Evidence map›Paper›PMID 29311950›Full record

ArticleFrontiers in physiology2017

Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology.

Chon Lok Lei, Ken Wang, Michael Clerx, Ross H Johnstone, Maria P Hortigon-Vinagre, Victor Zamora, Andrew Allan, Godfrey L Smith, David J Gavaghan, Gary R Mirams and 1 more

Abstract read
In one paragraph

Article in Frontiers in physiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Review
  5. Single-cell ionic current phenotyping explains stem cell-derived cardiomyocyte action potential morphology.American journal of physiology. Heart and circulatory physiology · 2024
    Article
  6. Article
  7. Engineering approaches for cardiac organoid formation and their characterization.Translational research : the journal of laboratory and clinical medicine · 2022
    Review
  8. Frontiers in physiology · 2021
    Review
  9. Article
  10. Article
  11. Article
  12. Calibration of ionic and cellular cardiac electrophysiology models.Wiley interdisciplinary reviews. Systems biology and medicine · 2020
    Review
  13. An audit of uncertainty in multi-scale cardiac electrophysiology models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020
    Review
  14. Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage-clamp experiments.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020
    Article
  15. Considering discrepancy when calibrating a mechanistic electrophysiology model.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020
    Article
  16. Article
  17. Required GBiophysical journal · 2019
    Article
  18. Four Ways to Fit an Ion Channel Model.Biophysical journal · 2019
    Article
  19. Article
  20. 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

11 authors.

Chon Lok LeiComputational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Ken WangRoche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
Michael ClerxComputational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Ross H JohnstoneComputational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Maria P Hortigon-VinagreClyde Biosciences, BioCity Scotland, Newhouse, United Kingdom.
Victor ZamoraClyde Biosciences, BioCity Scotland, Newhouse, United Kingdom.
Andrew AllanClyde Biosciences, BioCity Scotland, Newhouse, United Kingdom.
Godfrey L SmithClyde Biosciences, BioCity Scotland, Newhouse, United Kingdom.
David J GavaghanComputational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Gary R MiramsCentre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.
Liudmila PolonchukRoche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) have applications in disease modeling, cell therapy, drug screening and personalized medicine. Computational models can be used to interpret experimental findings in iPSC-CMs, provide mechanistic insights, and translate these findings to adult cardiomyocyte (CM) electrophysiology. However, different cell lines display different expression of ion channels, pumps and receptors, and show differences in electrophysiology. In this exploratory study, we use a mathematical model based on iPSC-CMs from Cellular Dynamic International (CDI, iCell), and compare its predictions to novel experimental recordings made with the Axiogenesis Cor.4U line. We show that tailoring this model to the specific cell line, even using limited data and a relatively simple approach, leads to improved predictions of baseline behavior and response to drugs. This demonstrates the need and the feasibility to tailor models to individual cell lines, although a more refined approach will be needed to characterize individual currents, address differences in ion current kinetics, and further improve these results.

Indexed as

cardiomyocytescomputational modelelectrophysiologymathematical modelpharmacologystem cell derivedvariability

Identifiers

PMID29311950
PMCPMC5732978

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.