整合人工智能、机器学习和精确肿瘤学动物模型：弥合临床前和临床差距

IF 3.7 Q1 CHEMISTRY, MEDICINAL

ACS Pharmacology and Translational Science Pub Date : 2025-09-30 DOI:10.1021/acsptsci.5c00543

Zahid Rafiq*, , , Tanzeel Bashir, , , Weiqin Lu, , and , Nahum Puebla-Osorio*,

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引用次数: 0

摘要

通过人工智能（AI）和机器学习（ML）的整合，可以显著放大动物模型有限的可翻译性。这一观点代表了药理学和转化科学的一种新范式，它加速了假设检验，减轻了资源负担，提高了临床可预测性。通过将计算精度与实验严谨性相结合，这种综合方法提供了更符合伦理、可扩展和个性化的癌症治疗方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Integrating AI, Machine Learning, and Animal Models for Precision Oncology: Bridging Preclinical and Clinical Gaps

The limited translatability of animal models can be significantly amplified by integration of Artificial Intelligence (AI) and Machine Learning (ML). This Viewpoint represents a fresh paradigm in pharmacology and translational science, one that accelerates hypothesis testing, reduces resource burden, and improves clinical predictability. By aligning computational precision with experimental rigor, this integrated approach provides more ethical, scalable, and personalized cancer therapeutics.

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来源期刊

ACS Pharmacology and Translational Science Medicine-Pharmacology (medical)

CiteScore

10.00

自引率

3.30%

发文量

133

期刊介绍： ACS Pharmacology & Translational Science publishes high quality, innovative, and impactful research across the broad spectrum of biological sciences, covering basic and molecular sciences through to translational preclinical studies. Clinical studies that address novel mechanisms of action, and methodological papers that provide innovation, and advance translation, will also be considered. We give priority to studies that fully integrate basic pharmacological and/or biochemical findings into physiological processes that have translational potential in a broad range of biomedical disciplines. Therefore, studies that employ a complementary blend of in vitro and in vivo systems are of particular interest to the journal. Nonetheless, all innovative and impactful research that has an articulated translational relevance will be considered. ACS Pharmacology & Translational Science does not publish research on biological extracts that have unknown concentration or unknown chemical composition. Authors are encouraged to use the pre-submission inquiry mechanism to ensure relevance and appropriateness of research.