Canada-UK AI 2019 : AI-driven biomaterial screening to accelerate medical device development and translation

加拿大-英国 AI 2019：人工智能驱动的生物材料筛选，加速医疗器械开发和转化

• 批准号: 548623-2019
• 负责人: LiJessen, NicoleYeeKey
• 金额: $ 12.6万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=699918
• 关键词: Canada; UK; AI; 2019; driven

Our multi-disciplinary team will leverage the power of artificial intelligence, computer simulation and high-throughput screening approaches to create biomaterials for regenerative medicine in a sustainable, rapid and rational framework. Conventional engineering processes of biomaterials are expensive and laborious that has significantly impeded the translation from bench to bedside. To mitigate this unmet challenge, we will use combinatorial numerical simulations and machine learning models to generate and analyze large-scale and multi-modality synthetic data of cell-biomaterial interactions. Such application will effectively narrow down material choices and lead to hypothesis-driven empirical experiments for model verification. Novel high-throughput arrays containing 3D cell-laden hydrogels will be used to conduct thousands of cell-biomaterial experiments. We will further scale up the AI-informed material design and apply to vocal fold tissue engineering. Lastly, we will perform a cost analysis of this new AI-driven biomaterial design and screening with that of the conventional hypothesis-driven approach. We anticipate that the implementation of AI in biomaterial therapeutics will dramatically reduce the finance, time and labor while accelerate the development of personalized and precise biomaterials for medical applications.

我们的多学科团队将利用人工智能、计算机模拟和高通量筛选方法的力量，在可持续、快速和合理的框架内为再生医学创造生物材料。传统的生物材料工程过程既昂贵又费力，这严重阻碍了从长凳到床边的转变。为了缓解这一尚未解决的挑战，我们将使用组合数值模拟和机器学习模型来生成和分析大规模和多模式的细胞-生物材料相互作用的合成数据。这样的应用将有效地缩小材料的选择范围，并导致模型验证的假设驱动的实证实验。含有3D细胞水凝胶的新型高通量阵列将被用于进行数千个细胞-生物材料实验。我们将进一步扩大人工智能材料设计的规模，并将其应用于声带组织工程。最后，我们将对这种新的人工智能驱动的生物材料设计和筛选与传统的假设驱动方法进行成本分析。我们预计，人工智能在生物材料治疗中的实施将大大减少资金、时间和人力，同时加快医疗应用的个性化和精确化生物材料的发展。