Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours

Abstract

Understanding the uptake of a drug by diseased tissue, and the drug’s subsequent spatiotemporal distribution, are central factors in the development of effective targeted therapies. However, the interaction between the pathophysiology of diseased tissue and individual therapeutic agents can be complex, and can vary across tissue types and across subjects. Here, we show that the combination of mathematical modelling, high-resolution optical imaging of intact and optically cleared tumour tissue from animal models, and in vivo imaging of vascular perfusion predicts the heterogeneous uptake, by large tissue samples, of specific therapeutic agents, as well as their spatiotemporal distribution. In particular, by using murine models of colorectal cancer and glioma, we report and validate predictions of steady-state blood flow and intravascular and interstitial fluid pressure in tumours, of the spatially heterogeneous uptake of chelated gadolinium by tumours, and of the effect of a vascular disrupting agent on tumour vasculature.

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Fig. 1: The REANIMATE pipeline for in vivo and ex vivo imaging of intact tumours and performing three-dimensional computational fluid mechanics simulations.
Fig. 2: Three-dimensional blood vessel networks, segmented from optical imaging data acquired from complete colorectal carcinoma xenografts, and reconstructed in graphical format (diameters scaled according to their measured values).
Fig. 3: REANIMATE steady-state simulation results example LS174T and SW1222 colorectal adenocarcinoma xenografts.
Fig. 4: REANIMATE simulations of steady-state fluid dynamics (vascular and interstitial) in an orthotopic murine glioma model (GL261).
Fig. 5: REANIMATE simulation of Gd-DTPA delivery to an example LS174T tumour, compared with uptake measured in vivo with DCE-MRI.
Fig. 6: Dual-fluorophore, optical imaging of the response of colorectal carcinoma models (LS174T and SW1222) to treatment with a VDA (OXi4503).
Fig. 7: Results of REANIMATE simulations of blood flow, IFP and perfusion in an LS174T tumour.
Fig. 8: REANIMATE simulation predictions of OXi4503 delivery and treatment response.

Data availability

The authors declare that all data supporting the findings of this study are available within the paper and its Supplementary Information. Raw data generated from this study can be found at https://doi.org/10.17605/OSF.IO/ZH9EU.

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Acknowledgements

The authors acknowledge support received for the Kings College London & UCL CR-UK and EPSRC Comprehensive Cancer Imaging Centre, in association with the MRC and Department of Health (England) (C1519/A10331), Wellcome Trust (WT100247MA) and Rosetrees Trust/Stoneygate Trust (M135-F1 and M601). The authors thank OXiGENE for supplying OXi4503.

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Contributions

A.d’E. designed and performed optical imaging experiments, analysed and interpreted results and wrote the first drafts of the manuscript. P.W.S. and R.S. developed software to perform mathematical and computational analysis. P.W.S. analysed and interpreted data and wrote and edited the paper. M.A. performed a subset of the optical imaging experiments. M.S. and S.W.-S. developed software for performing time-dependent simulations. R.R. and T.A.R. designed and performed ASL-MRI measurements, and developed software for quantifying the data. G.A. provided murine brain tumour models. A.D. assisted with student supervision and with the design of optical imaging experiments. M.F.L. provided access to imaging resources and student supervision. R.B.P. provided murine xenograft models and interpreted results. S.W.-S. developed software for segmenting optical imaging data. R.S. and S.W.-S. co-led the project, secured funding, supervised the design of experiments and simulations, developed the main concepts, interpreted results and contributed to the writing and editing of the manuscript.

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Correspondence to Rebecca Shipley or Simon Walker-Samuel.

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Supplementary Information

Supplementary Figures 1–8, Supplementary Tables 1–3, Supplementary Results, Supplementary References 1–4 and Supplementary Video Captions 1–4.

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Supplementary Video 1

Three-dimensional rendering of REANIMATE simulation results, describing the passage of a bolus of the commonly used MRI contrast agent Gd‐DTPA through tumour vasculature.

Supplementary Video 2

REANIMATE simulation of the delivery of the MRI contrast agent Gd‐DTPA using the vascular network from the LS174T colorectal tumour-xenograft model.

Supplementary Video 3

Dual-fluorescence labelling of the vasculature in an LS174T colorectal xenograft model, following treatment with the vascular targeting agent OXi4503.

Supplementary Video 4

REANIMATE simulation of the delivery of the vascular targeting agent OXi4503 to an LS174T colorectal xenograft model.

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d’Esposito, A., Sweeney, P.W., Ali, M. et al. Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours. Nat Biomed Eng 2, 773–787 (2018). https://doi.org/10.1038/s41551-018-0306-y

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