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, NicoleYeeKeyNKY
• 金额: $ 10.73万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749590
• 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，以在可持续，快速和理性的框架中为再生医学创建生物材料。生物材料的传统工程过程是昂贵且费力的，这极大地阻碍了从长凳到床边的翻译。为了减轻这一未满足的挑战，我们将使用组合数值模拟和机器学习模型来生成和分析细胞生物材料相互作用的大规模和多模式合成数据。这种应用将有效地缩小材料选择，并导致假设驱动的经验实验进行模型验证。含有3D细胞水凝胶的新型高通量阵列将用于进行数千种细胞生物材料实验。我们将进一步扩展AI信息的材料设计，并应用于声带组织工程。最后，我们将使用常规假设驱动的方法对这种新的AI驱动的生物材料设计和筛选进行成本分析。我们预计，生物材料治疗剂中AI的实施将大大减少融资，时间和劳动力，同时加速用于医疗应用的个性化和精确的生物材料。