Evidence map›Paper›PMID 37111785›Full record

ReviewPharmaceutics2023

Microfluidic Liver-on-a-Chip for Preclinical Drug Discovery.

Jingyu Fu, Hailong Qiu, Cherie S Tan

Abstract readReview
In one paragraph

Review in Pharmaceutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
  10. Review
  11. Review
  12. Article
  13. Lessons Learned from Liver-on-Chip Platform.Annals of biomedical engineering · 2025
    Review
  14. Review
  15. Review
  16. Functional Liver Cell-Based Platforms in Biomedical Research.Pharmacology research & perspectives · 2025
    Review
  17. Article
  18. Review
  19. Article
  20. 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.

Jingyu FuAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.
Hailong QiuTianjin Key Laboratory of Functional Crystal Materials, Institute of Functional Crystal, Tianjin University of Technology, Tianjin 300384, China.ORCID 0000-0001-6380-7731
Cherie S TanAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.ORCID 0000-0003-1612-6241

Funding

National Natural Science Foundation of China 22274110, 22250610197, 51872198, and 21904094
6 · The paper itself

Abstract

Drug discovery is an expensive, long, and complex process, usually with a high degree of uncertainty. In order to improve the efficiency of drug development, effective methods are demanded to screen lead molecules and eliminate toxic compounds in the preclinical pipeline. Drug metabolism is crucial in determining the efficacy and potential side effects, mainly in the liver. Recently, the liver-on-a-chip (LoC) platform based on microfluidic technology has attracted widespread attention. LoC systems can be applied to predict drug metabolism and hepatotoxicity or to investigate PK/PD (pharmacokinetics/pharmacodynamics) performance when combined with other artificial organ-on-chips. This review discusses the liver physiological microenvironment simulated by LoC, especially the cell compositions and roles. We summarize the current methods of constructing LoC and the pharmacological and toxicological application of LoC in preclinical research. In conclusion, we also discussed the limitations of LoC in drug discovery and proposed a direction for improvement, which may provide an agenda for further research.

Indexed as

drug discoveryliver-on-a-chipmicrofluidicsorgan-on-chip

Identifiers

PMID37111785
PMCPMC10141038

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.