Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives
Using artificial intelligence (AI) to enhance chimeric antigen receptor (CAR)-based therapies’ design, production, and delivery is a novel and promising approach. This review provides an overview of the current applications and challenges of AI for CAR-based therapies and suggests some directions fo...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-12-01
|
| Series: | Therapeutic Advances in Vaccines and Immunotherapy |
| Online Access: | https://doi.org/10.1177/25151355241305856 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850249865848684544 |
|---|---|
| author | Muqadas Shahzadi Hamad Rafique Ahmad Waheed Hina Naz Atifa Waheed Feruza Ravshanovna Zokirova Humera Khan |
| author_facet | Muqadas Shahzadi Hamad Rafique Ahmad Waheed Hina Naz Atifa Waheed Feruza Ravshanovna Zokirova Humera Khan |
| author_sort | Muqadas Shahzadi |
| collection | DOAJ |
| description | Using artificial intelligence (AI) to enhance chimeric antigen receptor (CAR)-based therapies’ design, production, and delivery is a novel and promising approach. This review provides an overview of the current applications and challenges of AI for CAR-based therapies and suggests some directions for future research and development. This paper examines some of the recent advances of AI for CAR-based therapies, for example, using deep learning (DL) to design CARs that target multiple antigens and avoid antigen escape; using natural language processing to extract relevant information from clinical reports and literature; using computer vision to analyze the morphology and phenotype of CAR cells; using reinforcement learning to optimize the dose and schedule of CAR infusion; and using AI to predict the efficacy and toxicity of CAR-based therapies. These applications demonstrate the potential of AI to improve the quality and efficiency of CAR-based therapies and to provide personalized and precise treatments for cancer patients. However, there are also some challenges and limitations of using AI for CAR-based therapies, for example, the lack of high-quality and standardized data; the need for validation and verification of AI models; the risk of bias and error in AI outputs; the ethical, legal, and social issues of using AI for health care; and the possible impact of AI on the human role and responsibility in cancer immunotherapy. It is important to establish a multidisciplinary collaboration among researchers, clinicians, regulators, and patients to address these challenges and to ensure the safe and responsible use of AI for CAR-based therapies. |
| format | Article |
| id | doaj-art-e7bdaa61a73342a4b40d6825197a1f74 |
| institution | OA Journals |
| issn | 2515-1363 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Therapeutic Advances in Vaccines and Immunotherapy |
| spelling | doaj-art-e7bdaa61a73342a4b40d6825197a1f742025-08-20T01:58:23ZengSAGE PublishingTherapeutic Advances in Vaccines and Immunotherapy2515-13632024-12-011210.1177/25151355241305856Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectivesMuqadas ShahzadiHamad RafiqueAhmad WaheedHina NazAtifa WaheedFeruza Ravshanovna ZokirovaHumera KhanUsing artificial intelligence (AI) to enhance chimeric antigen receptor (CAR)-based therapies’ design, production, and delivery is a novel and promising approach. This review provides an overview of the current applications and challenges of AI for CAR-based therapies and suggests some directions for future research and development. This paper examines some of the recent advances of AI for CAR-based therapies, for example, using deep learning (DL) to design CARs that target multiple antigens and avoid antigen escape; using natural language processing to extract relevant information from clinical reports and literature; using computer vision to analyze the morphology and phenotype of CAR cells; using reinforcement learning to optimize the dose and schedule of CAR infusion; and using AI to predict the efficacy and toxicity of CAR-based therapies. These applications demonstrate the potential of AI to improve the quality and efficiency of CAR-based therapies and to provide personalized and precise treatments for cancer patients. However, there are also some challenges and limitations of using AI for CAR-based therapies, for example, the lack of high-quality and standardized data; the need for validation and verification of AI models; the risk of bias and error in AI outputs; the ethical, legal, and social issues of using AI for health care; and the possible impact of AI on the human role and responsibility in cancer immunotherapy. It is important to establish a multidisciplinary collaboration among researchers, clinicians, regulators, and patients to address these challenges and to ensure the safe and responsible use of AI for CAR-based therapies.https://doi.org/10.1177/25151355241305856 |
| spellingShingle | Muqadas Shahzadi Hamad Rafique Ahmad Waheed Hina Naz Atifa Waheed Feruza Ravshanovna Zokirova Humera Khan Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives Therapeutic Advances in Vaccines and Immunotherapy |
| title | Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives |
| title_full | Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives |
| title_fullStr | Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives |
| title_full_unstemmed | Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives |
| title_short | Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives |
| title_sort | artificial intelligence for chimeric antigen receptor based therapies a comprehensive review of current applications and future perspectives |
| url | https://doi.org/10.1177/25151355241305856 |
| work_keys_str_mv | AT muqadasshahzadi artificialintelligenceforchimericantigenreceptorbasedtherapiesacomprehensivereviewofcurrentapplicationsandfutureperspectives AT hamadrafique artificialintelligenceforchimericantigenreceptorbasedtherapiesacomprehensivereviewofcurrentapplicationsandfutureperspectives AT ahmadwaheed artificialintelligenceforchimericantigenreceptorbasedtherapiesacomprehensivereviewofcurrentapplicationsandfutureperspectives AT hinanaz artificialintelligenceforchimericantigenreceptorbasedtherapiesacomprehensivereviewofcurrentapplicationsandfutureperspectives AT atifawaheed artificialintelligenceforchimericantigenreceptorbasedtherapiesacomprehensivereviewofcurrentapplicationsandfutureperspectives AT feruzaravshanovnazokirova artificialintelligenceforchimericantigenreceptorbasedtherapiesacomprehensivereviewofcurrentapplicationsandfutureperspectives AT humerakhan artificialintelligenceforchimericantigenreceptorbasedtherapiesacomprehensivereviewofcurrentapplicationsandfutureperspectives |