一种机器学习方法来实现群体药理动力学建模自动化
Sam Richardson1, Itziar Irurzun Arana2, Andrzej Nowojewski3
1Imaging & Data Analytics, Clinical Pharmacology & Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge, UK. sam.richardson@astrazeneca.com.
本研究引入了一种自动化方法,用于人口药理动力学 (PopPK) 模型的开发,大大减少了时间和精力. pyDarwin框架有效地确定最佳药物模型,提高药物动力学分析的可访问性和可重复性.
科学领域:
- 药理动力学 药理动力学
- 计算生物学 计算生物学
- 药物开发 药物开发
背景情况:
- 种群药动力学 (PopPK) 模型对于了解不同患者群体的药物处置至关重要.
- 传统的PopPK模型开发是耗时和资源密集的.
- 一种自动化的方法可以简化这一过程.
研究的目的:
- 开发和验证用于PopPK模型开发的自动化,即时的方法.
- 为了利用pyDarwin的优化算法来有效地识别血管外药物模型.
- 为了减少PopPK分析所需的手工工作和时间.
主要方法:
- 提出了一个外血管药物的通用模型搜索空间.
- 开发了一个惩罚函数,以防止过度参数化,并确保参数可信性.
- 使用随机森林替代品和使用pyDarwin进行本地搜索的贝叶斯优化.
主要成果:
- 自动化方法在48小时内确定了与专家开发的模型相比较的模型结构.
- 评估了不到2.6%的潜在模型,证明了效率.
- 废除研究证实了惩罚函数和全局搜索的有效性.
结论:
- 在pyDarwin中,一个统一的惩罚函数和模型空间能够自动识别各种药物的PopPK模型结构.
- 这种自动化方法简化了PopPK分析,使其更容易获得.
- 采用可以加快分析,提高模型质量,提高可复制性.
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