Mechanistic Computational Models of Epithelial Cell Transporters-the Adorned Heroes of Pharmacokinetics.

computational mechanistic models epithelial membrane lumped parameter pharmacokinetics transporter

Journal

Frontiers in pharmacology
ISSN: 1663-9812
Titre abrégé: Front Pharmacol
Pays: Switzerland
ID NLM: 101548923

Informations de publication

Date de publication:
2021
Historique:
received: 21 09 2021
accepted: 20 10 2021
entrez: 22 11 2021
pubmed: 23 11 2021
medline: 23 11 2021
Statut: epublish

Résumé

Epithelial membrane transporter kinetics portray an irrefutable role in solute transport in and out of cells. Mechanistic models are used to investigate the transport of solutes at the organ, tissue, cell or membrane scale. Here, we review the recent advancements in using computational models to investigate epithelial transport kinetics on the cell membrane. Various methods have been employed to develop transport phenomena models of solute flux across the epithelial cell membrane. Interestingly, we noted that many models used lumped parameters, such as the Michaelis-Menten kinetics, to simplify the transporter-mediated reaction term. Unfortunately, this assumption neglects transporter numbers or the fact that transport across the membrane may be affected by external cues. In contrast, more recent mechanistic transporter kinetics models account for the transporter number. By creating models closer to reality researchers can investigate the downstream effects of physical or chemical disturbances on the system. Evidently, there is a need to increase the complexity of mechanistic models investigating the solute flux across a membrane to gain more knowledge of transporter-solute interactions by assigning individual parameter values to the transporter kinetics and capturing their dependence on each other. This change results in better pharmacokinetic predictions in larger scale platforms. More reliable and efficient model predictions can be made by creating mechanistic computational models coupled with dedicated

Identifiants

pubmed: 34803720
doi: 10.3389/fphar.2021.780620
pii: 780620
pmc: PMC8599978
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

780620

Informations de copyright

Copyright © 2021 King, Giselbrecht, Truckenmüller and Carlier.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Jasia King (J)

Department of Cell Biology-Inspired Tissue Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands.
Department of Instructive Biomaterials Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands.

Stefan Giselbrecht (S)

Department of Instructive Biomaterials Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands.

Roman Truckenmüller (R)

Department of Instructive Biomaterials Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands.

Aurélie Carlier (A)

Department of Cell Biology-Inspired Tissue Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands.

Classifications MeSH