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3차원 세포배양 플랫폼 항암제 감수성 및 효능/독성 검사 전문 기업

3차원 세포배양 플랫폼 항암제 감수성 및 효능/독성 검사
전문 기업

논문 2023, Biomaterials , High-throughput organo-on-pillar (high-TOP) array…

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작성자 MBD
댓글 0건 조회 1,122회 작성일 23-03-16 09:42


The development of organoid culture technologies has triggered industrial interest in ex vivo drug test-guided
clinical response prediction for precision cancer therapy. The three-dimensional culture encapsulated with
basement membrane (BM) components is extremely important in establishing ex vivo organoids and drug
sensitivity tests because the BM components confer essential structures resembling tumor histopathology.
Although numerous studies have demonstrated three-dimensional culture-based drug screening methods,
establishing a large-scale drug-screening platform with matrix-encapsulated tumor cells is challenging because
the arrangement of microspots of a matrix–cell droplet onto each well of a microwell plate is inconsistent and
difficult to standardize. In addition, relatively low scales and lack of reproducibility discourage the application of
three-dimensional organoid-based drug screening data for precision treatment or drug discovery. To overcome
these limitations, we manufactured an automated organospotter-integrated high-throughput organo-on-pillar
(high-TOP) drug-screening platform. Our system is compatible with various extracellular matrices, including
BM extract, Matrigel, collagen, and hydrogel. In addition, it can be readily utilized for high-content analyses by
simply exchanging the bottom plates without disrupting the domes. Our system demonstrated considerable
robustness, consistency, reproducibility, and biological relevancy in three-dimensional drug sensitivity analyses
using Matrigel-encapsulated ovarian cancer cell lines. We also demonstrated proof-of-concept cases representing
the clinical feasibility of high-TOP-assisted ex vivo drug tests linked to clinical chemo-response in ovarian cancer
patients. In conclusion, our platform provides an automated and standardized method for ex vivo drugsensitivity-guided
clinical response prediction, suggesting effective chemotherapy regimens for patients with cancer.