Periodic Reporting for period 1 - Regeneration (PeRsonalised treatmEnts and pain manaGEmeNt for rEpaiR of Aged bone Tissue wIth quantum mOlecular resoNance)
Okres sprawozdawczy: 2023-10-01 do 2025-09-30
The REGENERATION project proposes an innovative, bioinspired, and integrated approach to the development of engineered bone tissues mimicking healthy and aged bone that will be used in bioreactors as in vitro bone models for preclinical drug screening and for understanding the possible effect obtained in basic physiology and physiotherapy studies on elders.
At the heart of the project lies the patented technology of Quantum Magnetic Resonance (QMR), a technology already in use in other medical fields like surgery, physiotherapy, aesthetic medicine and ophthalmology. In the context of REGENERATION, development results of lab-scale models of human bone tissue obtained with and without QMR will be compared.
The project accumulates expertise on different fields of science and technology, including innovative techniques in scaffold manufacturing, cell culture, and long-term tissue preservation, bone damage treatment together with mathematical modelling, advanced imaging and image analysis techniques, bioinformatics, artificial intelligence and machine learning applied to the compound search / discovery process.
The project addresses a real challenge related to the optimization of lab scale modeling of human tissues. In fact nowadays the common practice is related to the use of either in vitro models, 2D cultured cells, or in vivo models of animals. Both have disadvantages stemming from reduced complexity related to human tissue, inability to model correctly mechanisms like intercellular contacts, and ethics.
The project response to this challenge combines existing technologies in biomedical research, such as a bioreactor enabling 3D cell culture, QMR equipment to facilitate cell culture optimization, along with ICT technologies including ML / AI to augment knowledge relevant to human bone tissue, producing adequate scaffolds that can enable the development of human bone tissue like models.
At the heart of the project lies the patented technology of Quantum Magnetic Resonance (QMR), a technology already in use in other medical fields like surgery, physiotherapy, aesthetic medicine and ophthalmology. In the context of REGENERATION, development results of lab-scale models of human bone tissue obtained with and without QMR will be compared.
The project accumulates expertise on different fields of science and technology, including innovative techniques in scaffold manufacturing, cell culture, and long-term tissue preservation, bone damage treatment together with mathematical modelling, advanced imaging and image analysis techniques, bioinformatics, artificial intelligence and machine learning applied to the compound search / discovery process.
The project addresses a real challenge related to the optimization of lab scale modeling of human tissues. In fact nowadays the common practice is related to the use of either in vitro models, 2D cultured cells, or in vivo models of animals. Both have disadvantages stemming from reduced complexity related to human tissue, inability to model correctly mechanisms like intercellular contacts, and ethics.
The project response to this challenge combines existing technologies in biomedical research, such as a bioreactor enabling 3D cell culture, QMR equipment to facilitate cell culture optimization, along with ICT technologies including ML / AI to augment knowledge relevant to human bone tissue, producing adequate scaffolds that can enable the development of human bone tissue like models.
The REGENERATION project has a focus on the experimentation of the Quantum Magnetic Resonance (QMR) patented technology for optimized human bone tissue model development. To this end an adaptation of the relevant QMR equipment has been done in the first phase of the project, so that the relevant equipment is best fit to the needs of the project. More specifically a device reduced and size and more compatible has been produced and handed over to the consortium for relevant experimentation.
As a second step the consortium is dealing at this point with the appropriate experimentation layout including the Cellex bioreactor and the updated QMR equipment. More specifically different options are considered related to the use of electrodes for applying the QMR technology to the bioreactor. Upon testing these options the project will be possible to engage into testing of how the combined technologies can be used for optimized human tissue modeling.
Further to the real experimentation of the project, predictive modeling of cell growth in bioreactors has been performed using elastic net regression. Two types of bioreactors have been used in this context with 4 and 12 scaffolds respectively. The predictive models address the number of cells based on a number of features from a dataset of biological experiments. High dimensional data was used and cross validation was performed to assess the model performance. Further research steps in this direction involve model enhancement and validation.
Finally, work is done on requirement collection related to the scaffold design. To this end micro computational tomography data have been used. This data enable the determination of parameters that characterize the exact geometry of human bone tissue and which have to be modelled by our project effort. The use of AI is deemed necessary in providing adequate algorithms that could use this data for producing relevant scaffolds that would lead to 3D bio printing.
As a second step the consortium is dealing at this point with the appropriate experimentation layout including the Cellex bioreactor and the updated QMR equipment. More specifically different options are considered related to the use of electrodes for applying the QMR technology to the bioreactor. Upon testing these options the project will be possible to engage into testing of how the combined technologies can be used for optimized human tissue modeling.
Further to the real experimentation of the project, predictive modeling of cell growth in bioreactors has been performed using elastic net regression. Two types of bioreactors have been used in this context with 4 and 12 scaffolds respectively. The predictive models address the number of cells based on a number of features from a dataset of biological experiments. High dimensional data was used and cross validation was performed to assess the model performance. Further research steps in this direction involve model enhancement and validation.
Finally, work is done on requirement collection related to the scaffold design. To this end micro computational tomography data have been used. This data enable the determination of parameters that characterize the exact geometry of human bone tissue and which have to be modelled by our project effort. The use of AI is deemed necessary in providing adequate algorithms that could use this data for producing relevant scaffolds that would lead to 3D bio printing.
The project integrates expertise on different fields of science and technology, including innovative techniques in scaffold manufacturing, cell culture, and long-term tissue preservation, bone damage treatment together with mathematical modelling, advanced imaging and image analysis techniques, bioinformatics, artificial intelligence and machine learning applied to the compound search / discovery process. This trans-disciplinarity creates a strong potential for high impact.
At the core of the REGENERATION projects lies a methodology for knowledge sharing and upskilling of the trans-disciplinary team of the project. The staff engaged by the different partners in the project secondments is characterized by different scientific and technological backgrounds, bigger of smaller experience, coming from academia, research or business world. Envisaged training and knowledge transfer aims at combining this overall wealth of knowledge and expertise in producing a coherent team that can stand up to the challenge of producing optimized human tissue models. The project is expected to significantly contribute to this end to the careers of the secondees.
The final outcome of the project being a valid methodology for optimized human bone tissue model development has a strong market potential, being quite innovative, enabling the training of less experienced personnel to participate in highly innovative activities, and addressing a market segment that is quite extended and growing more and more important as aging population percentages increase worldwide.
At the core of the REGENERATION projects lies a methodology for knowledge sharing and upskilling of the trans-disciplinary team of the project. The staff engaged by the different partners in the project secondments is characterized by different scientific and technological backgrounds, bigger of smaller experience, coming from academia, research or business world. Envisaged training and knowledge transfer aims at combining this overall wealth of knowledge and expertise in producing a coherent team that can stand up to the challenge of producing optimized human tissue models. The project is expected to significantly contribute to this end to the careers of the secondees.
The final outcome of the project being a valid methodology for optimized human bone tissue model development has a strong market potential, being quite innovative, enabling the training of less experienced personnel to participate in highly innovative activities, and addressing a market segment that is quite extended and growing more and more important as aging population percentages increase worldwide.