{"title":"人工智能时代的免疫工程:从基础研究到临床转化","authors":"Meng Zhu, Andy Tay, Xianlei Li","doi":"10.1002/adtp.202500087","DOIUrl":null,"url":null,"abstract":"<p>Artificial intelligence (AI) plays a pivotal role in advancing immune engineering, a field aimed at developing personalized therapies for cancer, autoimmune diseases, and infections. AI is overcoming significant challenges in immune engineering, such as immune system complexity, patient-specific variability, and the dynamic interactions within the immune microenvironment. This perspective highlights how AI is bridging the gap between laboratory research and clinical application through three major pillars: 1) Mechanistic Decoding, where AI integrates multi-omics data to understand immune system complexity and predict molecular interactions; 2) Therapeutic Innovation, where AI designs personalized immunotherapies, such as optimizing antibody-antigen binding and immune receptor dynamics; and 3) Clinical Acceleration, where AI enhances clinical trial designs, speeds up drug development, and adjusts therapies in real-time based on patient responses. Further discussion addresses algorithmic bias, data privacy, and the need for global standards in AI-powered decision-making, ethical and regulatory challenges. AI is not only enabling breakthroughs in immune engineering but also paving the way for customized immune-based therapies. It ensures that the technologies are applied responsibly and equitably in clinical practice.</p>","PeriodicalId":7284,"journal":{"name":"Advanced Therapeutics","volume":"8 9","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Immuno-Engineering in the AI Era: From Fundamental Research to Clinical Translation\",\"authors\":\"Meng Zhu, Andy Tay, Xianlei Li\",\"doi\":\"10.1002/adtp.202500087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Artificial intelligence (AI) plays a pivotal role in advancing immune engineering, a field aimed at developing personalized therapies for cancer, autoimmune diseases, and infections. AI is overcoming significant challenges in immune engineering, such as immune system complexity, patient-specific variability, and the dynamic interactions within the immune microenvironment. This perspective highlights how AI is bridging the gap between laboratory research and clinical application through three major pillars: 1) Mechanistic Decoding, where AI integrates multi-omics data to understand immune system complexity and predict molecular interactions; 2) Therapeutic Innovation, where AI designs personalized immunotherapies, such as optimizing antibody-antigen binding and immune receptor dynamics; and 3) Clinical Acceleration, where AI enhances clinical trial designs, speeds up drug development, and adjusts therapies in real-time based on patient responses. Further discussion addresses algorithmic bias, data privacy, and the need for global standards in AI-powered decision-making, ethical and regulatory challenges. AI is not only enabling breakthroughs in immune engineering but also paving the way for customized immune-based therapies. It ensures that the technologies are applied responsibly and equitably in clinical practice.</p>\",\"PeriodicalId\":7284,\"journal\":{\"name\":\"Advanced Therapeutics\",\"volume\":\"8 9\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Therapeutics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://advanced.onlinelibrary.wiley.com/doi/10.1002/adtp.202500087\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Therapeutics","FirstCategoryId":"3","ListUrlMain":"https://advanced.onlinelibrary.wiley.com/doi/10.1002/adtp.202500087","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
Immuno-Engineering in the AI Era: From Fundamental Research to Clinical Translation
Artificial intelligence (AI) plays a pivotal role in advancing immune engineering, a field aimed at developing personalized therapies for cancer, autoimmune diseases, and infections. AI is overcoming significant challenges in immune engineering, such as immune system complexity, patient-specific variability, and the dynamic interactions within the immune microenvironment. This perspective highlights how AI is bridging the gap between laboratory research and clinical application through three major pillars: 1) Mechanistic Decoding, where AI integrates multi-omics data to understand immune system complexity and predict molecular interactions; 2) Therapeutic Innovation, where AI designs personalized immunotherapies, such as optimizing antibody-antigen binding and immune receptor dynamics; and 3) Clinical Acceleration, where AI enhances clinical trial designs, speeds up drug development, and adjusts therapies in real-time based on patient responses. Further discussion addresses algorithmic bias, data privacy, and the need for global standards in AI-powered decision-making, ethical and regulatory challenges. AI is not only enabling breakthroughs in immune engineering but also paving the way for customized immune-based therapies. It ensures that the technologies are applied responsibly and equitably in clinical practice.