z-logo
Premium
Numerical investigation of dynamic microorgan devices as drug screening platforms. Part II: Microscale modeling approach and validation
Author(s) -
Tourlomousis Filippos,
Chang Robert C.
Publication year - 2016
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.25824
Subject(s) - microscale chemistry , biological system , multiscale modeling , computer science , mechanics , chemistry , physics , mathematics , computational chemistry , biology , mathematics education
The authors have previously reported a rigorous macroscale modeling approach for an in vitro 3D dynamic microorgan device (DMD). This paper represents the second of a two‐part model‐based investigation where the effect of microscale (single liver cell‐level) shear‐mediated mechanotransduction on drug biotransformation is deconstructed. Herein, each cell is explicitly incorporated into the geometric model as single compartmentalized metabolic structures. Each cell's metabolic activity is coupled with the microscale hydrodynamic Wall Shear Stress (WSS) simulated around the cell boundary through a semi‐empirical polynomial function as an additional reaction term in the mass transfer equations. Guided by the macroscale model‐based hydrodynamics, only 9 cells in 3 representative DMD domains are explicitly modeled. Dynamic and reaction similarity rules based on non‐dimensionalization are invoked to correlate the numerical and empirical models, accounting for the substrate time scales. The proposed modeling approach addresses the key challenge of computational cost towards modeling complex large‐scale DMD‐type system with prohibitively high cell densities. Transient simulations are implemented to extract the drug metabolite profile with the microscale modeling approach validated with an experimental drug flow study. The results from the author's study demonstrate the preferred implementation of the microscale modeling approach over that of its macroscale counterpart. Biotechnol. Bioeng. 2016;113: 623–634. © 2015 Wiley Periodicals, Inc.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here