Dataset Information

MR-labelled liposomes and focused ultrasound for spatiotemporally controlled drug release in triple negative breast cancers in mice.

ABSTRACT: Rationale: Image-guided, triggerable, drug delivery systems allow for precisely placed and highly localised anti-cancer treatment. They contain labels for spatial mapping and tissue uptake tracking, providing key location and timing information for the application of an external stimulus to trigger drug release. High Intensity Focused Ultrasound (HIFU or FUS) is a non-invasive approach for treating small tissue volumes and is particularly effective at inducing drug release from thermosensitive nanocarriers. Here, we present a novel MR-imageable thermosensitive liposome (iTSL) for drug delivery to triple-negative breast cancers (TNBC). Methods: A macrocyclic gadolinium-based Magnetic Resonance Imaging (MRI) contrast agent was covalently linked to a lipid. This was incorporated at 30 mol% into the lipid bilayer of a thermosensitive liposome that was also encapsulating doxorubicin. The resulting iTSL-DOX formulation was assessed for physical and chemical properties, storage stability, leakage of gadolinium or doxorubicin, and thermal- or FUS-induced drug release. Its effect on MRI relaxation time was tested in phantoms. Mice with tumours were used for studies to assess both tumour distribution and contrast enhancement over time. A lipid-conjugated near-infrared fluorescence (NIRF) probe was also included in the liposome to facilitate the real time monitoring of iTSL distribution and drug release in tumours by NIRF bioimaging. TNBC (MDA-MB-231) tumour-bearing mice were then used to demonstrate the efficacy at retarding tumour growth and increasing survival. Results: iTSL-DOX provided rapid FUS-induced drug release that was dependent on the acoustic power applied. It was otherwise found to be stable, with minimum leakage of drug and gadolinium into buffers or under challenging conditions. In contrast to the usually suggested longer FUS treatment we identified that brief (~3 min) FUS significantly enhanced iTSL-DOX uptake to a targeted tumour and triggered near-total release of encapsulated doxorubicin, causing significant growth inhibition in the TNBC mouse model. A distinct reduction in the tumours' average T₁ relaxation times was attributed to the iTSL accumulation. Conclusions: We demonstrate that tracking iTSL in tumours using MRI assists the application of FUS for precise drug release and therapy.

SUBMITTER: Amrahli M

PROVIDER: S-EPMC7806456 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MR-labelled liposomes and focused ultrasound for spatiotemporally controlled drug release in triple negative breast cancers in mice.

Amrahli Maral M Centelles Miguel M Cressey Paul P Prusevicius Martynas M Gedroyc Wladyslaw W Xu Xiao Yun XY So Po-Wah PW Wright Michael M Thanou Maya M

Nanotheranostics 20210101 2

<b>Rationale:</b> Image-guided, triggerable, drug delivery systems allow for precisely placed and highly localised anti-cancer treatment. They contain labels for spatial mapping and tissue uptake tracking, providing key location and timing information for the application of an external stimulus to trigger drug release. High Intensity Focused Ultrasound (HIFU or FUS) is a non-invasive approach for treating small tissue volumes and is particularly effective at inducing drug release from thermosens ...[more]

PMID: 33457192

Similar Datasets

Project description:Background and purposeTranscranial MR imaging-guided focused ultrasound is a promising novel technique to treat multiple disorders and diseases. Planning for transcranial MR imaging-guided focused ultrasound requires both a CT scan for skull density estimation and treatment-planning simulation and an MR imaging for target identification. It is desirable to simplify the clinical workflow of transcranial MR imaging-guided focused ultrasound treatment planning. The purpose of this study was to examine the feasibility of deep learning techniques to convert MR imaging ultrashort TE images directly to synthetic CT of the skull images for use in transcranial MR imaging-guided focused ultrasound treatment planning.Materials and methodsThe U-Net neural network was trained and tested on data obtained from 41 subjects (mean age, 66.4 ± 11.0 years; 15 women). The derived neural network model was evaluated using a k-fold cross-validation method. Derived acoustic properties were verified by comparing the whole skull-density ratio from deep learning synthesized CT of the skull with the reference CT of the skull. In addition, acoustic and temperature simulations were performed using the deep learning CT to predict the target temperature rise during transcranial MR imaging-guided focused ultrasound.ResultsThe derived deep learning model generates synthetic CT of the skull images that are highly comparable with the true CT of the skull images. Their intensities in Hounsfield units have a spatial correlation coefficient of 0.80 ± 0.08, a mean absolute error of 104.57 ± 21.33 HU, and a subject-wise correlation coefficient of 0.91. Furthermore, deep learning CT of the skull is reliable in the skull-density ratio estimation (r = 0.96). A simulation study showed that both the peak target temperatures and temperature distribution from deep learning CT are comparable with those of the reference CT.ConclusionsThe deep learning method can be used to simplify workflow associated with transcranial MR imaging-guided focused ultrasound.

Dataset Information

MR-labelled liposomes and focused ultrasound for spatiotemporally controlled drug release in triple negative breast cancers in mice.

Publications

MR-labelled liposomes and focused ultrasound for spatiotemporally controlled drug release in triple negative breast cancers in mice.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets