Machine Learning Approaches to 3D Models for Drug Screening

被引：0
作者：
Victor Allisson da Silva [1 ]
Ruchi Sharma [1 ]
Ekaterina Shteinberg [1 ]
Vaidehi Patel [1 ]
Lavanya Bhardwaj [1 ]
Tania Garay [1 ]
Bosco Yu [1 ]
Stephanie M. Willerth [1 ]
机构：
[1] Department of Mechanical Engineering, University of Victoria, Victoria, V8W 2Y2, BC
[2] Axolotl Biosciences, 3800 Finnerty Road, Victoria, V8W 2Y2, BC
[3] Division of Medical Sciences, University of Victoria, 3800 Finnerty Road, Victoria, V8W 2Y2, BC
[4] Centre for Advanced Materials and Technologies, University of Victoria, 3800 Finnerty Road, Victoria, V8W 2Y2, BC
[5] School of Biomedical Engineering, University of British Columbia, Vancouver, V6T 1Z4, BC
来源：
Biomedical Materials & Devices | 2024年 / 2卷 / 2期
基金：
加拿大健康研究院; 加拿大自然科学与工程研究理事会;
关键词：
3D models; Artificial intelligence; Bioprinting; Drug screening; Machine learning; Tissue engineering;
D O I：
10.1007/s44174-023-00142-4
中图分类号：
学科分类号：
摘要：
The creation of precise, functional 3D tissues can enable effective drug screening as well as advancements in regenerative medicine. However, the inherent limitations present during the development of these 3D models pose challenges when manufacturing. This review examines the obstacles associated with the pre-processing, processing, and post-processing phases when bioprinting that must be addressed to overcome these constraints and produce reproducible tissue constructs. These obstacles include critical elements such as cell composition, biomaterial formulation, processing techniques, media conditions, 3D structure design, model conditioning, and 3D model quantification. This review identifies these inherent process limitations when making 3D tissue models. The review then leverages machine learning tools that have proven successful in related contexts and discusses them in the context of 3D tissue models. The review aims to inspire researchers to explore and implement innovative machine learning techniques for developing 3D models by drawing insights from studies taken from a variety of engineering domains. This curated compilation covers a wide array of machine learning solutions to navigate the intricate complexities of 3D model creation, pushing the boundaries of tissue engineering. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC 2023.
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收藏
页码：695 / 720
页数：25
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