药物发现中的数据标准:还有很长的路要走
Jens M Kelm1, Marc Ferrer2, Martin-Immanuel Bittner3
1Precomb Therapeutics AG, Hombrechtikon, Switzerland.
Drug discovery today
|January 12, 2024
概括
每年产生数百万个数据点来评估人类细胞中的化学和生物反应. 提高标准化对于最大限度地提高这些数据的价值至关重要,特别是复杂的生理模型.
科学领域:
- 毒理学和药理学的毒理学和药理学.
- 生物医学研究的研究.
- 在生命科学中的数据科学生命科学中的数据科学
背景情况:
- 每年从体外和体内研究中生成大量数据集,评估人类细胞中的化学和生物反应.
- 尽管产生了大量的数据,但化学和生物开发过程的效率并没有显著提高.
- 越来越依赖复杂的生理模型引入了时间和成本的限制.
研究的目的:
- 突出化学和生物反应评估中改善数据标准化的关键需求.
- 强调最大化从广泛的实验数据中获得的价值.
- 解决复杂,昂贵和耗时的生理模型所带来的挑战.
主要方法:
- 对化学和生物测试数据生成趋势的分析.
- 审查目前评估细胞反应的方法.
- 评估生理模型复杂度对研究效率的影响.
主要成果:
- 从不同的实验设置每年生成数百万到数万亿个数据点.
- 尽管数据量大,开发过程中缺乏显著的效率提升.
- 识别复杂的生理模型作为时间和成本的主要贡献者.
结论:
- 数据的标准化对于化学和生物反应数据的有效解释和利用至关重要.
- 最大化数据价值至关重要,特别是采用先进的,资源密集型模型.
- 解决标准化差距将提高生物医学研发的整体生产力.
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