To aid the development of gel-like materials — which are made from microscale building blocks akin to squishy LEGOs and can be injected into the body to heal injured tissues or manufacture entirely new tissues — MIT and Harvard University researchers have created a set of computational models to predict the material’s structure and mechanical properties, as well as functional performance outcomes.
The aim is to make it easier to design materials that can be injected for different types of applications, which has been mainly a trial-and-error process.
“It’s really exciting from a material standpoint and from a clinical application standpoint,” said Ellen Roche, Associate Professor, MIT. “More broadly, it’s a nice example of taking labbased data and synthesizing it into something usable that can give you predictive guidelines that could be applied to things beyond these hydrogels.”
When individual hydrogel blocks are densely compacted together, they form a gel-like material known as a granular matrix. These materials can act as a solid or a liquid, which makes them good candidates for applications such as 3D-bioprinting engineered tissues. Once injected or implanted into the body, they could release drugs or help to regenerate injured tissue.
While working in Harvard Professor Jennifer Lewis’ lab, Connor Verheyen, lead author and grad student in the Harvard-MIT Program in Health Sciences and Technology, began trying to figure out how to get these materials to be reliably injectable — a daunting task.
“That spurred the effort to take the empirical data, turn it into something that a machine could read and work with, and then ask it to build a predictive map that we could interrogate to help us understand what was going on and how to go to the next step,” he said.
To create the design framework, the team broke the assembly process down into several stages. In the first stage, the model analyzed how bioblock properties are affected by the starting material of the blocks and how they are assembled. In the next stage, the bioblocks are packed together to form structures called granular hydrogels. Through their modeling, the researchers identified several factors that influence the injectability of the final gel, including 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.
“Our long-term goal was to get to the point where we had reliable and predictable injection properties, because that was something that we really struggled with in the lab — getting these materials to flow properly,” said Verheyen.
Some team members now plan to use this modeling approach to try to develop materials that could be used for repairing heart defects or delivering drugs to the gastrointestinal tract. The researchers have also made their models and data available online for other labs to use.
“It’s all open source, and hopefully it will reduce the amount of frustration with issues that you might have reproducing something that happened in another lab, or even within one lab when you’re transferring knowledge from one person to another,” said Roche.
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