Computational tools often help in studying complex parameters that are difficult to measure in experimental models, including fluid flow, stress, pressure, temperature, etc. In addition, they can also assist in optimising the design of a technology. This has led to their increased usage in the preclinical stages of drug discovery. In the last decade, there has been a rise in the use of microphysiological systems-based models, such as organoids and organs-on-chip and computational methods are emerging as an important tool in MPS investigations.
Some of areas where numerical or computational simulations play a role include understanding the fluid flow and mass transfer in the MPS model, development of nanoparticles, and optimizing the MPS devices.
Fluid Flow
One of the key components in the functioning of an organ-on chip device is the fluid flow which governs other parameters, such as velocity, shear stress, concentration of vital parameters (oxygen, glucose), and temperature. The flow of culture medium also ensures the presence of adequate nutrients and thus, regulating and understanding the flow properties is critical to the functioning of organ chips. A research group, using a combination of experimental and numerical simulations, found that an increase in shear stress could lead to an increase inactive oxygen species. In another study, researchers developed a computational model of a microfluidic device and incorporated flow-induced shear stress to monitor cell-secreted biomolecules. Using numerical simulations, researchers tested the oxygen concentration in the interior of a tumor spheroid in response to two different velocities of the culture medium. They found that lower velocity corrosponded to insufficient oxygen concentration that is not amenable for spheroid growth.
Figure: Fluid flow in an organ chip.
(Reproduced from Carvalho et al. 2021 under CC license)
Temperature is another critical parameter as it can influence not just the environment, but also the function and behavior of the cells. Using a simulation involving heat transfer and fluid flow, a research group was able to obtain the temperature gradient that the proposed chip could reach, and they also showed it was capable of mimicking physiological temperatures.
Nanoparticle Simulations
Nanoparticles have generated substantial interest in the past decades due to their use as a drug deleivery system. However, many of their properties, including size and shape involve optimisation to generate desired cellular response. Researchers are performing computational and experimental studies to understand the transport of nanoparticles within organ on chip devices. In a study, the correlation between the diameter of nanoparticles and fluid flow was determined. Numerical simulations also revealed information regarding the effect of tumour microenvironment on the dynamic transport behavior of nanoparticles.
System optimisation
Using numerical simulations to optimise the organ on chip device is one of the critical steps during its design. Thus, researchers can accelerate the development of more efficient models in a time and cost effective manner. For example, one study developed a microfluidc chip to screen individual cancer cells, and simulations were performed to understand the timescale for autophagy, a process by which cells remove and recycle damaged organelles. This allowed the researchers to estimate the timescale of viability of the experiment, which in turn saved time and resources.
During the design of microfluidic devices, micropillars are often added to increase the surface/volume ratio which would in turn increase the efficiency of the chip. Numerical simulations have often helped to optimize the design and space between the micropillars for estimating the appropriate diffusion rates of the nutrients.
Thus, computational models can help in accelerating the growth of organ on chip technology. However, certain challenges still remain. For example, most computational studies do not provide details regarding the simulations, which make it difficult to reproduce these studies. In addition, as the organ on chip devices become increasingly complex involving several physical and chemical process along with biological events, it becomes challenging to model these in a numerical model.
However, the field of computational tools has advanced at a tremendous pace in the recent years, and continues to grow, indicating the very strong role this field is currently playing and would continue to do in the area of developing microphysiological systems models.
