Evidence map›Paper›PMID 30404334›Full record

ReviewMicromachines2016

Microfluidic-Based Multi-Organ Platforms for Drug Discovery.

Ahmad Rezaei Kolahchi, Nima Khadem Mohtaram, Hassan Pezeshgi Modarres, Mohammad Hossein Mohammadi, Armin Geraili, Parya Jafari, Mohsen Akbari, Amir Sanati-Nezhad

Abstract readReview
In one paragraph

Review in Micromachines, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Review
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  5. Review
  6. Review
  7. Patient-Derived Organoids as a Model for Cancer Drug Discovery.International journal of molecular sciences · 2021
    Review
  8. Review
  9. Review
  10. Review
  11. Predicting tubular reabsorption with a human kidney proximal tubule tissue-on-a-chip and physiologically-based modeling.Toxicology in vitro : an international journal published in association with BIBRA · 2020
    Article
  12. Article
  13. Review
  14. Review
  15. Developments with 3D bioprinting for novel drug discovery.Expert opinion on drug discovery · 2018
    Review
  16. Article
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

8 authors.

Ahmad Rezaei KolahchiBioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada. ahmad.rezaei@polymtl.ca.
Nima Khadem MohtaramLaboratory for Innovations in MicroEngineering (LiME), Department of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada. nkhadem@uvic.ca.
Hassan Pezeshgi ModarresBioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada. hassan.pezeshki@gmail.com.
Mohammad Hossein MohammadiDepartment of Chemical and Petroleum Engineering, Sharif University of Technology, Azadi Ave., Tehran 11155-9516, Iran. maosein@gmail.com.
Armin GerailiDepartment of Chemical and Petroleum Engineering, Sharif University of Technology, Azadi Ave., Tehran 11155-9516, Iran. armin.geraili@gmail.com.
Parya JafariDepartment of Electrical Engineering, Sharif University of Technology, Azadi Ave., Tehran 11155-9516, Iran. parya74jafari@gmail.com.
Mohsen AkbariLaboratory for Innovations in MicroEngineering (LiME), Department of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada. makbari@uvic.ca.
Amir Sanati-NezhadBioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada. amir.sanatinezhad@ucalgary.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC) model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.

Indexed as

body-on-chipdrug discoveryin silico modelingmicrofluidicsorgan-on-chip

Identifiers

PMID30404334
PMCPMC6189912

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.