{"title":"Advances in high-throughput drug screening based on pharmacotranscriptomics","authors":"Liansheng Qiao, Xiaoqian Huo, Wenting Huang, Zewen Wang, Yue Ren, Yanxia Liu, Qun Li, Yanling Zhang","doi":"10.1016/j.jare.2025.09.006","DOIUrl":null,"url":null,"abstract":"<h3>Background</h3>Drug screening constitutes the predominant paradigm for novel drug discovery. With the development of omics, drug screening has gradually developed into pharmacotranscriptomics-based drug screening (PTDS), which is different from target-based and phenotype-based drug screening. PTDS is a rapidly evolving interdisciplinary field that concurrently demands overcoming large-scale pharmacotranscriptomics profiling and computational challenges inherent to high-dimensional feature data.Aim of review: This review aims to summarize the developmental trajectory and research advancements in PTDS, with a focus on large-scale pharmacotranscriptomics profiling and artificial intelligence-driven data mining. It elucidates the appropriate application fields of PTDS in comparison to traditional drug screening paradigms, thereby providing novel perspectives for the technological evolution and implementation of PTDS.Key scientific concepts of review: PTDS can detect gene expression changes following drug perturbation in cells on a large scale and analyze the efficacy of drug-regulated gene sets, signaling pathways, and even complex diseases by combining artificial intelligence. The technical evolution of PTDS is systematically summarized, encompassing advancements in high-throughput PTDS detection technologies and data analysis methods. PTDS is categorized into microarray, targeted transcriptomics, and RNA-seq. Data analysis of PTDS involves ranking, unsupervised learning, and supervised learning algorithms. All these methods remain active in research and industry, coexisting to address evolving drug screening needs. On this basis, the roles of PTDS in promoting pathway-based drug screening strategies are deeply explored for drug discovery and drug combination design. Meanwhile, we also focus on the application of PTDS in screening and mechanism analysis of traditional Chinese medicine (TCM), which reflects that PTDS is suitable for detecting the complex efficacy of drugs, especially TCM. PTDS is an important development direction for high-throughput screening. By combining with artificial intelligence, PTDS will greatly revolutionize our understanding of drug screening and promote new drug research and development.","PeriodicalId":14952,"journal":{"name":"Journal of Advanced Research","volume":"43 1","pages":""},"PeriodicalIF":13.0000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Research","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1016/j.jare.2025.09.006","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Drug screening constitutes the predominant paradigm for novel drug discovery. With the development of omics, drug screening has gradually developed into pharmacotranscriptomics-based drug screening (PTDS), which is different from target-based and phenotype-based drug screening. PTDS is a rapidly evolving interdisciplinary field that concurrently demands overcoming large-scale pharmacotranscriptomics profiling and computational challenges inherent to high-dimensional feature data.Aim of review: This review aims to summarize the developmental trajectory and research advancements in PTDS, with a focus on large-scale pharmacotranscriptomics profiling and artificial intelligence-driven data mining. It elucidates the appropriate application fields of PTDS in comparison to traditional drug screening paradigms, thereby providing novel perspectives for the technological evolution and implementation of PTDS.Key scientific concepts of review: PTDS can detect gene expression changes following drug perturbation in cells on a large scale and analyze the efficacy of drug-regulated gene sets, signaling pathways, and even complex diseases by combining artificial intelligence. The technical evolution of PTDS is systematically summarized, encompassing advancements in high-throughput PTDS detection technologies and data analysis methods. PTDS is categorized into microarray, targeted transcriptomics, and RNA-seq. Data analysis of PTDS involves ranking, unsupervised learning, and supervised learning algorithms. All these methods remain active in research and industry, coexisting to address evolving drug screening needs. On this basis, the roles of PTDS in promoting pathway-based drug screening strategies are deeply explored for drug discovery and drug combination design. Meanwhile, we also focus on the application of PTDS in screening and mechanism analysis of traditional Chinese medicine (TCM), which reflects that PTDS is suitable for detecting the complex efficacy of drugs, especially TCM. PTDS is an important development direction for high-throughput screening. By combining with artificial intelligence, PTDS will greatly revolutionize our understanding of drug screening and promote new drug research and development.
期刊介绍:
Journal of Advanced Research (J. Adv. Res.) is an applied/natural sciences, peer-reviewed journal that focuses on interdisciplinary research. The journal aims to contribute to applied research and knowledge worldwide through the publication of original and high-quality research articles in the fields of Medicine, Pharmaceutical Sciences, Dentistry, Physical Therapy, Veterinary Medicine, and Basic and Biological Sciences.
The following abstracting and indexing services cover the Journal of Advanced Research: PubMed/Medline, Essential Science Indicators, Web of Science, Scopus, PubMed Central, PubMed, Science Citation Index Expanded, Directory of Open Access Journals (DOAJ), and INSPEC.