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使用机器学习预测大鼠的药理动力学:基于经验,区分和PBPK的方法之间的比较研究
Moritz Walter1, Ghaith Aljayyoussi2, Bettina Gerner2
1Boehringer Ingelheim Pharma GmbH & Co. KG, Medicinal Chemistry, Computational Chemistry, Biberach, Germany.
机器学习模型现在可以在合成之前预测药物药理动力学 (PK) 概况. 这有助于优先考虑具有更好的PK特性的候选药物,改善临床前和临床药物开发.
科学领域:
- 药理动力学 药理动力学
- 药物发现 药物发现 药物发现
- 计算化学的计算化学
背景情况:
- 药物开发需要高强度和有利的药理动力学 (PK) 特性才能持续有效.
- 在体内PK研究对于在临床前和临床环境中剂量估计至关重要.
- 使用机器学习 (ML) 预测ADME属性已经确立,但PK配置文件预测正在出现.
研究的目的:
- 系统地比较不同的方法来预测大鼠的PK概况.
- 评估ML与经验或机械学PK模型的整合,以进行合成前预测.
- 使用内部临床前数据评估各种PK预测方法的准确性.
主要方法:
- 四种PK概况预测方法的比较:基于NCA,纯ML,区间建模和基于生理的药理动力学 (PBPK) 建模.
- 利用了超过1000个小分子的内部临床前数据.
- 用几何平均折叠误差对等离子体度-时间概况进行评估的预测准确性.
主要成果:
- 纯ML,隔间和PBPK建模方法在PK概况预测中显示了可比的准确性.
- 这三种方法的表现优于基于标准非分支分析 (NCA) 的预测.
- 对于大量小分子数据集,可以准确地预测PK配置文件.
结论:
- 与ML集成的PK建模显著提高了在合成之前预测药物行为的能力.
- 这种方法可以加强对具有可取药理学特性的候选药物的优先考虑.
- 促进更高效的药物发现和开发管道.
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