[2026] Developing an Ultrasound Transducer for Image-Guided, Ultrasound-Triggered Drug Release from Liposomes for Drug Accumulation in Deep Tumor Models

One of the most critical challenges in cancer therapy is the inefficient delivery of chemotherapeutic agents to tumor sites. Systemically administered drugs typically show poor tumor accumulation; in some cases, less than 1% of the injected dose reaches the target tissue. This low targeting efficiency leads to severe side effects due to damage to healthy tissues.

To address this limitation, liposomes have been developed as nanocarriers capable of encapsulating chemotherapeutic drugs and passively accumulating in tumors [1]. However, passive targeting alone rarely ensures sufficient or controlled drug release at the tumor site. External stimuli, such as ultrasound, provide a safe and non-invasive strategy to actively trigger drug release from liposomes directly at the tumor, thereby reducing systemic exposure and improving therapeutic efficacy [2,3].

Through Ignasi’s PhD project, our laboratory has extensively investigated:

1. Liposome formulation optimization, using mechanistic insights combined with a Bayesian optimization AI model.

1. The influence of ultrasound parameters on ultrasound-triggered drug release.

These studies led to the development of an optimized liposome formulation that outperforms existing commercial and patented formulations. Additionally, based on the understanding of relevant acoustic parameters, a dual-element ultrasound device was recently developed (Wietse’s MEP) to induce efficient ultrasound-triggered drug release in vivo. This device enables drug release from liposomes in mice with subcutaneous tumors, where the tumor can be visually identified, and the ultrasound transducer can be manually aligned with the tumor location.

However, tumors in humans are frequently located deep within tissue, where visual alignment of the ultrasound focus is not feasible. In these cases, real-time imaging is required to accurately identify the tumor location and guide the therapeutic ultrasound beam. Integrating ultrasound imaging with the therapeutic transducer will therefore allow the operator to visualize the tumor, position the acoustic focus at the target region, and subsequently trigger drug release from liposomes.

However, tumors in humans are frequently located deep within tissue, where direct visual alignment of the ultrasound focus with the tumor location is not feasible. In such cases, the operator cannot rely on external anatomical landmarks to accurately position the therapeutic ultrasound beam. As a result, real-time imaging is required to identify the tumor location and guide the therapeutic ultrasound exposure. Ultrasound imaging provides a non-invasive and clinically established method for visualizing soft tissues in real time. By integrating an imaging modality into the therapeutic setup, it becomes possible to continuously monitor the tissue region of interest and precisely determine the spatial location of the tumor. This information can then be used to guide the delivery of acoustic energy to the correct target region.

Thus, this exciting master’s project will combine two distinct ultrasound transducers with complementary functions. A high-frequency imaging transducer will be used to generate ultrasound images of the tissue, enabling the visualization and localization of tumors within deeper anatomical structures. High-frequency ultrasound provides improved spatial resolution, which is essential for accurately identifying tumor boundaries and surrounding tissue structures. In parallel, a low-frequency dual-element therapeutic transducer will be used to induce ultrasound-triggered drug release from liposomes. Lower ultrasound frequencies are more suitable for generating the mechanical and acoustic effects required to destabilize liposomal membranes and trigger drug release. This dual-element transducer, previously developed in our laboratory, has already demonstrated the capability to induce efficient ultrasound-triggered drug release in vivo.

The key concept of this project is therefore to develop a system coupling these two transducers into a single integrated system. The imaging transducer would first be used to locate the tumor and visualize the surrounding tissue environment. Based on this image, the operator would be able to determine the correct spatial position of the therapeutic focal volume generated by the dual-element transducer. As such, in this project, a custom interface will be developed to display the position of this focal volume relative to the ultrasound image, enabling accurate alignment between imaging and therapy.

In summary, the ultimate goal of this project is to develop a fully image-guided ultrasound-triggered drug delivery platform that combines diagnostic imaging with localized therapeutic activation. Such a system could enable more precise targeting of tumors located deep within tissue and would serve as a key technological step toward future preclinical studies in mice and larger animal models. Ultimately, this approach aims to facilitate the clinical translation of ultrasound-responsive liposomal drug delivery systems, improving the safety and effectiveness of chemotherapeutic treatments.

Bibliography

[1]             M.J. Mitchell, M.M. Billingsley, R.M. Haley, M.E. Wechsler, N.A. Peppas, R. Langer, Engineering precision nanoparticles for drug delivery, Nat Rev Drug Discov 20 (2021) 101–124. https://doi.org/10.1038/s41573-020-0090-8.

[2]             Y. Wang, D.S. Kohane, External triggering and triggered targeting strategies for drug delivery, Nat Rev Mater 2 (2017) 1–14. https://doi.org/10.1038/natrevmats.2017.20.

[3]             D. Rosenblum, N. Joshi, W. Tao, J.M. Karp, D. Peer, Progress and challenges towards targeted delivery of cancer therapeutics, Nat Commun 9 (2018) 1410. https://doi.org/10.1038/s41467-018-03705-y.

[4]             L. Cao, D. Russo, E. Matthews, A. Lapkin, D. Woods, Computer-aided design of formulated products: A bridge design of experiments for ingredient selection, Computers & Chemical Engineering 169 (2023) 108083. https://doi.org/10.1016/j.compchemeng.2022.108083.

[5]             G.A. Husseini, R. Sabouni, V. Puzyrev, M. Ghommem, Deep Learning for the Accurate Prediction of Triggered Drug Delivery, IEEE Transactions on NanoBioscience (2024) 1–1. https://doi.org/10.1109/TNB.2024.3426291.

[6]             S. Bae, H. Choi, J. Lee, M. Kang, S. Ahn, Y. Lee, H. Choi, S. Jo, Y. Lee, H. Park, S. Lee, S. Yoon, G. Roh, S. Cho, Y. Cho, D. Ha, S. Lee, E. Choi, A. Oh, J. Kim, S. Lee, J. Hong, N. Lee, M. Lee, J. Park, D. Jeong, K. Lee, J. Nam, Rational Design of Lipid Nanoparticles for Enhanced mRNA Vaccine Delivery via Machine Learning, Small (2024) 2405618. https://doi.org/10.1002/smll.202405618.

[7]             B. Shahriari, K. Swersky, Z. Wang, R.P. Adams, N. de Freitas, Taking the Human Out of the Loop: A Review of Bayesian Optimization, Proceedings of the IEEE 104 (2016) 148–175. https://doi.org/10.1109/JPROC.2015.2494218.

Assignment

1st part: Literature review on image-guided ultrasound therapeutic systems
2nd part: Design, simulation, fabrication and experimental validation of dual-transducer system for ultrasound imaging and focal volume generation

Requirements

MSc students from Biomedical Engineering, Tecnical Medicine, or other relevant backgrounds. Interested students should include their CV, the list of courses attended, and a motivation letter, to Tiago Costa (t.m.l.dacosta@tudelft.nl) and Ignasi Simon (I.SimonGrau@tudelft.nl)

Contact

Last modified: 2026-04-02