Evidence map›Paper›PMID 41039855›Full record

ReviewClinical pharmacology and therapeutics2026

Foundational Principles for the Quantitative Translation of T-Cell Therapeutics for Hematologic Malignancies and Immunology.

Peter Ashcroft, Sarah McFann, Alex Ferguson, Arthur Van De Vyver, Suzanne Gaudet, Eshita Khera, Jan-Frederik Schlender

Abstract readReview
In one paragraph

Review in Clinical pharmacology and therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Peter AshcroftBiomedical Research, Novartis, Basel, Switzerland.ORCID 0000-0003-4067-7692
Sarah McFannBiomedical Research, Novartis, Cambridge, Massachusetts, USA.ORCID 0000-0002-9390-9349
Alex FergusonBiomedical Research, Novartis, Cambridge, Massachusetts, USA.ORCID 0000-0002-9503-9949
Arthur Van De VyverBiomedical Research, Novartis, Basel, Switzerland.ORCID 0000-0002-8613-5568
Suzanne GaudetBiomedical Research, Novartis, Cambridge, Massachusetts, USA.ORCID 0000-0002-5701-2825
Eshita KheraBiomedical Research, Novartis, Cambridge, Massachusetts, USA.ORCID 0000-0002-6971-1033
Jan-Frederik SchlenderBiomedical Research, Novartis, Basel, Switzerland.ORCID 0009-0003-9079-5205

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

T-cell engaging antibodies (TCEs) and chimeric antigen receptor (CAR) T cells (CAR-T cells) are among precision medicine therapies that have revolutionized the treatment of hematologic cancers. Their success in oncology has piqued interest in translating this promise into additional indications, such as autoimmune disorders. This review discusses the foundational principles for mechanistic modeling to provide a unified assessment framework for cross-modality (i.e., CAR-T cells vs. TCEs) and cross-indication (i.e., oncology vs. immunology) translation. This framework captures the unique elements of each modality, such as CAR-T cellular kinetics, TCE pharmacokinetics, and complex formation with target cells, as well as shared elements such as B-cell kinetics and biodistribution across indications. We describe how this integrated approach can lead to informed decision making for more personalized and effective treatment strategies with these immune therapies.

Indexed as

Hematologic NeoplasmsImmunotherapy, AdoptiveT-LymphocytesTranslational Research, BiomedicalAnimalsHumansPrecision MedicineReceptors, Chimeric AntigenReceptors, Chimeric Antigen

Identifiers

PMID41039855
PMCPMC12746527

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.