机器人筛选核心的考验和磨难。

J Babiak, B Lucotch, A Russo, L Heydt, S Williams, R McCaully
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引用次数: 1

摘要

在制药行业内,高通量筛选是一种有价值的快速工具,可以识别可能导致未来药物的新化合物。高通量筛选涉及针对特定的分子或细胞“目标”(例如酶)尽可能快地测试尽可能多的化合物,以期相互作用的化合物可能提供显着的治疗益处。在惠氏研究公司,已经建立了一个机器人和自动化研究核心小组,作为高通量筛选的内部资源。机器人小组有三个任务:(1)为公司所有治疗部门的客户开发和执行高通量筛选;(2)对客户进行屏幕设计相关问题的教育;(3)帮助客户将自动化工作站带入他们的实验室。因此,这项任务需要有效地使用自动化,以及与客户建立强有力的合作关系。面临的挑战分为两类:技术限制和客户关系。由于必须在极短的时间框架内开发和实施具有非常不同格式和生化终点的分析,因此出现了技术挑战。应对这些挑战的主要手段是灵活的机器人和灵活的人。客户关系领域的挑战包括设定切合实际的期望,保持合作意识(而不仅仅是服务),教育调查人员如何处理产生的大量数据并寻求反馈。有效和频繁的沟通,以及对每个人观点的认识,对于提供最合适的服务至关重要。
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The trials and tribulations of a robotic screening core.

It is well recognized within the pharmaceutical industry that high throughput screening is a valuable and rapid tool to identify novel chemical compounds that may lead to tomorrow's drugs. High throughput screening involves testing as many chemical compounds as quickly as possible against a defined molecular or cellular 'target' (for example an enzyme) in the hope that interacting compounds may provide significant therapeutic benefits.At Wyeth-Ayerst Research, a Robotics and Automation Research Core Group has been established which serves as the in-house resource for high throughput screening. The robotics group has three missions: (1) develop and perform high throughput screens for customers in all therapeutic departments in the company; (2) educate customers in issues related to screen design; and (3) help customers to bring automated workstations into their laboratories. The mission, therefore, requires the effective use of automation, as well as building a strong collaboration with customers.THE CHALLENGES THAT HAVE BEEN FACED FALL INTO TWO CATEGORIES: technology limiting and customer relations. Technological challenges arise because it is necessary to develop and implement assays with very different formats and biochemical endpoints within extremely shortened time frames. The primary means to meet these challenges is with flexible robotics and flexible people. Challenges in the area of customer relations include setting realistic expectations, maintaining a sense of collaboration (and not merely service), educating investigators as to how to deal with the huge amount of data generated and seeking feedback. Effective and frequent communication, and an awareness of each individual's perspective, are essential to provide the most appropriate service.

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