Literature DB >> 33716174

Fabrication approaches for high-throughput and biomimetic disease modeling.

Mackenzie L Grubb1, Steven R Caliari2.   

Abstract

There is often a tradeoff between in vitro disease modeling platforms that capture pathophysiologic complexity and those that are amenable to high-throughput fabrication and analysis. However, this divide is closing through the application of a handful of fabrication approaches-parallel fabrication, automation, and flow-driven assembly-to design sophisticated cellular and biomaterial systems. The purpose of this review is to highlight methods for the fabrication of high-throughput biomaterial-based platforms and showcase examples that demonstrate their utility over a range of throughput and complexity. We conclude with a discussion of future considerations for the continued development of higher-throughput in vitro platforms that capture the appropriate level of biological complexity for the desired application. STATEMENT OF SIGNIFICANCE: There is a pressing need for new biomedical tools to study and understand disease. These platforms should mimic the complex properties of the body while also permitting investigation of many combinations of cells, extracellular cues, and/or therapeutics in high-throughput. This review summarizes emerging strategies to fabricate biomimetic disease models that bridge the gap between complex tissue-mimicking microenvironments and high-throughput screens for personalized medicine.
Copyright © 2021 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Biomaterials; Disease models; High-throughput

Mesh:

Substances:

Year:  2021        PMID: 33716174      PMCID: PMC8433272          DOI: 10.1016/j.actbio.2021.03.006

Source DB:  PubMed          Journal:  Acta Biomater        ISSN: 1742-7061            Impact factor:   10.633


  253 in total

1.  A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays.

Authors: 
Journal:  J Biomol Screen       Date:  1999

Review 2.  Microfluidics for cell-based high throughput screening platforms - A review.

Authors:  Guansheng Du; Qun Fang; Jaap M J den Toonder
Journal:  Anal Chim Acta       Date:  2015-11-22       Impact factor: 6.558

Review 3.  Cellular imaging in drug discovery.

Authors:  Paul Lang; Karen Yeow; Anthony Nichols; Alexander Scheer
Journal:  Nat Rev Drug Discov       Date:  2006-04       Impact factor: 84.694

4.  3D printing of step-gradient nanocomposite hydrogels for controlled cell migration.

Authors:  Andisheh Motealleh; Betül Çelebi-Saltik; Nihal Ermis; Sacha Nowak; Ali Khademhosseini; Nermin Seda Kehr
Journal:  Biofabrication       Date:  2019-08-22       Impact factor: 9.954

Review 5.  Advances in the Fabrication of Biomaterials for Gradient Tissue Engineering.

Authors:  Chunching Li; Liliang Ouyang; James P K Armstrong; Molly M Stevens
Journal:  Trends Biotechnol       Date:  2020-07-07       Impact factor: 19.536

6.  Spatiotemporal Control of Viscoelasticity in Phototunable Hyaluronic Acid Hydrogels.

Authors:  Erica Hui; Kathryn I Gimeno; Grant Guan; Steven R Caliari
Journal:  Biomacromolecules       Date:  2019-10-22       Impact factor: 6.988

Review 7.  Microfluidic techniques for high throughput single cell analysis.

Authors:  Amy Reece; Bingzhao Xia; Zhongliang Jiang; Benjamin Noren; Ralph McBride; John Oakey
Journal:  Curr Opin Biotechnol       Date:  2016-03-28       Impact factor: 9.740

8.  Increased Cell Traction-Induced Prestress in Dynamically Cultured Microtissues.

Authors:  Mathieu A J van Kelle; Nilam Khalil; Jasper Foolen; Sandra Loerakker; Carlijn V C Bouten
Journal:  Front Bioeng Biotechnol       Date:  2019-03-12

9.  A microfabricated platform with hydrogel arrays for 3D mechanical stimulation of cells.

Authors:  Haijiao Liu; Jenna Usprech; Yu Sun; Craig A Simmons
Journal:  Acta Biomater       Date:  2015-11-29       Impact factor: 8.947

10.  Lung Microtissue Array to Screen the Fibrogenic Potential of Carbon Nanotubes.

Authors:  Zhaowei Chen; Qixin Wang; Mohammadnabi Asmani; Yan Li; Chang Liu; Changning Li; Julian M Lippmann; Yun Wu; Ruogang Zhao
Journal:  Sci Rep       Date:  2016-08-11       Impact factor: 4.379

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