AURA:加速药物发现,以准确性,实用性和排名顺序评估以实现数据驱动的决策
Edward Price1, Virginia Saulnier1, John Cory Kalvass1
1Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, United States.
一种新的统计方法,准确性,实用性和排名顺序评估 (AURA),通过分析项目特定数据来改善肠道吸收的预测. 这种方法增强了生物制药公司的早期药物优化.
科学领域:
- 药理动力学和药物新陈代谢
- 计算化学计算化学
- 生物制药研究 生物制药研究
背景情况:
- 生物制药公司通过in silico,in vitro和in vivo研究生成大量数据集.
- 预测肠道吸收对于药物开发的早期阶段至关重要,但仍然很复杂.
- 现有的方法往往缺乏项目特定的定制,导致次优化优化.
研究的目的:
- 引入一个标准化的统计方法,准确性,实用性和排名顺序评估 (AURA),用于评估预测肠道吸收的终点有效性.
- 展示项目特定数据分析对药物优化全球模型的价值.
- 为生物制药组织提供一个框架,以加强早期药物开发工作流程.
主要方法:
- 准确性,实用性和排名顺序评估 (AURA) 的实施,这是一个统计方法,结合了基本分析和动态数据可视化.
- 评估各种物理化学性质及其对肠道吸收的影响.
- 与全球模型对比项目特定的相关性.
主要成果:
- 物理化学性质在项目特定的基础上独特地影响肠道吸收.
- 无论是"规则5" (Ro5) 还是"超越规则5" (bRo5) 项目,都面临着不同的吸收挑战,需要量身定制的战略.
- 与全球模型相比,项目特定的相关性显示出更高的准确性,突出显示了吸收预测的复杂性.
结论:
- AURA为生物制药公司提供了一种标准化,可适应的方法,以改善早期药物优化.
- 在AURA中,自动化分析,多样化的数据集成和灵活的可视化可以促进数据驱动的决策.
- 该AURA方法提高了研究效率和跨职能团队在药物开发中的合作.
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