PO.CH01.02 · 化学

利用患者来源类器官实现表型高通量药物筛选

Enabling phenotypic high-throughput drug screening with patient-derived organoids

编号 6421 展板 21 时间 4/21 02:00–05:00 区域 Section 39 主讲 Abraham Lin, PhD

分会场 Screening and Technology Advances for Probe and Drug Discovery

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作者与单位 Authors & Affiliations

Abraham Lin¹, Maxim Le Compte¹, Rebecca Stone², Tyler Gilcrest², Edgar Cardenas De La Hoz³, Geert Roeyen⁴, Jeroen M. H. Hendriks⁵, Filip Lardon¹, Christophe Deben¹

¹Center for Oncological Research, University of Antwerp, Wilrijk, Belgium,²Orbits Oncology, Palo Alto, CA,³Industrial Vision Lab, University of Antwerp, Antwerp, Belgium,⁴Department of Hepatobiliary Transplantation & Endocrine Surgery, University Hospital Antwerp, Wilrijk, Belgium,⁵Thoracic and Vascular Surgery, University Hospital Antwerp, Wilrijk, Belgium

摘要 Abstract

中文摘要

患者来源类器官(PDO)能够在实验室中重现患者肿瘤，使其成为相较于传统体外细胞培养更真实的模型。然而，可重复性、标准化和分析方面的挑战限制了PDO在药物发现中的应用，尤其是在高通量筛选(HTS)应用中。为解决这一问题，我们开发了一个高效的工作流程，使用自动化设备和可互操作的计算平台来促进PDO在HTS应用中的采用¹。我们的方案利用活细胞成像技术捕捉动态、复杂的PDO-药物相互作用，以进行更全面的分析。通过从明场图像对类器官进行无标记检测，我们将基于生长速率的药物反应指标²与药物协同指标相结合，显著提高了对协同药物相互作用的识别³。在此，我们在10种PDO上筛选了10种不同的药物组合，这些PDO来源于健康肺、非小细胞肺癌和胰腺导管腺癌。我们特别聚焦于硫氧还蛋白还原酶抑制剂Auranofin(金诺芬)的再利用。该筛选总共需要20块384孔板，并产生了37,000张图像采集。借助实验室自动化，类器官接种仅耗时1小时；借助分析自动化，全部37,000张图像在8小时内完成分析。总之，我们鉴定出能够以肿瘤选择性方式协同增强Auranofin疗效的候选药物。我们的研究凸显了将实验室自动化与计算自动化相结合以实现PDO的HTS的优势。我们方法的实施支持了FDA当前推动减少动物实验以发现有效治疗策略的努力。我们目前正在进一步研究不同实验室自动化系统之间的简化整合技术。 ¹ Le Compte, M., 等 JoVE 190 (2022) ² Deben, C., 等 Communications Biology 1612 (2024) ³ Deben, C., 等 Journal of Experimental & Clinical Cancer Research 88(2024)

查看英文原文 English abstract

Patient-derived organoids (PDOs) can recapitulate patient tumors in the lab, making them more realistic models compared to traditional in vitro cell cultures. However, challenges in reproducibility, standardization, and analysis have limited the use of PDOs in drug discovery, especially for high-throughput screening (HTS) applications. To address this, we have developed an efficient workflow, using automation equipment and interoperable computational platforms to facilitate the adoption of PDOs for HTS applications 1 . Our protocol leverages live-cell imaging techniques to capture the dynamic, complex PDO-drug interactions for more comprehensive analysis. Using label-free detection of organoids from brightfield images, we integrated growth-rate-based drug response metrics 2 with drug synergy metrics to significantly improve the identification of synergistic drug interactions 3 . Here, we screened 10 distinct drug combinations on 10 PDOs, sourced from healthy lung, non-small cell lung cancer, and pancreatic ductal adenocarcinoma. In particular, we focused on repurposing the Thioredoxin reductase inhibitor, Auranofin. This screen required a total of 20 384-well plates and resulted in 37,000 images captures. With lab automation, organoid seeding only took 1 hour, and with analysis automation, all 37,000 images were analyzed in under 8 hours. Altogether, we identified drug candidates that can synergistically enhanced the efficacy of Auranofin in a tumor selective manner. Our study highlights the advantage of combining lab automation with computational automation to enable HTS with PDOs. The implementation of our method supports the current push by the FDA to reduce animal experimentation for the discovery of effective therapeutic strategies. We are now further investigating streamlined integration techniques between different lab automation systems. 1 Le Compte, M., et al JoVE 190 (2022) 2 Deben, C., et al Communications Biology 1612 (2024) 3 Deben, C., et al Journal of Experimental & Clinical Cancer Research 88(2024)

利益披露 Disclosure

A. Lin, None.. M. Le Compte, None.. R. Stone, None.. T. Gilcrest, None.. E. Cardenas De La Hoz, None.. G. Roeyen, None.. J. M. H. Hendriks, None.. F. Lardon, None.. C. Deben, None.

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