A 3D microfluidic liver model for high throughput compound toxicity screening in the OrganoPlate®.


Journal

Toxicology
ISSN: 1879-3185
Titre abrégé: Toxicology
Pays: Ireland
ID NLM: 0361055

Informations de publication

Date de publication:
28 02 2021
Historique:
received: 30 08 2020
revised: 11 12 2020
accepted: 15 12 2020
pubmed: 29 12 2020
medline: 13 5 2021
entrez: 28 12 2020
Statut: ppublish

Résumé

We report the development, automation and validation of a 3D, microfluidic liver-on-a-chip for high throughput hepatotoxicity screening, the OrganoPlate LiverTox™. The model is comprised of aggregates of induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep) seeded in an extracellular matrix in the organ channel and co-cultured with endothelial cells and THP-1 monoblasts differentiated to macrophages seeded in the vascular channel of the 96 well Mimetas OrganoPlate 2-lane. A key component of high throughput screening is automation and we report a protocol to seed, dose, collect and replenish media and add assay reagents in the OrganoPlate 2-lane using a standard laboratory liquid handling robot. A combination of secretome measurements and image-based analysis was used to demonstrate stable 15 day cell viability, albumin and urea secretion. Over the same time-period, CYP3A4 activity increased and alpha-fetoprotein secretion decreased suggesting further maturation of the iHeps. Troglitazone, a clinical hepatotoxin, was chosen as a control compound for validation studies. Albumin, urea, hepatocyte nuclear size and viability staining provided Robust Z'factors > 0.2 in plates treated 72 h with 180 μM troglitazone compared with a vehicle control. The viability assay provided the most robust statistic for a Robust Z' factor = 0.6. A small library of 159 compounds with known liver effects was added to the OrganoPlate LiverTox model for 72 h at 50 μM and the Toxicological Prioritization scores were calculated. A follow up dose-response evaluation of select hits revealed the albumin assay to be the most sensitive in calculating TC

Identifiants

pubmed: 33359578
pii: S0300-483X(20)30306-1
doi: 10.1016/j.tox.2020.152667
pii:
doi:

Substances chimiques

Cytochrome P-450 CYP3A EC 1.14.14.1
Troglitazone I66ZZ0ZN0E

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

152667

Subventions

Organisme : NIH HHS
ID : S10 OD012269
Pays : United States
Organisme : NCATS NIH HHS
ID : UH3 TR000503
Pays : United States

Informations de copyright

Copyright © 2020. Published by Elsevier B.V.

Auteurs

Kristin M Bircsak (KM)

Mimetas US, Inc., Gaithersburg, MD, 20878, USA. Electronic address: k.bircsak@mimetas.com.

Richard DeBiasio (R)

Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.

Mark Miedel (M)

Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.

Alaa Alsebahi (A)

Mimetas US, Inc., Gaithersburg, MD, 20878, USA.

Ryan Reddinger (R)

Mimetas US, Inc., Gaithersburg, MD, 20878, USA.

Anthony Saleh (A)

Mimetas US, Inc., Gaithersburg, MD, 20878, USA.

Tongying Shun (T)

Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.

Lawrence A Vernetti (LA)

Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.

Albert Gough (A)

Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, 15260, USA. Electronic address: gough@pitt.edu.

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Classifications MeSH