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整合人工智能,机器学习和动物模型用于精确瘤学:弥合临床前和临床差距

Zahid Rafiq^1,2, Tanzeel Bashir³, Weiqin Lu²

¹Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States.

ACS pharmacology & translational science

|October 16, 2025

概括

人工智能 (AI) 和机器学习 (ML) 可以改善研究中的动物模型的翻译. 这种方法增强了药物开发,使得癌症治疗更可预测,更道德,更个性化.

科学领域:

药理学药理学是指药理学的学科.
翻译科学翻译科学
计算生物学计算生物学

背景情况:

动物模型在临床前研究中至关重要,但往往对人类结果的翻译性有限.
从动物研究中预测临床疗效仍然是药物开发中的一个重大挑战.

研究的目的:

提出一个新的范式,将人工智能 (AI) 和机器学习 (ML) 整合到药理学和翻译科学中.
提高治疗开发临床前研究的预测准确性和效率.

主要方法:

利用AI和ML算法来分析来自临床前模型的复杂生物数据.
将计算精度与实验严谨性相结合,弥合动物研究和临床结果之间的差距.

主要成果:

人工智能和机器学习的整合显著放大了动物模型的可翻译性.
加快假设测试和减少药物发现管道中的资源负担.
改善了新型治疗干预措施的临床可预测性.

结论:

人工智能和机器学习的整合为翻译科学提供了一个变革性的方法.
这种计算-实验协同作用促进了更道德,更可扩展和更个性化的癌症治疗方法的开发.
这种范式的转变有望加速向患者提供有效的治疗方法.

关键词:

在这里,我们可以看到AIAIAI.动物模型动物模型临床临床临床临床 ML ML 在 ML 精确瘤学精确瘤学临床前临床前

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