Suppr超能文献

一种用于物理化学/药物代谢动力学/毒理学分析的分级筛选方法。

A hierarchical screening methodology for physicochemical/ADME/Tox profiling.

作者信息

DeWitte Robert S, Robins Russell H

机构信息

New Pharma R&D Solutions, Thermo Electron Corporation, 5344 John Lucas Drive, Burlington, Ontario L7L 6A6, Canada.

出版信息

Expert Opin Drug Metab Toxicol. 2006 Oct;2(5):805-17. doi: 10.1517/17425255.2.5.805.

Abstract

Full integration of pharmaceutical profiling into pharmaceutical lead selection and optimisation requires that complete sets of unequivocal data be available at the time compound design or advancement decisions are made. As the productivity of chemical synthesis expands, and the breadth of profiling assays grow in scope, physicochemical/ADME/Tox laboratories are being challenged to produce ever more data to support an accelerating decision cycle. This article focuses on the challenges of increasing preclinical profiling productivity while managing lower accuracy higher throughput data streams to preserve confidence in decision making. The authors propose a hierarchical screening strategy and describe the implementation of an automated system designed to support that strategy efficiently.

摘要

将药物特征分析全面整合到药物先导物的选择和优化过程中,需要在做出化合物设计或推进决策时能够获得完整且明确的数据集。随着化学合成效率的提高以及特征分析检测范围的扩大,物理化学/药物代谢动力学/毒理学实验室面临着挑战,需要生成更多数据以支持加速的决策周期。本文重点探讨了在管理准确性较低但通量较高的数据流以维持决策信心的同时,提高临床前特征分析效率所面临的挑战。作者提出了一种分层筛选策略,并描述了一个旨在有效支持该策略的自动化系统的实施情况。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验