基于生物标志物的生理年龄的人工智能建模:对第一阶段药物代谢酶表型的影响
Amruta Gajanan Bhat1, Murali Ramanathan1,2
1Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA.
CPT: pharmacometrics & systems pharmacology
|November 14, 2024
概括
使用集体学习和NHANES数据开发了生理年龄 (PA) 模型. 这些模型准确地预测药物代谢,并与关键健康指标和酶活性相关联.
科学领域:
- 药理学和老年学 药理学和老年学
- 生物标志物发现发现
- 在健康科学领域进行集体学习.
背景情况:
- 衰老是影响健康结果,疾病进展和药物反应的关键因素.
- 对生理年龄 (PA) 的准确评估对于了解个体健康轨迹和药物代谢至关重要.
- 现有的PA评估方法可能无法完全捕捉衰老过程或药物相互作用的复杂性.
研究的目的:
- 开发和验证基于集体学习的生理年龄 (PA) 模型,用于评估药物代谢.
- 通过使用综合健康数据,确定PA的关键预测因子.
- 调查PA与药物代谢酶表型之间的关联.
主要方法:
- 利用了来自22,307名参与者的国家健康和营养检查调查 (NHANES) 数据.
- 开发了两种PA模型 (PA-M1和PA-M2),使用集体学习,结合各种生物标志物和临床数据.
- 采用贝叶斯网络用于机械系统药理学建模和评估端粒磨损.
主要成果:
- PA模型表现出强大的性能和令人满意的独立验证.
- 胰腺压力的关键预测因素包括克洛托,瘦身量,血糖血红蛋白,静脉血压,血清肌素和胆固醇.
- 模型预测的PA显著与主要药物代谢酶 (CYP1A2,CYP2A6,CYP2E1,XO,NAT-2) 的活性和端粒衰减有关.
结论:
- 集体学习模型提供了一种可靠的方法,用于使用可访问的生物标志物来评估生理年龄.
- 生理年龄显然与第一阶段药物代谢酶的活性有关.
- 这些PA模型可以增强对药物代谢和个性化医疗方法的评估.
相关概念视频
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01:15Model Approaches for Pharmacokinetic Data: Physiological Models
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