Toward the clinical development of synthetic immunity to cancer.
Published In: Immunological Reviews, 2023, v. 320, n. 1. P. 83 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Garcia, Julie M.; Burnett, Cassandra E.; Roybal, Kole T. 3 of 3
Abstract
Summary: Synthetic biology (synbio) tools, such as chimeric antigen receptors (CARs), have been designed to target, activate, and improve immune cell responses to tumors. These therapies have demonstrated an ability to cure patients with blood cancers. However, there are significant challenges to designing, testing, and efficiently translating these complex cell therapies for patients who do not respond or have immune refractory solid tumors. The rapid progress of synbio tools for cell therapy, particularly for cancer immunotherapy, is encouraging but our development process should be tailored to increase translational success. Particularly, next‐generation cell therapies should be rooted in basic immunology, tested in more predictive preclinical models, engineered for potency with the right balance of safety, educated by clinical findings, and multi‐faceted to combat a range of suppressive mechanisms. Here, we lay out five principles for engineering future cell therapies to increase the probability of clinical impact, and in the context of these principles, we provide an overview of the current state of synbio cell therapy design for cancer. Although these principles are anchored in engineering immune cells for cancer therapy, we posit that they can help guide translational synbio research for broad impact in other disease indications with high unmet need. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Immunological Reviews. 2023/11, Vol. 320, Issue 1, p83
- Document Type:Article
- Subject Area:Consumer Health
- Publication Date:2023
- ISSN:0105-2896
- DOI:10.1111/imr.13245
- Accession Number:173777595
- Copyright Statement:Copyright of Immunological Reviews is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.