集中式代谢软点分析方法的开发、优化与实施
Development, optimization and implementation of a centralized metabolic soft spot assay.
作者信息
Paiva Anthony A, Klakouski Cheryl, Li Shu, Johnson Benjamin M, Shu Yue-Zhong, Josephs Jonathan, Zvyaga Tatyana, Zamora Ismael, Shou Wilson Z
机构信息
Bristol-Myers Squibb R & D, SATT, 5 Research Parkway, Wallingford, CT 06492, USA.
Bristol-Myers Squibb R & D, PCO, 5 Research Parkway, Wallingford, CT 06492, USA.
出版信息
Bioanalysis. 2017 Apr;9(7):541-552. doi: 10.4155/bio-2016-0299. Epub 2017 Mar 24.
AIM
High clearance is a commonly encountered issue in drug discovery. Here we present a centralized metabolic soft spot identification assay with adequate capacity and turnaround time to support the metabolic optimization needs of an entire discovery organization.
METHODOLOGY
An integrated quan/qual approach utilizing both an orthogonal sample-pooling methodology and software-assisted structure elucidation was developed to enable the assay. Major metabolic soft spots in liver microsomes (rodent and human) were generated in a batch mode, along with kinetics of parent disappearance and metabolite formation, typically within 1 week of incubation.
RESULTS & CONCLUSION: A centralized metabolic soft spot identification assay has been developed and has successfully impacted discovery project teams in mitigating instability and establishing potential structure-metabolism relationships.
目的
高清除率是药物研发中常见的问题。在此，我们介绍一种集中式代谢软点识别分析方法，它具有足够的能力和周转时间，以支持整个研发机构的代谢优化需求。
方法
开发了一种综合定量/定性方法，该方法利用正交样本合并方法和软件辅助结构解析来实现该分析。肝微粒体（啮齿动物和人类）中的主要代谢软点以批处理模式生成，同时生成母体消失和代谢物形成的动力学数据，孵育通常在1周内完成。
结果与结论
已开发出一种集中式代谢软点识别分析方法，并已成功影响研发项目团队，以减轻不稳定性并建立潜在的结构-代谢关系。
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