AI-driven biomaterial screening to accelerate medical device development

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.

This project intersects artificial intelligence (AI), biomaterials, medical devices, high-throughput screening (HTS) and regenerative medicine that are seamlessly aligned with the objective of responsible AI through "sustainable development in the research design".

Social-Economic Impact: Tissue engineering products for medical applications require a highly complex manufacturing process and evaluation protocol. In biomaterial research, figures have shown that the total costs for preclinical testing, clinical evaluation and medical follow up could reach GBP £602k or CAD $975k per patient. Expensive costs and lengthy cycles in biomaterial product development and empirical testing create a notable barrier in the pipeline of clinical translation. The proposed AI-informed biomaterial design
will lead to the creation of a computational platform that can be used for performance evaluation of tissue engineering products, e.g., scaffold biomaterials. The combinatorial simulation and machine learning approach can be easily adapted to test other application of tissue engineering, e.g., cell/ drug delivery as well tissue engineering for the replacement of other organs. We anticipate that this computational tool will accelerate the clinical translation of novel engineered products and mitigate some of the high costs associated with animal and human preclinical testing.

Patient Care: This project's population significance is linked to the widespread nature of VF and IVD disorders, documented personal and financial costs for it, and the lack for effective treatments for intractable VF and IVD tissue defects. Specifically, VF dysfunction is a significant public health problem and concern. This chronic laryngeal disease affects almost one-third of the general population in North America. The negative impact on quality of life in patients with defected VF are documented as significant as those of many chronic diseases such as angina pectoris, sciatica and chronic sinus infection. Unfortunately, effective treatments are not available for these perplexing and functionally debilitating vocal conditions. Given the unique anatomical and mechanical properties of VF, tissue engineering-based strategies are needed for defected VF replacement and regeneration. The success of this proposal will help develop biomaterials that are personalized and precise to the desired VF reparative therapeutic outcome.

Research Training: At least 2 PhD students and 3 post-doctoral trainees will be supported by this proposal. Given the highly cross-sectoral nature of this project, trainees in this research program will develop well-rounded research skills and competency that will contribute to the fields of computer science, material science and tissue engineering. Computational medicine and tissue engineering are growing fields in both academia and industry. Research specialists in these areas are in high demand and will guarantee our research trainees promising academic and career options and in turn contribute to the advancement of technology in the UK and Canada.

Innovation and wealth-creation for the UK and Canada: The foundational academic research of this project will have a significant and wide impact on the growing number of start-ups, SMEs and corporations in the UK and Canada that use AI and biomaterials as part of their research and revenue stream. This will help build relationships with industry to explore IP exploitation and clinical translation. Development of hydrogels for VF and IVD tissue engineering will have wide-ranging impact beyond the project objectives such as: (i) facilitate development of hydrogel coatings for medical implants; (ii) inform new medical devices development, including stents, meshes and support structures that may be derived from hydrogels (iii) enable new product development for pharmaceutical applications, e.g. as biocompatible delivery systems for active components.