药物开发中的人口药理动力学分析的历史和未来
1Teuscher Solutions LLC, Pleasant Grove, UT, USA.
Xenobiotica; the fate of foreign compounds in biological systems
|December 5, 2023
概括
药物动力学数据分析正在发展. 机器学习为人口药理动力学分析提供了更快,更少偏差的方法,尽管外推需要进一步研究.
科学领域:
- 药理动力学 药理动力学
- 药理动力学是什么 药理动力学
- 机器学习在药物开发中的作用
背景情况:
- 药理动力学 (PK) 数据分析已经从机械学理解演变为使用非线性混合效应模型进行人口PK分析.
- 种群PK分析显著提升了对不同受试者的PK参数可变性评估.
- 尽管有计算方面的进步,但由于大量的数据集和复杂的模型,在及时将 PK 学习纳入基于模型的药物开发中仍然存在挑战.
研究的目的:
- 探索新型机器学习 (ML) 工具的潜力,以应对人口药动力学分析中的挑战.
- 研究ML方法如何提高药物开发中的药理动力学数据分析的效率和准确性.
主要方法:
- 基因算法在PK分析中用于模型选择的应用.
- 机器学习算法的利用用于共变量选择.
- 实施深度学习模型,用于综合药理动力学和药理动力学 (PK/PD) 数据分析.
主要成果:
- 新的ML方法表明,在药物动力学建模中减少偏差和分析时间的前景.
- 这些先进技术有助于整合更大,更复杂的数据集.
- 改善基于模型的药物开发流程的潜力.
结论:
- 机器学习工具为药理动力学数据分析当前的挑战提供了有希望的解决方案.
- 需要进一步的研究来验证这些ML模型的推断能力.
- 持续的调查对于通过ML推进基于模型的药物开发至关重要.
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