Filtration & fluid control How Verheyen
employed computational fluid dynamics
to design the physical experimentation protocol.
to use them, they can be turned into fluid and travel through a nozzle. “Once they come out the other side, they immediately start self-healing and regaining their elastic cell-like properties, so they’ll maintain themselves in whatever shape you’ve patterned them in,” Verheyen explains. “That makes them really useful, because it allows you to deliver them non-invasively, so you don’t have to cut patients open to get them there. Once delivered, they self-heal and hold their shape again, and so that kind of combination of properties makes them really attractive from both a tissue engineering standpoint and from an in vitro or organ fabrication standpoint.”
The project to create the granular hydrogels proved more challenging than expected, however, requiring him to experiment with changing features of the gels so that they were optimised for delivery by injection. “The initial work was a lot of very frustrating trial and error,” Verheyen says. “It doesn’t sound so hard. You just make these little squishy building blocks; you compact them and then you should get them to inject – we found that was very much not the case.” With the arrival of the pandemic in 2020, Verheyen worked on Covid research, during which he acquired computational modelling skills. He realised that some of the data science and machine learning tools he’d been using could be applied to the work on hydrogels. Computational fluid dynamics (CFD) involves using computers to model the flow of liquids in a particular environment, making it possible to predict how a fluid will behave. In medical devices, the modelling could help determine the optimal size and shape of a device for delivering certain drugs, or predict how blood will flow in a catheter. The advantages of using computer models particularly at the start of a project are they are a cheaper and less time-intensive way of testing a hypothesis than using physical materials.
Rigorous and reproducible Initially, his colleagues were a little sceptical; and rightly so, according to Verheyen. After a few preliminary test building models, he decided that the approach was worth pursuing. He made sure
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it was as rigorous as possible, he says: “One of the things that is frightening about the machine learning approach is that you can throw something in, and it will spit something out, but you’re not always sure if it’s useful.” The work of building a computational model was time-consuming. Verheyen’s experience with Covid-19 research had primed him to make sure it was a “rigorous, reproducible, computational pipeline”. It took several months before he reached a finalised workflow that he was confident would lead to a good model that could be robustly validated. The work involved breaking down the process of assembling the hydrogels into separate stages, each of which was then modelled separately using data from the earlier experiments. In the first stage, the model analysed how bioblock properties are affected by the starting material of the blocks and how they are assembled. In the second stage, the bioblocks were packed together to form the granular hydrogels. The modelling enabled the team to identify the different factors influencing the injectability of the final gel. These included the size and stiffness of the bioblocks, the viscosity of the interstitial fluid between the blocks, and the dimensions of the needle and syringe used to inject the gel. Having modelled this process from beginning to end, the researchers are now able to use the model to predict the best way to create a material with the qualities needed for a particular application, instead of going through a laborious process of trial- and-error for each new material. One of the things Verheyen finds attractive about the approach is that having used the data to build a model, it can then be shared through the open- source software repository GitHub, making it fully reproducible by anyone anywhere in the world. Rather than simply picking and sharing the three most successful trials, he says, researchers share everything: “You’re saying, ‘Here’s everything that we did, some of which was failed, some of which was OK, some of which went well. I don’t fully understand what’s going on behind the scenes there, but I can leverage these tools that are really effective at learning from these high-dimensional spaces.’ You
Medical Device Developments /
www.nsmedicaldevices.com
Connor Verheyen
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