Evidence map›Paper›PMID 40892062›Full record

ReviewArchives of toxicology2025

Revolutionizing toxicological risk assessment: integrative advances in new approach methodologies (NAMs) and precision toxicology.

Qiu-Shuang Sheng, Bin Liu, Xiao Wang, Lei Hua, Shou-Cheng Zhao, Xiao-Zhong Sun, Mu-Yang Li, Xiang-Yu Zhang, Jia-Xu Wang, Pei-Li Hu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Archives of toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
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  4. Article
  5. Review
  6. Article
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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

10 authors.

Qiu-Shuang ShengJilin Province Product Quality Supervision and Inspection Institute, Changchun, 130103, China. shengqiushuang@jlzjy.org.ORCID 0009-0006-6751-8084
Bin LiuJilin Province Product Quality Supervision and Inspection Institute, Changchun, 130103, China.
Xiao WangJilin Province Product Quality Supervision and Inspection Institute, Changchun, 130103, China.
Lei HuaJilin Province Product Quality Supervision and Inspection Institute, Changchun, 130103, China.
Shou-Cheng ZhaoJilin Province Product Quality Supervision and Inspection Institute, Changchun, 130103, China.
Xiao-Zhong SunJilin Province Product Quality Supervision and Inspection Institute, Changchun, 130103, China.
Mu-Yang LiJilin Province Product Quality Supervision and Inspection Institute, Changchun, 130103, China.
Xiang-Yu ZhangJilin Province Product Quality Supervision and Inspection Institute, Changchun, 130103, China.
Jia-Xu WangJilin Province Product Quality Supervision and Inspection Institute, Changchun, 130103, China.
Pei-Li HuNational Institutes for Food and Drug Control, Beijing, 100050, China.

Funding

the Jilin Provincial Science and Technology Department 20250202067NC
6 · The paper itself

Abstract

Traditional toxicological paradigms, reliant on animal testing and simplistic in vitro models, face significant limitations, including prolonged timelines, high costs, and poor translational predictability due to interspecies differences. This review highlights the transformative potential of New Approach Methodologies (NAMs) in overcoming these challenges. Key advancements include Organ-on-a-Chip (OoC) platforms that emulate human organ physiology and multi-organ crosstalk, significantly improving predictive accuracy. Integration of multi-omics technologies (genomics, proteomics, metabolomics) provides unprecedented mechanistic insights into toxicity pathways. Computational toxicology, leveraging machine learning and QSAR modeling, enables high-throughput hazard prioritization and risk prediction. While NAMs offer human-relevant, efficient alternatives for chemical safety evaluation, critical bottlenecks remain. These involve insufficient physiological complexity in current in vitro models, interpretability limitations of AI-driven approaches, challenges in quantifying mixture toxicity and low-dose effects, and a lag in regulatory adoption. Emerging strategies like probabilistic risk assessment, AI-driven exposomics, and tiered testing paradigms hold promise for addressing chemical mixture risks and personalized exposures. Future progress requires interdisciplinary collaboration to refine microphysiological systems, harmonize regulatory frameworks with scientific innovation, and establish open-access data repositories, paving the way for precision toxicology and sustainable chemical risk management.

Indexed as

Toxicity TestsToxicologyAnimalsHumansLab-On-A-Chip DevicesMachine LearningQuantitative Structure-Activity RelationshipRisk AssessmentComputational toxicologyMulti-omics technologiesNew approach methodologies (NAMs)Organ-on-a-chipPrecision toxicologyRisk assessment

Identifiers

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.