Researchers at Colorado State University have developed an adaptive computational tool that uses advanced modeling to instantly predict how tissue engineering scaffolds, patches, and porous implants will guide tissue growth. This algorithm, which uses Finite Element Analysis (FEA), replaces slow and expensive in vivo (animal) and in vitro (bench-to) screening regimens with computer simulations that quickly, in minutes to hours, predict temporal in vivo tissue growth. The technology allows scientists to quickly screen and optimize scaffold designs and material selection, such as the new Arc-S gradient structure, to ensure they promote the regeneration of complex tissues, such as the tendon-to-bone junction. This breakthrough is helping the biomedical field accelerate product development and align with emerging NIH guidelines to move away from animal testing.
Regenerating complex tissues like the enthesis (the interface between bone and tendon) is critical for improving the high failure rates (up to 94% for rotator cuff repairs) seen in common orthopedic surgeries. Current tissue engineering efforts rely heavily on multi-material or single-material scaffolds, which often fail because they create abnormal stress concentrations, leading to scar tissue formation rather than native tissue regeneration. This novel computational platform addresses the challenge of designing scaffolds with continuous mechanical gradients to smoothly transition load, which is essential for guiding the fate of multiple cell types (like osteoblasts and tenocytes) simultaneously—a process that has been intractable with previous simulation or single-tissue approaches.
The disclosed technology is a computer-based algorithm that uses Finite Element Analysis (FEA) combined with proprietary scripting to model and optimize tissue engineering scaffold architectures and materials. The user inputs a base scaffold geometry (such as the novel Arc-S, which has curved fibers to create a mechanical gradient), material properties, loading conditions, and the cell types of interest (e.g., osteoblasts, tenocytes). The algorithm generates a detailed model and iteratively simulates the complex mechanical environment inside the scaffold, determining the strain and hydrostatic pressure on an element-by-element basis. This data is then passed through a “cellular controller,” which uses Tissue Healing Windows (THWs)—predetermined bounds of strain and pressure known to cause an anabolic (regenerative) response—to predict multi-tissue growth and remodeling. This iterative, computational process predicts results faster than existing benchtop and/or animal experiments, moving the assessment timeline from months to years down to days to weeks. The computational models generated during parametric studies showed that the resulting mechanical gradients in the Arc-S scaffold approached levels seen in the native rotator cuff (~2 orders of magnitude).
Available for Exclusive Licensing, Collaboration or Funding
TRL: 3
US Provisional Patent
Kirk McGilvray, PhD
Sam Winston
Steve Foster
Steve.Foster@colostate.edu
970-491-7100