ReviewTransfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie2020
AIM Platform: A Novel Nano Artificial Antigen-Presenting Cell-Based Clinical System Designed to Consistently Produce Multi-Antigen-Specific T-Cell Products with Potent and Durable Anti-Tumor Properties.
Review in Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Review
- T-cell receptor/CD28-targeted immunotherapeutics selectively drive naive T-cell expansion to generate functional HIV-specific responses.Journal of virology · 2025Article
- Combining antigen specific T-cells with T-cell engager therapy induces a molecular signature that favors T-cell fitness.Blood immunology & cellular therapy · 2025Article
- The DC-T cell axis is an effective target for the treatment of non-small cell lung cancer.Immunity, inflammation and disease · 2023Review
- AIM™ platform: A new immunotherapy approach for viral diseases.Frontiers in medicine · 2022Article
- Nanomedicine for Immunotherapy Targeting Hematological Malignancies: Current Approaches and Perspective.Nanomaterials (Basel, Switzerland) · 2021Review
- Antigen-Presenting Cells: Potential of Proven und New Players in Immune Therapies.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the last decade, tremendous progress has been made in the field of adoptive cell therapy. The two prevailing modalities include endogenous non-engineered approaches and genetically engineered T-cell approaches. Endogenous non-engineered approaches include dendritic cell-based systems and tumor-infiltrating lymphocytes (TIL) that are used to produce multi-antigen-specific T-cell products. Genetically engineered approaches, such as T-cell receptor engineered cells and chimeric antigen receptor T cells are used to produce single antigen-specific T-cell products. It is noted by the authors that there are alternative methods to sort for antigen-specific T cells such as peptide multimer sorting or cytokine secretion assay-based sorting, both of which are potentially challenging for broad development and commercialization. In this review, we are focusing on a novel nanoparticle technology that generates a non-engineered product from the endogenous T-cell repertoire. The most common approaches for ex vivo activation and expansion of endogenous, non-genetically engineered cell therapy products rely on dendritic cell-based systems or IL-2 expanded TIL. Hurdles remain in developing efficient, consistent, controlled processes; thus, these processes still have limited access to broad patient populations. Here, we describe a novel approach to produce cellular therapies at clinical scale, using proprietary nanoparticles combined with a proprietary manufacturing process to enrich and expand antigen-specific CD8
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.