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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2506.12185 |
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| _version_ | 1866911006410670080 |
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| author | Elfatimi, Elhoucine Lekbach, Yassir Prakash, Swayam BenMohamed, Lbachir |
| author_facet | Elfatimi, Elhoucine Lekbach, Yassir Prakash, Swayam BenMohamed, Lbachir |
| contents | In the past, the development of vaccines and immunotherapeutics relied heavily on trial-and-error experimentation and extensive in vivo testing, often requiring years of pre-clinical and clinical trials. Today, artificial intelligence (AI) and deep learning (DL) are actively transforming vaccine and immunotherapeutic design, by (i) offering predictive frameworks that support rapid, data-driven decision-making; (ii) increasingly being implemented as time- and resource-efficient strategies that integrate computational models, systems vaccinology, and multi-omics data to better phenotype, differentiate, and classify patient diseases and cancers; predict patients' immune responses; and identify the factors contributing to optimal vaccine and immunotherapeutic protective efficacy; (iii) refining the selection of B- and T-cell antigen/epitope targets to enhance efficacy and durability of immune protection; and (iv) enabling a deeper understanding of immune regulation, immune evasion, immune checkpoints, and regulatory pathways. The future of AI and DL points toward (i) replacing animal preclinical testing of drugs, vaccines, and immunotherapeutics with computational-based models, as recently proposed by the United States FDA; and (ii) enabling real-time in vivo modeling for immunobridging and prediction of protection in clinical trials. This may result in a fast and transformative shift for the development of personal vaccines and immunotherapeutics against infectious pathogens and cancers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence and Machine Learning in the Development of Vaccines and Immunotherapeutics Yesterday, Today, and Tomorrow Elfatimi, Elhoucine Lekbach, Yassir Prakash, Swayam BenMohamed, Lbachir Artificial Intelligence In the past, the development of vaccines and immunotherapeutics relied heavily on trial-and-error experimentation and extensive in vivo testing, often requiring years of pre-clinical and clinical trials. Today, artificial intelligence (AI) and deep learning (DL) are actively transforming vaccine and immunotherapeutic design, by (i) offering predictive frameworks that support rapid, data-driven decision-making; (ii) increasingly being implemented as time- and resource-efficient strategies that integrate computational models, systems vaccinology, and multi-omics data to better phenotype, differentiate, and classify patient diseases and cancers; predict patients' immune responses; and identify the factors contributing to optimal vaccine and immunotherapeutic protective efficacy; (iii) refining the selection of B- and T-cell antigen/epitope targets to enhance efficacy and durability of immune protection; and (iv) enabling a deeper understanding of immune regulation, immune evasion, immune checkpoints, and regulatory pathways. The future of AI and DL points toward (i) replacing animal preclinical testing of drugs, vaccines, and immunotherapeutics with computational-based models, as recently proposed by the United States FDA; and (ii) enabling real-time in vivo modeling for immunobridging and prediction of protection in clinical trials. This may result in a fast and transformative shift for the development of personal vaccines and immunotherapeutics against infectious pathogens and cancers. |
| title | Artificial Intelligence and Machine Learning in the Development of Vaccines and Immunotherapeutics Yesterday, Today, and Tomorrow |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.12185 |