Evidence map›Paper›PMID 42312897›Full record

ArticlePharmaceutical statistics

Early Phase Dose-Finding Designs for CAR-T Cell Therapies.

Weishi Chen, Pavel Mozgunov, Jimmy Mullaert, Xavier Paoletti

Abstract read
In one paragraph

Article in Pharmaceutical statistics. 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

4 authors.

Weishi ChenMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0003-0718-5901
Pavel MozgunovMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Jimmy MullaertClinical Research Division, Institut Curie, Paris, France.
Xavier PaolettiClinical Research Division, Institut Curie, Paris, France.

Funding

Gates Cambridge TrustNIH HHS NIHR300576UK Medical Research Council MC_UU_00040/3
6 · The paper itself

Abstract

Chimeric Antigen Receptor (CAR)-T cell is an immunotherapy which revolutionised the treatment of relapsed/refractory lymphoma and leukaemia. It is shown to have a higher response rate, higher mid-to-long term overall survival, and lower toxicity than standard treatments. However, due to a lack of dose-limiting toxicity (DLT) and unclear dose-effect relationship, traditional phase I designs of clinical trials cannot lead to accurate selections of the optimal dose (OD). Beside clinical outcomes, the CAR-T cell expansion from serial blood samples is measured at various time points. We propose a novel early phase dose-finding design for CAR-T cells, using both toxicity and activity endpoints to locate the OD. The number of CAR-T cells measured in the peripheral blood is used to indicate activity, which is more sensitive than the short-term clinical responses traditionally used. A Bi-Exponential model is used for the repeated measures of the number of cells for each patient, and is estimated under a Bayesian framework. The model is motivated by biological concerns and is flexible enough to accommodate different shapes of the cell-expansion curve. Three criteria for activity are considered: (1) the number of cells at specific time points, (2) the duration before all cells are eliminated, (3) the area under the cell-expansion curve. Simulation studies show that the OD can be selected with high accuracy even under small sample sizes.

Indexed as

Clinical Trials, Phase I as TopicImmunotherapy, AdoptiveReceptors, Chimeric AntigenResearch DesignT-LymphocytesBayes TheoremDose-Response Relationship, ImmunologicHumansReceptors, Chimeric AntigenCAR‐T cellsdose findingphase I clinical trials

Identifiers

PMID42312897
PMCPMC13277398

What OpenQuestion holds

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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.