定量系统药理学和机器学习:天堂与地狱之间的匹配?
Marcus John Tindall1, Lourdes Cucurull-Sanchez2, Hitesh Mistry2
1Department of Mathematics and Statistics and Institute of Cardiovascular and Metabolic Research, University of Reading, Whiteknights, Reading, United Kingdom (M.J.T.); GSK Medicines Research Centre, Stevenage, United Kingdom (L.C.-S., J.W.T.Y.); and Pharmacy, Division of Pharmacy and Optometry, University of Manchester, Oxford Road, Manchester, United Kingdom (H.M.) m.tindall@reading.ac.uk.
整合机器学习 (ML) 和机械定量系统药理学 (QSP) 模型可以促进药品的发展. 这种协同作用优化了药物发现和开发管道的决策,通过利用数据和先前知识.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 制药开发产生的数据跨多个规模,从亚细胞到患者队列.
- 有效的决策需要整合不同的数据类型和知识.
- 机器学习 (ML) 和机械建模是关键的计算方法.
研究的目的:
- 概述ML和定量系统药理 (QSP) 模型在药物开发中的应用.
- 在不同的管道阶段指导ML和QSP的最佳整合.
- 要突出数据驱动的ML和知识驱动的QSP之间的相互作用.
主要方法:
- 审查ML和QSP模型的应用,无论是单独的还是双重的.
- 讨论敏感性和可识别性分析在QSP模型开发中的作用.
- 检查ML如何为机械模型开发提供信息,反之亦然.
主要成果:
- ML和QSP模型可以在整个药物发现和开发管道中协同应用.
- 在模型选择中,区分现有数据和先前知识至关重要.
- 质量标准模型的敏感性和可识别性分析可以指导ML的实验设计.
结论:
- 结合ML和QSP模型的应用,提高了制药开发中的决策能力.
- 在ML和QSP之间做出选择取决于数据和先前知识的可用性.
- 未来的研究应该考虑这些方法之间的动态相互作用,在持续的数据采集中.
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