在工业制药开发环境中深度集成低成本液体处理机器人。

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS SLAS Technology Pub Date : 2024-08-31 DOI:10.1016/j.slast.2024.100180
Anton Thieme , Sean Renwick , Michaela Marschmann , Pedro Ivo Guimaraes , Susanne Weissenborn , Jamie Clifton
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引用次数: 0

摘要

制药行业正越来越多地采用实验室自动化来提高实验效率和操作弹性,特别是通过集成自动液体处理机(ALH)。本文探讨了如何将低成本的 Opentrons OT-2 液体处理机器人与 F. Hoffmann-La Roche AG 公司的内部工作流协调软件 AutoLab 集成,以克服实验室自动化的障碍。通过利用 OT-2 面向开发的界面和 AutoLab 的模块化架构,我们实现了一个用户友好、经济高效且灵活的自动化解决方案,该解决方案符合 FAIR(可查找、可访问、可互操作、可重用)数据原则。我们展示了一种先进的工作流程开发方法,利用该软件架构,可以方便地创建两个灵活的移液协议和中等复杂程度的检测。这种深度集成方法降低了新手用户的学习曲线,同时提高了实验工作流程的整体效率和可靠性。我们的研究结果表明,这种集成可以大大减轻实验室自动化所面临的挑战,包括成本、复杂性和适应性,从而为制药研究领域更方便、更强大的自动化系统铺平道路。
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Deep integration of low-cost liquid handling robots in an industrial pharmaceutical development environment

The pharmaceutical industry is increasingly embracing laboratory automation to enhance experimental efficiency and operational resilience, particularly through the integration of automated liquid handlers (ALHs). This paper explores the integration of the low-cost Opentrons OT-2 liquid handling robot with F. Hoffmann-La Roche AG's in-house workflow orchestration software, AutoLab, to overcome barriers to lab automation. By leveraging the OT-2′s development-oriented interfaces and AutoLab's modular architecture, we achieved a user-friendly, cost-efficient, and flexible automation solution that aligns with FAIR (findable, accessible, interoperable, reusable) data principles. We demonstrate an advanced workflow development methodology, utilizing the software architecture, that facilitates the creation of two flexible pipetting protocols and medium complexity assays. This deep integration approach diminishes the learning curve for novice users while simultaneously enhancing the overall efficiency and reliability of the experimental workflow. Our findings suggest that such integrations can significantly mitigate the challenges associated with lab automation, including cost, complexity, and adaptability, paving the way for more accessible and robust automated systems in pharmaceutical research.

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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
期刊最新文献
Model-Based Interactive Visualization for Complex Systems Requirements and Design in Joint Tests. Implementing enclosed sterile integrated robotic platforms to improve cell-based screening for drug discovery. Life Sciences Discovery and Technology Highlights. Notes on AEMS methods development for high throughput experimentation in drug discovery. Prosthesis repair of oral implants based on artificial intelligenc`e finite element analysis.
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