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Leedale, J, Colley, HE, Gaskell, H, Williams, DP, Bearon, RN, Chadwick, AE, Murdoch, C and Webb, SD (2019) In silico-guided optimisation of oxygen gradients in hepatic spheroids. Computational Toxicology, 12. ISSN 2468-1113
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Spheroid_O2_Optimisation_JL_clean.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
Abstract
One of the key advantages of assessing the hepatotoxic potential of xenobiotics in spheroids rather than monolayer cell culture is the existence of a more physiologically relevant testing environment. Three-dimensional cultures support spatial gradients in nutrients such as oxygen that can be exploited to better represent in vivo gradients that exist along a fundamental sub-unit of liver microarchitecture, the liver sinusoid. The physical and physiological processes that result in the establishment of such gradients can be described mathematically. Quantification of the rates governing these processes and optimisation of cell culture conditions can be performed in silico to better inform experimental design. In this study, we take into account cell line-specific physiological properties, spheroid size and the impact of experimental equipment geometries in order to demonstrate how mathematical models can be optimised to achieve specific in vivo-like features in different scenarios. Furthermore, the sensitivity of such optimised gradients is analysed with respect to culture conditions and considerations are given to prevent the emergence of hypoxic regions in the spheroid. The methodology presented provides an enhanced understanding of the mechanisms of the system within this simulated in vitro framework such that experimental design can be more carefully calibrated when conducting experiments using hepatic spheroids. © 2019 Elsevier B.V.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics R Medicine > R Medicine (General) |
| Divisions: | Applied Mathematics (merged with Comp Sci 10 Aug 20) |
| Publisher: | Elsevier |
| Date Deposited: | 26 Sep 2019 10:24 |
| Last Modified: | 04 Sep 2021 09:04 |
| DOI or ID number: | 10.1016/j.comtox.2019.100093 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/11133 |
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