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Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish
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一种用于预测复合大脑度-时间概况的实用制方法:PK建模和机器学习的结合

Koichi Handa1, Daichi Fujita1, Mariko Hirano1

  • 1Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan.

Molecular pharmaceutics
|September 26, 2024
PubMed
概括

开发一种新的in silico方法,将建模和模拟与机器学习相结合,以预测大脑中的药物度. 这种方法减少了对动物进行广泛测试的需求,为研究中枢神经系统药物提供了更有效的方法.

关键词:
在CNS中,CNS是CNS.的PK参数PK参数在QSAR中使用QSAR.复合大脑度-时间概况.复合设计是一种复合设计.机器学习是机器学习.建模和模拟的模型和模拟.小鼠的药理动力学随机的森林随机的森林

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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