Title: AI-driven biomaterial screening to accelerate medical device development
Funder: Economic and Social Research Council

Abstract: Conventional engineering processes of biomaterials are expensive and laborious that heavily relies on in vitro and in vivo models. The overarching goal of this project is to integrate artificial intelligence (AI), namely computer simulation and machine learning, into the pipeline of tissue engineering research. The success of this project will help effectively identify and optimize design parameters of biomaterials as well as reducing the reliance on animal and human testing for tissue engineering. Specific Aim 1: Combine machine learning with computer simulation techniques to identify possible optimal biomaterial designs. Our existing agent-based models of biomaterials (ABM-B) will be updated to generate synthetic data of cell activities and mechanical properties as function of material-related and tissue-related input parameters across temporal and spatial scales. Due to highly complex data outputs, multi-task machine learning (ML) models will be used to perform network analysis and further narrow down which biomaterial design would be optimal for specific tissue condition. Targeted biomaterial design will then be evaluated with empirical experiments in Specific Aims 2 and 3 below. Empirical data obtained will then be used to iteratively calibrate and verify ABM-B and ML models herein. Specific Aim 2: Develop AI-informed high-throughput screening (HTS) platform of biomaterials. We will leverage our expertise in HTS strategies for biomaterial discovery to design a new screening platform comprising a combinatorial library of cell-laden hydrogels. The design of this screening platform will be directed by ABM-B and ML models in Specific Aim 1. This will allow us to study cell phenotype of the Vocal fold (VF) and Intervertebral Disc (IVD)-specific cell lines in 3D within the hydrogel and macrophage phenotype in 2D at the surface of each hydrogel microenvironment. This will allow vast information to be gathered on cell biocompatibility and foreign body response simultaneously that will be captured and processed by microscopy and deep-learning based models, respectively. Specific Aim 3: Scale up the AI/HTS identified biomaterials for vocal fold (VF) and intervertebral disc (IVD) tissue engineering. With the most optimal conditions identified with the computation and HTS studies in terms of biocompatibility and foreign body response in Specific Aim 2, we will next fabricate scaled-up biomaterial constructs to fully characterize the mechanical characteristics, cellular responses and other outputs under the clinical application of VF and IVD repair. Specific Aim 4: Perform cost analysis of AI/ HTS versus conventional biomaterial design and evaluation. To validate our experimental workflow for optimised biomaterial development, a rigorous cost analysis will be performed comparing each biomaterial development approach. The analysis will compare a tissue-engineered product for VF and IVD repair which has been developed using a conventional hypothesis-driven development strategy and our AI-driven HTS methodology. The analysis will be focused on developing the "product" to technology readiness level (TRL) 4 (prior to preclinical animal models) as this is most relevant to the TRL level we will achieve in this proposal.

Lay abstract: 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.

Research activity codes: 5.2 CELLULAR AND GENE THERAPIES
Health categories: ORAL AND GASTROINTESTINAL;MUSCULOSKELETAL