相关实验视频
Updated: Jun 16, 2026
09:07
Generation of Transgenic Rats using a Lentiviral Vector Approach
Published on: May 17, 2020
8.7K
转移学习的应用,以预测药物诱导的人体体体内基因表达变化,使用子体内和体内数据
Shauna D O'Donovan1,2,3, Rachel Cavill4, Florian Wimmenauer4
1Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
PloS one
|November 30, 2023
概括
这项研究使用深度学习来从大鼠数据中预测人类肝脏基因表达,改善药物安全性评估. 这种新的方法通过整合不同的数据领域以获得更好的毒理洞察力来提高预测准确性.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算生物学 计算生物学
- 生物技术是生物技术.
背景情况:
- 肝脏是药物代谢和解毒的核心,使其成为药物不良反应的关键地点.
- 人类肝脏组织很难获得,因此需要依赖动物和体外模型进行毒性测试.
- 从临床前模型 (动物,细胞系) 的发现转化为人类临床结果仍然是药物开发中的一个重大挑战.
研究的目的:
- 开发一个包含转移学习原则的深度人工神经网络模型.
- 为了利用大量的实验室和体内的老鼠暴露数据来预测人类体内的基因表达.
- 评估模型能够弥合动物/体外数据和人体体外反应之间的差距的能力.
主要方法:
- 使用了含有老鼠体外和体外暴露数据的开放TG-GATE数据集.
- 应用深度学习与转移学习和域调整技术.
- 训练了一个神经网络,从人类体外数据中预测人类体内基因表达模式,使用大鼠数据进行模型训练.
主要成果:
- 取得了成功的域调整,通过在网络潜伏空间中的老鼠和人类体外数据的不可分割性来证明.
- 为新型化合物生成人体体内活体基因表达的生理学上可信的预测.
- 表明,整合人体体外试验数据显著提高了预测老鼠体内基因表达的时间准确性.
结论:
- 开发的深度学习模型有效地预测了使用转移学习的人体体内生物基因表达模式.
- 域调整是整合来自不同生物领域 (如老鼠和人类) 的数据的可行策略.
- 这种方法提高了预测的准确性,为评估潜在药物毒性提供了更可靠的方法,并改善了临床前临床转化.
相关概念视频
02:43Mouse Models of Cancer Study
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
01:20Drug Product Performance: In Vitro–In Vivo Correlation
In pharmaceutical development, it's crucial to establish a predictive in vitro–in vivo correlation (IVIVC) for two or more formulations to gain a comprehensive understanding of release properties. IVIVC reduces the need for costly in vivo studies and facilitates the establishment of meaningful dissolution specifications with significant cost savings and decreased regulatory burden. Furthermore, a meaningful IVIVC should predict Cmax and AUC within 20%, aligning with FDA guidance while adhering...