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

Angela d'Esposito; Paul W Sweeney; Morium Ali; Magdy Saleh; Rajiv Ramasawmy; Thomas A Roberts; Giulia Agliardi; Adrien Desjardins; Mark F Lythgoe; R Barbara Pedley; Rebecca Shipley; Simon Walker-Samuel

doi:10.1038/s41551-018-0306-y

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

Angela d'Esposito, Paul W Sweeney, Morium Ali, Magdy Saleh, Rajiv Ramasawmy, Thomas A Roberts, Giulia Agliardi, Adrien Desjardins, Mark F Lythgoe, R Barbara Pedley, Rebecca Shipley, Simon Walker-Samuel

Biomedical Engineering Department

Univ London, Birkbeck University London, University College London, University of London Royal Veterinary College, University of London, St Georges University London, Imperial College London, Kings College London, Royal Holloway University London, Queen Mary University London, Div Populat Hlth Sci & Educ

Research output: Contribution to journal › Article › peer-review

82 Citations (Scopus)

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.

Original language	English
Pages (from-to)	773-787
Number of pages	15
Journal	Nature Biomedical Engineering
Volume	2
Issue number	10
DOIs	https://doi.org/10.1038/s41551-018-0306-y
Publication status	Published - Oct 2018

Keywords

Animals
Antineoplastic Agents/metabolism
Blood Vessels/drug effects
Cell Line, Tumor
Colorectal Neoplasms/diagnostic imaging
Contrast Media/chemistry
Diphosphates/metabolism
Disease Models, Animal
Female
Gadolinium/chemistry
Glioma/diagnostic imaging
Humans
Hydrodynamics
Image Processing, Computer-Assisted
Mice
Mice, Inbred C57BL
Mice, Nude
Models, Theoretical
Regional Blood Flow
Stilbenes/metabolism
Transplantation, Heterologous

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s41551-018-0306-y

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d'Esposito, A., Sweeney, P. W., Ali, M., Saleh, M., Ramasawmy, R., Roberts, T. A., Agliardi, G., Desjardins, A., Lythgoe, M. F., Pedley, R. B., Shipley, R., & Walker-Samuel, S. (2018). Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours. Nature Biomedical Engineering, 2(10), 773-787. https://doi.org/10.1038/s41551-018-0306-y

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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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author = "Angela d'Esposito and Sweeney, {Paul W} and Morium Ali and Magdy Saleh and Rajiv Ramasawmy and Roberts, {Thomas A} and Giulia Agliardi and Adrien Desjardins and Lythgoe, {Mark F} and Pedley, {R Barbara} and Rebecca Shipley and Simon Walker-Samuel",

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d'Esposito, A, Sweeney, PW, Ali, M, Saleh, M, Ramasawmy, R, Roberts, TA, Agliardi, G, Desjardins, A, Lythgoe, MF, Pedley, RB, Shipley, R & Walker-Samuel, S 2018, 'Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours', Nature Biomedical Engineering, vol. 2, no. 10, pp. 773-787. https://doi.org/10.1038/s41551-018-0306-y

TY - JOUR

T1 - Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours

AU - d'Esposito, Angela

AU - Sweeney, Paul W

AU - Ali, Morium

AU - Saleh, Magdy

AU - Ramasawmy, Rajiv

AU - Roberts, Thomas A

AU - Agliardi, Giulia

AU - Desjardins, Adrien

AU - Lythgoe, Mark F

AU - Pedley, R Barbara

AU - Shipley, Rebecca

AU - Walker-Samuel, Simon

PY - 2018/10

Y1 - 2018/10

N2 - 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.

AB - 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.

KW - Animals

KW - Antineoplastic Agents/metabolism

KW - Blood Vessels/drug effects

KW - Cell Line, Tumor

KW - Colorectal Neoplasms/diagnostic imaging

KW - Contrast Media/chemistry

KW - Diphosphates/metabolism

KW - Disease Models, Animal

KW - Female

KW - Gadolinium/chemistry

KW - Glioma/diagnostic imaging

KW - Humans

KW - Hydrodynamics

KW - Image Processing, Computer-Assisted

KW - Mice

KW - Mice, Inbred C57BL

KW - Mice, Nude

KW - Models, Theoretical

KW - Regional Blood Flow

KW - Stilbenes/metabolism

KW - Transplantation, Heterologous

U2 - 10.1038/s41551-018-0306-y

DO - 10.1038/s41551-018-0306-y

M3 - Article

C2 - 31015649

VL - 2

SP - 773

EP - 787

JO - Nature Biomedical Engineering

JF - Nature Biomedical Engineering

IS - 10

ER -

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

Abstract

Keywords

UN SDGs

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