2D 3D MULTISCALE COMPUTATIONAL MODELING OF DYNAMIC MICROORGAN DEVICES AS DRUG SCREENING PLATFORMS

摘要

The ability to incorporate three-dimensional (3D) hepatocyte-laden hydrogel constructs using layered fabrication approaches into devices that can be perfused with drugs enables the creation of dynamic microorgan devices (DMDs) that offer an optimal analog of the in vivo liver metabolism scenario. The dynamic nature of such in vitro metabolism models demands reliable numerical tools to determine the optimum process, material, and geometric parameters for the most effective metabolic conversion of the perfused drug into the liver microenvironment. However, there is a current lack of literature that integrates computational approaches to guide the optimum design of such devices. The groundwork of the present numerical study has been laid by our previous study, where the authors modeled in 2D an in vitro DMD of arbitrary dimensions and identified the modeling challenges towards meaningful results. These constructs are hosted in the chamber of the microfluidic device serving as walls of the microfluidic array of channels through which a fluorescent drug substrate is perfused into the microfluidic printed channel walls at a specified volumetric flow rate assuring Stokes flow conditions (Re＜＜1). Due to the porous nature of the hydrogel walls, a metabolized drug product is collected at the outlet port. A rigorous FEM based modeling approach is presented for a single channel parallel model geometry (1 free flow channel with 2 porous walls), where the hydrodynamics, mass transfer and pharmacokinetics equations are solved numerically in order to yield the drug metabolite concentration profile at the DMD outlet. The fluid induces shear stresses are assessed both in 3D, with only 27 cells modeled as single compartment voids, where all of the enzymatic reactions are assumed to take place. In this way, the mechanotransduction effect that alters the hepatocyte metabolic activity is assessed for a small scale model. This approach overcomes the numerical limitations imposed by the cell density (～10~(12) cells/m~3) of the large scale DMD device. In addition, a compartmentalization technique is proposed in order to assess the metabolism process at the subcellular level. The numerical results are validated with experiments to reveal the robustness of the proposed modeling approach and the necessity of scaling the numerical results by preserving dynamic and biochemical similarity between the small and large scale model.