Abstract
Microfluidic tumour spheroid-on-a-chip platforms enable control of spheroid size and their microenvironment and offer the capability of high-throughput drug screening, but drug supply to spheroids is a complex process that depends on a combination of mechanical, biochemical, and biophysical factors. To account for these coupled effects, many microfluidic device designs and operating conditions must be considered and optimized in a time- and labour-intensive trial-and-error process. Computational modelling facilitates a systematic exploration of a large design parameter space via in silico simulations, but the majority of in silico models apply only a small set of conditions or parametric levels. Novel approaches to computational modelling are needed to explore large parameter spaces and accelerate the optimization of spheroid-on-a-chip and other organ-on-a-chip designs. Here, we report an efficient computational approach for simulating fluid flow and transport of drugs in a high-throughput arrayed cancer spheroid-on-a-chip platform. Our strategy combines four key factors: i) governing physical equations; ii) parametric sweeping; iii) parallel computing; and iv) extensive dataset analysis, thereby enabling a complete “full-factorial” exploration of the design parameter space in combinatorial fashion. The simulations were conducted in a time-efficient manner without requiring massive computational time. As a case study, we simulated >15,000 microfluidic device designs and flow conditions for a representative multicellular spheroids-on-a-chip arrayed device, thus acquiring a single dataset consisting of ∼10 billion datapoints in ∼95 GBs. To validate our computational model, we performed physical experiments in a representative spheroid-on-a-chip device that showed excellent agreement between experimental and simulated data. This study offers a computational strategy to accelerate the optimization of microfluidic device designs and provide insight on the flow and drug transport in spheroid-on-a-chip and other biomicrofluidic platforms.
Highlights
Several studies have focused on the simulations of drug delivery to multicellular tumour spheroids (MCTSs) in a spheroid-on-a-chip platform (Kim et al, 2008; Moshksayan et al, 2018)
These simulations explored the effect of MF device geometry including microwell and microchannel dimensions on drug supply and uptake by MCTSs
The results revealed that the increase in the flow rate of drug treatment significantly enhanced the penetration of the MCTSs with a drug (Figure 4G)
Summary
Over the past few decades, microfluidics (MFs) has emerged as a powerful platform for fundamental and applied research in cell biology, soft robotics, medicine, drug screening, materials science, and analytical chemistry (Whitesides, 2006; Tian and Finehout, 2009; Young et al, 2012; Pak et al, 2015; Rajendra et al, 2015; Moore et al, 2017; Humayun et al, 2018; Khuu et al, 2019; Gevorkian et al, 2021). The CFD model provided guidelines for the design of MF devices operating within a range of physiological shear stresses while ensuring efficient transport of biomolecules through the membrane In another computational study, a mathematical model coupled with numerical simulations led to an optimal design of MF devices for studies of endothelial cell migration and angiogenesis (Kuzmic et al, 2019). Several studies have focused on the simulations of drug delivery to multicellular tumour spheroids (MCTSs) in a spheroid-on-a-chip platform (Kim et al, 2008; Moshksayan et al, 2018) These simulations explored the effect of MF device geometry including microwell and microchannel dimensions on drug supply and uptake by MCTSs. Recently, a numerical study of the design of a tumour-on-a-chip platform was performed to determine its optimal performance in screening multidrug combinations (Hajari et al, 2021)
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