Evidence map›Paper›PMID 39628582›Full record

ArticleiScience2024

Mathematical multi-compartment modeling of chronic lymphocytic leukemia cell kinetics under ibrutinib.

Melanie Schulz, Sanne Bleser, Manouk Groels, Dragan Bošnački, Jan A Burger, Nicholas Chiorazzi, Carsten Marr

Abstract read
In one paragraph

Article in iScience, 2024. 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.

Melanie SchulzInstitute of AI for Health, Helmholtz Munich - German Research Centre for Environmental Health, Neuherberg, Germany.
Sanne BleserInstitute of AI for Health, Helmholtz Munich - German Research Centre for Environmental Health, Neuherberg, Germany.
Manouk GroelsInstitute of AI for Health, Helmholtz Munich - German Research Centre for Environmental Health, Neuherberg, Germany.
Dragan BošnačkiFaculty of Biomedical Engineering, Technichal University Eindhoven, Eindhoven, the Netherlands.
Jan A BurgerDepartment of Leukemia, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Nicholas ChiorazziFeinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, USA.
Carsten MarrInstitute of AI for Health, Helmholtz Munich - German Research Centre for Environmental Health, Neuherberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Bruton tyrosine kinase inhibitor ibrutinib is an effective treatment for patients with chronic lymphocytic leukemia (CLL). While it rapidly reduces lymph node and spleen size, it initially increases the number of lymphocytes in the blood due to cell redistribution. A previously published mathematical model described and quantified those cell kinetics. Here, we propose an alternative mechanistic model that outperforms the previous model in 26 of 29 patients. Our model introduces constant subcompartments for healthy lymphocytes and benign tissue and treats spleen and lymph nodes as separate compartments. This three-compartment model (comprising blood, spleen, and lymph nodes) performed significantly better in patients without a mutation in the IGHV gene, indicating a diverse response to ibrutinib for cells residing in lymph nodes and spleen. Additionally, high ZAP-70 expression was linked to less cell death in the spleen. Overall, our study enhances understanding of CLL genetics and patient response to ibrutinib and provides a framework applicable to the study of similar drugs.

Indexed as

Biological sciencesComputer scienceNatural sciences

Identifiers

PMID39628582
PMCPMC11613170

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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.