Summary
Mammalian cell engineering has emerged as a new ground-breaking modality for the development of cell based therapies to treat several hard-to-cure diseases, including cancer (T cell-based immunotherapies) and to produce molecule with diagnostic and therapeutic applications such as monoclonal antibodies (mAbs), that are now a dominant product class in the biopharmaceutical industry. However, the pipeline for efficient design-test-commercialization of the product is long and expensive, even more so when cells must be engineered with two or more transgenes, an increasing need in T cell-based therapies or drug production (e.g., cells engineered to produce enzyme and co-enzymes, or antibody cocktails). At the core of the problem is the competition for a finite number of intracellular resources, transcriptional and translational, that cause an unbalanced expression of products thus hampering the therapeutic effect. BURnOUT is an Artificial Intelligence and Machine Learning based software that will provide, in an automated manner, paired gene sequence optimisation to accelerate the process of mammalian cell engineering. BURnOUT will be validated in two different settings: one for the biopharmaceutics (engineered CHO cell lines for antibodies production) and one for cell therapy (engineering T cells for multiple CARs expression). The successful validation of the technology will be of trans-and multi-disciplinary interest and will have the goal of targeting the amplest variety of markets in Life Science, from AI to synthetic biology for cell and gene therapies and global cell technologies for drug industry. We envision that BURnOUT will respond to current strategic societal needs and challenges such as reduced costs of biopharmaceutics, and more effective treatment for cancer.
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| Web resources: | https://cordis.europa.eu/project/id/101157871 |
| Start date: | 01-09-2024 |
| End date: | 28-02-2026 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
Mammalian cell engineering has emerged as a new ground-breaking modality for the development of cell based therapies to treat several hard-to-cure diseases, including cancer (T cell-based immunotherapies) and to produce molecule with diagnostic and therapeutic applications such as monoclonal antibodies (mAbs), that are now a dominant product class in the biopharmaceutical industry. However, the pipeline for efficient design-test-commercialization of the product is long and expensive, even more so when cells must be engineered with two or more transgenes, an increasing need in T cell-based therapies or drug production (e.g., cells engineered to produce enzyme and co-enzymes, or antibody cocktails). At the core of the problem is the competition for a finite number of intracellular resources, transcriptional and translational, that cause an unbalanced expression of products thus hampering the therapeutic effect. BURnOUT is an Artificial Intelligence and Machine Learning based software that will provide, in an automated manner, paired gene sequence optimisation to accelerate the process of mammalian cell engineering. BURnOUT will be validated in two different settings: one for the biopharmaceutics (engineered CHO cell lines for antibodies production) and one for cell therapy (engineering T cells for multiple CARs expression). The successful validation of the technology will be of trans-and multi-disciplinary interest and will have the goal of targeting the amplest variety of markets in Life Science, from AI to synthetic biology for cell and gene therapies and global cell technologies for drug industry. We envision that BURnOUT will respond to current strategic societal needs and challenges such as reduced costs of biopharmaceutics, and more effective treatment for cancer.Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
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