Light- and microfluidic-guided release of drugs: Microfluidics

Master Thesis (2024)
Author(s)

G.T. van Veen (TU Delft - Mechanical Engineering)

Contributor(s)

U. Staufer – Mentor (TU Delft - Micro and Nano Engineering)

P.F.J. van Altena – Mentor (TU Delft - Micro and Nano Engineering)

Alina Y. Rwei – Graduation committee member (TU Delft - ChemE/Product and Process Engineering)

P. Fanzio – Graduation committee member (TU Delft - Micro and Nano Engineering)

Faculty
Mechanical Engineering
More Info
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Publication Year
2024
Language
English
Graduation Date
15-07-2024
Awarding Institution
Delft University of Technology
Programme
['Mechanical Engineering | Micro and Nano Engineering', 'Mechanical Engineering | Energy, Flow and Process Technology']
Faculty
Mechanical Engineering
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Abstract

Current stroke treatments are limited to acute phase management, and there are few clinically available drugs for neuron protection or damage repair due to restrictions imposed by the blood-brain barrier. This project envisions an implantable DDS for precise drug dispensing to areas of the brain affected by stroke, using light-activated liposomes and microfluidic control to improve therapeutic effectiveness and minimize off-target side effects.

Reviewing literature on microfluidic trap-and-release mechanisms, it became evident that deformable particles often tend to slip out of traps. To accurately predict this behavior, the Young's-Laplace equation and energy stored in the liposome membrane have been assessed. Next, microfluidic devices that were able to manipulate the liposomes to the desired trapping locations were designed and produced. The trapping behavior of liposomes has been assessed by increasing the hydrostatic pressure under a fluorescent microscope. The results show that the experimental pressure of 50-250 Pa is lower than the expected theoretical predictions and that there exists a diameter threshold of 17-18 μm for which the liposomes show lysis behavior. Finally, the entire DDS concept has been demonstrated using a network of traps using the path-of-least-resistance approach.

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