基于目标选择和临床试验设计的临床试验结果预测,使用多模式人工智能
Alex Aliper1, Roman Kudrin1, Daniil Polykovskiy2
1Insilico Medicine AI Ltd, Masdar City, Abu Dhabi, United Arab Emirates.
Clinical pharmacology and therapeutics
|July 24, 2023
概括
本研究介绍了inClinico,一个AI平台,使用多式联络数据预测II期临床试验的成功. 该工具实现了高精度,显示了提高药物开发效率和投资回报的潜力.
科学领域:
- 计算生物学是一种计算生物学.
- 人工智能在药物发现中的作用
- 临床试验分析学
背景情况:
- 药物发现是高风险的,失败率很高.
- 预测临床试验结果可以提高效率和患者益处.
- 现有的方法往往缺乏全面的预测能力.
研究的目的:
- 引入inClinico,一个用于预测II期临床试验结果的AI平台.
- 利用生成性人工智能和多式联运数据来提高预测准确度.
- 通过回顾性和前性研究来验证平台的性能.
主要方法:
- 开发了一个基于变压器的AI平台,inClinico.
- 集成的生成人工智能与多式联络数据 (omics,文本,试验设计,分子特性).
- 采用一组预测引擎进行可靠的预测.
主要成果:
- 在第二阶段到第三阶段的过渡预测中实现了0.88 ROC AUC.
- 在预测现实世界II期试验结果时,证明了79%的准确性.
- 在虚拟投资组合应用中展示了35%的9个月ROI.
结论:
- inClinico准确地预测了II期临床试验的成功.
- 人工智能平台为优化药物开发管道提供了有价值的工具.
- inClinico在制药行业的投资策略中具有潜在的应用.
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