A Novel Microfluidic Platform for Personalized Anticancer Drug Screening Through Image Analysis
Year: 2024
Type: article
Abstract: The advancement of personalized treatments in oncology has garnered increasing attention, particularly for rare and aggressive cancer with low survival rates like the bone tumors osteosarcoma and chondrosarcoma. This study introduces a novel PDMS-agarose microfluidic device tailored for generating patient-derived tumor spheroids and serving as a reliable tool for personalized drug screening. Using this platform in tandem with a custom imaging index, we evaluated the impact of the anticancer agent doxorubicin on spheroids from both tumor types. The device produces 20 spheroids, each around 300 µm in diameter, within a 24 h timeframe, facilitating assessments of characteristics and reproducibility. Following spheroid generation, we measured patient-derived spheroid diameters in bright-field images, calcein AM-positive areas/volume, and the binary fraction area, a metric analyzing fluorescence intensity. By employing a specially developed equation that combines viability signal extension and intensity, we observed a substantial decrease in spheroid viability of around 75% for both sarcomas at the highest dosage (10 µM). Osteosarcoma spheroids exhibited greater sensitivity to doxorubicin than chondrosarcoma spheroids within 48 h. This approach provides a reliable in vitro model for aggressive sarcomas, representing a personalized approach for drug screening that could lead to more effective cancer treatments tailored to individual patients, despite some implementation challenges.
Source: Micromachines
DOI: 10.3390/mi15121521
Authors: Maria Veronica Lipreri, Marilina Tamara Totaro, Julia Alicia Boos, Maria Sofia Basile, Nicola Baldini, Sofia Avnet
Institutions: Istituto Ortopedico Rizzoli, University of Bologna, ETH Zurich
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
Award: G74271788
OpenAlex ID: W4405722807
