为机器学习应用程序调整基于生理学的药物动力学模型.
Sohaib Habiballah1, Brad Reisfeld2,3
1Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, 80523-1301, USA.
Scientific reports
|September 11, 2023
概括
机器学习 (ML) 模型可以从生理学基础的药理动力学 (PBPK) 模型准确预测药理动力学 (PK) 参数. 这种整合通过提高预测建模的准确性和范围来增强药物查和评估.
科学领域:
- 药理动力学 药理动力学
- 机器学习 机器学习
- 药物开发 药物开发
背景情况:
- 在药物开发中,生理学基础的药理动力学 (PBPK) 模型和机器学习 (ML) 是至关重要的.
- 将PBPK模型集成到ML管道中可以提高药物查和评估的准确性.
研究的目的:
- 开发和测试一个独立的ML模块,从PBPK模型中复制总结药业动力学 (PK) 参数.
- 根据PBPK模型 (OpenCAT) 和实验数据评估ML模块的性能.
主要方法:
- 开发了一个ML模块,使用药物特定和疗法特定的输入来预测PK参数.
- 使用一个开源的PBPK模型,OpenCAT,进行方法演示.
- 在各种药物配方中测试了ML模块,具有不同的可溶性和吸收特性.
主要成果:
- 对于总结PK参数,ML模型的预测一般同意在PBPK模型预测的20%以内.
- 在广泛的药物和配方特性中观察到一致性.
- 模型和实验数据之间的差异表明PBPK模型本身存在潜在的局限性.
结论:
- 开发的ML模块有效地回顾了PBPK模型对PK参数的输出.
- 这种综合方法有望提高药物开发中的预测准确性.
- 为了提高实验一致性,可能需要进一步改进PBPK模型.
相关概念视频
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