Highly Biomimetic Proregenerative Scaffold for Personalized Nerve Repair
According to Allied Market Research, the market for nerve repair and regeneration is expected to reach €11 billion by 2023, with an annual increase of 13%. Autograft, the gold standard, only achieves a success rate of about 45%, d...
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Información proyecto ProRegScaffold
Duración del proyecto: 27 meses
Fecha Inicio: 2021-04-20
Fecha Fin: 2023-07-31
Líder del proyecto
KAROLINSKA INSTITUTET
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
192K€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
According to Allied Market Research, the market for nerve repair and regeneration is expected to reach €11 billion by 2023, with an annual increase of 13%. Autograft, the gold standard, only achieves a success rate of about 45%, despite intrinsic drawbacks including availability and comorbidities. Neural scaffolds offer the bright prospect of nerve injury treatment by guiding axon sprouting and creating a permissive microenvironment. However, current products including NeuraGen® and Neuromaix® failed to find their way towards widespread clinical practice due to their dimensional instability and the mismatch of regenerated axons, as well as showing no effect on the intrinsic regenerative capability of neurons. To address the current technological gap, this project aims to develop a proregenerative scaffold (ProRegScaffold) that completely mimic the microenvironment of host tissues to guide axonal growth, promote regeneration and stimulate integration into the existing healthy tissue. The ProRegScaffold comprises of chitosan/collagen microchannels resembling the geometries of native nervous network, which will be tailored by precisely controlling the growth of ice crystals within digitally predefined moulds. This is an automated method amenable to low-cost and large-scale production. Near infrared light responsive lipid nanoparticles will be introduced and filled with growth factors to activate the intrinsic regenerative capability of neurons. Moreover, I will complement the finite-element method (FEM) with machine learning algorithms to accelerate the design phase, analyze structural mechanics and provide proper parameters of scaffolds allowing faster axon regrowth. This project represents cutting-edge research to screen structure-activity relationships for effective nerve repair, allowing the establishment of a practical personalized neural scaffold.