Evidence map›Paper›PMID 41669343›Full record

ArticleFrontiers in medicine2026

Humanized anti-CD33 CAR-T cells and antibody-drug conjugates for targeted therapy in acute myeloid leukemia.

Lijun Chen, Honghong Duan, Chunling Huang, Yajing Xu, Zitong Wang, Huibin Huang

Abstract read
In one paragraph

Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lijun Chen *Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Honghong Duan *Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Chunling Huang *Department of Electrocardiogram, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Yajing XuDepartment of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Zitong WangJohns Hopkins University, Baltimore, MD, United States.
Huibin HuangDepartment of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Acute myeloid leukemia (AML) is a malignant disorder originating from myeloid hematopoietic stem and progenitor cells. Despite the availability of current treatment options, a significant number of patients fail to achieve complete remission after initial chemotherapy. CD33, a transmembrane protein highly expressed on AML cells, serves as a promising therapeutic target. This study aimed to develop and evaluate chimeric antigen receptor T cells (CAR-T) and antibody-drug conjugates (ADC) based on humanized antibodies, specifically targeting CD33, to assess their potential efficacy against AML. Methods: Monoclonal antibodies specific to human CD33 were generated by immunizing mice and then humanized. These humanized antibodies were then used to construct CAR-T cells and ADCs, and their cytotoxic properties were evaluated both Results: The Conclusion: The CAR-T cells and ADCs developed in this study, based on humanized antibodies, showed significant anti-tumor efficacy in the AML model. Clone3HM-MMAE, in particular, demonstrated excellent anti-tumor activity along with a strong safety profile. These results strongly support the further development of targeted therapeutic strategies for AML.

Indexed as

AMLantibody-drug conjugatescar-tCD33 antigenhumanized monoclonal antibodies

Identifiers

PMID41669343
PMCPMC12884322

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Registered trials

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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.