How to make hydrogels more injectable to e.g. treat diseased tissues

Good news! Of course, this is based on computer modeling! In practice, this may not work.

"Gel-like materials that can be injected into the body hold great potential to heal injured tissues or manufacture entirely new tissues.  ...
researchers have created a set of computational models to predict the material’s structure, mechanical properties, and functional performance outcomes. The researchers hope that their new framework could make it easier to design materials that can be injected for different types of applications, which until now has been mainly a trial-and-error process. ...
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, depending on the conditions, 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. ...
[researchers] now plan to use this modeling approach to try to develop materials that could be used for medical applications such as repairing heart defects or delivering drugs to the gastrointestinal tract. ..."

From the highlights and abstract:

"Highlights

• Built flexible ML pipeline for robust model selection, validation, and explanation

• Applied modular ML approach to stepwise empirical material development workflow

• Optimized hydrogel bioblocks, granular matrices, complex rheology, and extrudability

• Produced data-driven models and extracted human-readable predictive design insights

Progress and potential

Granular hydrogel matrices are promising for biomedical applications ranging from extrusion-based bioprinting to injectable tissue engineering. However, they remain challenging to design, assemble, and optimize. Each development stage involves multidimensional input-output spaces affected by poorly understood multi-scale, multi-physics phenomena. Here, we demonstrate the utility of a flexible and modular machine learning (ML) approach to advance complex materials in a stepwise fashion. We apply our ML approach to automatically construct, validate, and explain predictive design frameworks for each set of empirical results. These data-driven models allow one to assess each experimental design space and provide condensed design insights extracted from high-dimensional input-output maps. The resulting bioblock materials have broad biomedical applications, yet our approach should be applicable for data-driven advancement of any complex material system.

Summary

Granular hydrogel matrices have emerged as promising candidates for cell encapsulation, bioprinting, and tissue engineering. However, it remains challenging to design and optimize these materials given their broad compositional and processing parameter space. Here, we combine experimentation and computation to create granular matrices composed of alginate-based bioblocks with controlled structure, rheological properties, and injectability profiles. A custom machine learning pipeline is applied after each phase of experimentation to automatically map the multidimensional input-output patterns into condensed data-driven models. These models are used to assess generalizable predictability and define high-level design rules to guide subsequent phases of development and characterization. Our integrated, modular approach opens new avenues to understanding and controlling the behavior of complex soft materials."

How to make hydrogels more injectable | MIT News | Massachusetts Institute of Technology A new computational framework could help researchers design granular hydrogels to repair or replace diseased tissues.

Integrated data-driven modeling and experimental optimization of granular hydrogel matrices (open access)

Graphical abstract