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  Cornell University

MAE Publications and Papers

Sibley School of Mechanical and Aerospace Engineering

New article: How can 50 years of Solute Transport Data in Articular Cartilage Inform the Design of Arthritis Therapeutics?

Article:  DiDomenico, CD; Bonassar, LJ; “How can 50 years of Solute Transport Data in Articular Cartilage Inform the Design of Arthritis Therapeutics?”, Osteoarthritis and Cartilage, 26 (11): 31438-1446


Abstract:  Objective: For the last half century, transport of nutrients and therapeutics in articular cartilage has been studied with various in vitro systems that attempt to model in vivo conditions. However, experimental technique, tissue species, and tissue storage condition (fresh/frozen) vary widely and there is debate on the most appropriate model system. Additionally, there is still no clear overarching framework with which to predict solute transport properties based on molecular characteristics. This review aims to develop such a framework, and to assess whether experimental procedure affects trends in transport data.

Methods: Solute data from 31 published papers that investigated transport in healthy articular cartilage were obtained and analyzed for trends.

Results: Here, we show that diffusivity of spherical and globular solutes in cartilage can be predicted by molecular weight (MW) and hydrodynamic radius via a power-law relationship. This relationship is robust for many solutes, spanning 5 orders of magnitude in MW and was not affected by variations in cartilage species, age, condition (fresh/frozen), and experimental technique. Traditional models of transport in porous media exhibited mixed effectiveness at predicting diffusivity in cartilage, but were good in predicting solute partition coefficient.

Conclusion: Ultimately, these robust relationships can be used to accurately predict and improve transport of solutes in adult human cartilage and enable the development of better optimized arthritis therapeutics. (c) 2018 Osteoarthritis Research Society International.

Published by Elsevier Ltd. All rights reserved.

Funding Acknowledgement:  National Science Foundation [NSF-1536463]; AbbVie Inc.

Funding Text:  This work was supported by the National Science Foundation grant (NSF-1536463) and in part by AbbVie Inc. These sources had no involvement or influence in this work.

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