Elfatimi Elhoucine, Lekbach Yassir, Prakash Swayam, BenMohamed Lbachir
Laboratory of Cellular and Molecular Immunology, College of Medicine, The Gavin Herbert Eye Institute, University of California, Irvine, Irvine, CA, United States.
Institute for Immunology, University of California, Irvine Medical Center, Irvine, CA, United States.
Front Artif Intell. 2025 Jul 18;8:1620572. doi: 10.3389/frai.2025.1620572. eCollection 2025.
The development of vaccines and immunotherapies against infectious diseases and cancers has been one of the significant achievements of medical science in the last century. Subunit vaccines offer key advantages over whole-inactivated or attenuated-pathogen-based vaccines, as they elicit more specific B-and T-cell responses with improved safety, immunogenicity, and protective efficacy. However, developing subunit vaccines is often cost-and time-consuming. In the past, the development of vaccines and immunotherapeutics relied heavily on trial-and-error experimentation, as well as extensive and costly testing, which typically required years of pre-clinical and clinical trials. Today, artificial intelligence (AI) and deep learning (DL) are actively transforming vaccine and immunotherapeutic research by (i) offering predictive frameworks that support rapid, data-driven decision-making, (ii) integrating computational models, systems vaccinology, and multi-omics data (iii) helping to better phenotype, differentiate, and classify patients diseases and cancers; (iv), integrating host characteristics for tailored vaccines and immunotherapeutics; (v) refining the selection of B-and T-cell antigen/epitope targets to enhance efficacy and durability of immune protection; and (vi) enabling a deeper understanding of immune regulation, immune evasion, and regulatory pathways. Artificial intelligence and DL are pushing the boundaries toward (i) the potential replacement of animal preclinical testing of vaccines and immunotherapeutics with computational-based models, as recently proposed by the United States NIH and FDA, and (ii) improving clinical trials by enabling real-time modeling for immune-bridging, predicting patients' immune responses, safety, and protective efficacy to vaccines and immunotherapeutics. In this review, we describe the past and current applications of AI and DL as time-and resource-efficient strategies and discuss future challenges in implementing AI and DL as new transformative fields that may facilitate the rapid development of precision and personalized vaccines and immunotherapeutics for infectious diseases and cancers.
针对传染病和癌症的疫苗及免疫疗法的发展是上个世纪医学科学的重大成就之一。亚单位疫苗相对于全灭活或减毒病原体疫苗具有关键优势，因为它们能引发更具特异性的B细胞和T细胞反应，安全性、免疫原性和保护效力也更高。然而，研发亚单位疫苗通常既耗费成本又耗时。过去，疫苗和免疫疗法的研发严重依赖反复试验以及广泛且昂贵的测试，这通常需要数年的临床前和临床试验。如今，人工智能（AI）和深度学习（DL）正在积极改变疫苗和免疫疗法研究，具体方式如下：（i）提供支持快速、数据驱动决策的预测框架；（ii）整合计算模型、系统疫苗学和多组学数据；（iii）帮助更好地表型分析、区分和分类患者的疾病及癌症；（iv）整合宿主特征以开发定制化疫苗和免疫疗法；（v）优化B细胞和T细胞抗原/表位靶点的选择，以提高免疫保护的效力和持久性；（vi）加深对免疫调节、免疫逃逸和调节途径的理解。人工智能和深度学习正在推动界限，朝着（i）如美国国立卫生研究院（NIH）和食品药品监督管理局（FDA）最近提议的，用基于计算的模型潜在替代疫苗和免疫疗法的动物临床前测试；（ii）通过实现免疫桥接的实时建模、预测患者对疫苗和免疫疗法的免疫反应、安全性及保护效力来改进临床试验。在本综述中，我们将人工智能和深度学习的过去及当前应用描述为节省时间和资源的策略，并讨论将人工智能和深度学习作为新的变革性领域实施时未来面临的挑战，这些领域可能会促进针对传染病和癌症的精准及个性化疫苗和免疫疗法的快速发展。
