presents regulatory focused use cases, which could be explored within educational settings. These use cases consider mature, as well as emerging regulatory applications, and therefore highlight the need to apply foundational principles (e.g., expert review, qualification of methods) in diverse contexts. This approach reinforces a context-of-use driven approach to curriculum design and provides opportunities for growth through real-world application and experiential learning, supported by collaborative initiatives and open-access resources.

Indexed as

compound safetycomputational toxicologyqualitative structure-activity relationships (QSAR)read-acrosstoxicology education

Identifiers

PMID41602273
PMCPMC12832508

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.