Artificial intelligence and anti-cancer drugs' response

被引：0

作者：

Long, Xinrui ^{[1
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Sun, Kai ^{[1
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Lai, Sicen ^{[1
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Liu, Yuancheng ^{[1
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Su, Juan ^{[1
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Chen, Wangqing ^{[1
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Liu, Ruhan ^{[1
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He, Xiaoyu ^{[1
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Zhao, Shuang ^{[1
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Huang, Kai ^{[1
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机构：

[1] Cent South Univ, Xiangya Hosp, Dept Dermatol, Changsha 410028, Peoples R China

[2] Cent South Univ, Xiangya Sch Med, Changsha 410031, Peoples R China

[3] Cent South Univ, Furong Lab, Changsha 410028, Peoples R China

[4] Natl Engn Res Ctr Personalized Diagnost & Therapeu, Changsha 410028, Peoples R China

[5] Cent South Univ, Xiangya Hosp, Hunan Key Lab Skin Canc & Psoriasis, Changsha 410028, Peoples R China

[6] Cent South Univ, Xiangya Hosp, Natl Clin Res Ctr Geriatr Disorders, Changsha 410028, Peoples R China

来源：

ACTA PHARMACEUTICA SINICA B | 2025年 / 15卷 / 07期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

KEY WORDS Drug resistance; Anti-cancer drugs; Artificial intelligence; Multi-omics; Precision medication; NEURAL-NETWORK; CANCER; RESISTANCE; PREDICTION; SIGNATURES; THERAPY; CHEMOTHERAPY; SENSITIVITY; CLASSIFIER; PACLITAXEL;

D O I：

10.1016/j.apsb.2025.05.009

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Drug resistance is one of the key factors affecting the effectiveness of cancer treatment methods, including chemotherapy, radiotherapy, and immunotherapy. Its occurrence is related to factors such as mRNA expression and methylation within cancer cells. If drug resistance in patients can be accurately identified early, doctors can devise more effective treatment plans, which is of great significance for improving patients' survival rates and quality of life. Cancer drug resistance prediction based on artificial intelligence (AI) technology has emerged as a current research hotspot, demonstrating promising application prospects in guiding clinical individualized and precise medication for cancer patients. This review aims to comprehensively summarize the research progress in utilizing AI algorithms to analyze multiand histopathology, for predicting cancer drug resistance. It provides a detailed exposition of the processes involved in data processing , model construction, examines the current challenges faced in this field and future development directions, with the aim of better advancing the progress of precision medicine. <feminine ordinal indicator> 2025 The Authors. Published by Elsevier B.V. on behalf of Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

引用

页码：3355 / 3371

页数：17

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