Summary
Cancer is the second most important cause of death in Europe, with an estimated 2.27 million new cases and 1.3 million deaths in 2020. The prerequisite for a successful medicinal therapy is that the drug reaches its target and that toxicity towards healthy tissue is limited, however the systemic injection of drugs leads to less than 1 % of the drugs accumulating in solid tumours. Relying on the permeability of the blood vessel in cancer tumours, the encapsulation of drugs in nanoparticles (NPs) constitutes a promising approach for cancer treatment. However, the homogeneous distribution of NPs in the tumour tissue remains a challenge. To reach all cells in the tumour the NPs must cross the extracellular matrix (ECM), a major component of solid tumours consisting mainly of a network of collagen fibres embedded in a hydrophilic gel of proteoglycans. Together, they limit the diffusion of drugs across the tumour. Focused UltraSound (FUS) exposure has been reported to improve the delivery of NPs to tumour cells. FUS induces acoustic radiation force and cavitation that can lead to microstreaming or shock waves but the underlying mechanism(s) for the improved transport are not well understood. Successful delivery depends on many factors including the pathology, structure and composition of the diseased tissue, the characteristics of the NPs, and the exposure parameters of the FUS, making it a complex problem difficult to solve using an experimental approach. In this project, we will use molecular modelling to identify correlations between the molecular details of NPs and ECM, and FUS exposure parameters with NP transport across the ECM, creating a predictive model for FUS delivery of NPs and drugs to diseased tissue. The model, validated by experimental data, will contribute to the design of personalized medicine for improved NP-based drug delivery.
Unfold all
/
Fold all
More information & hyperlinks
Web resources: | https://cordis.europa.eu/project/id/101062456 |
Start date: | 31-08-2023 |
End date: | 31-07-2025 |
Total budget - Public funding: | - 210 911,00 Euro |
Cordis data
Original description
Cancer is the second most important cause of death in Europe, with an estimated 2.27 million new cases and 1.3 million deaths in 2020. The prerequisite for a successful medicinal therapy is that the drug reaches its target and that toxicity towards healthy tissue is limited, however the systemic injection of drugs leads to less than 1 % of the drugs accumulating in solid tumours. Relying on the permeability of the blood vessel in cancer tumours, the encapsulation of drugs in nanoparticles (NPs) constitutes a promising approach for cancer treatment. However, the homogeneous distribution of NPs in the tumour tissue remains a challenge. To reach all cells in the tumour the NPs must cross the extracellular matrix (ECM), a major component of solid tumours consisting mainly of a network of collagen fibres embedded in a hydrophilic gel of proteoglycans. Together, they limit the diffusion of drugs across the tumour. Focused UltraSound (FUS) exposure has been reported to improve the delivery of NPs to tumour cells. FUS induces acoustic radiation force and cavitation that can lead to microstreaming or shock waves but the underlying mechanism(s) for the improved transport are not well understood. Successful delivery depends on many factors including the pathology, structure and composition of the diseased tissue, the characteristics of the NPs, and the exposure parameters of the FUS, making it a complex problem difficult to solve using an experimental approach. In this project, we will use molecular modelling to identify correlations between the molecular details of NPs and ECM, and FUS exposure parameters with NP transport across the ECM, creating a predictive model for FUS delivery of NPs and drugs to diseased tissue. The model, validated by experimental data, will contribute to the design of personalized medicine for improved NP-based drug delivery.Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
Images
No images available.
Geographical location(s)