Evidence map›Paper›PMID 35856032›Full record

ArticleiScience2022

Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer.

Hanwen Wang, Chen Zhao, Cesar A Santa-Maria, Leisha A Emens, Aleksander S Popel

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
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

37 citing papers in PubMed, 39 citations in OpenAlex.

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  14. Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade.Proceedings of the National Academy of Sciences of the United States of America · 2024
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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

5 authors at 2 institutions in 2 countries.

Hanwen WangDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Chen ZhaoDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Cesar A Santa-MariaDepartment of Oncology, the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD21205, USA.
Leisha A EmensUniversity of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, USA.
Aleksander S PopelDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Johns Hopkins University · USUPMC Hillman Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative systems pharmacology (QSP) modeling is an emerging mechanistic computational approach that couples drug pharmacokinetics/pharmacodynamics and the course of disease progression. It has begun to play important roles in drug development for complex diseases such as cancer, including triple-negative breast cancer (TNBC). The combination of the anti-PD-L1 antibody atezolizumab and nab-paclitaxel has shown clinical activity in advanced TNBC with PD-L1-positive tumor-infiltrating immune cells. As tumor-associated macrophages (TAMs) serve as major contributors to the immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model to investigate their impact on cancer treatment. We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor microenvironment while maintaining its predictive power of the trial results at the population level. Despite its high mechanistic complexity, the modularized QSP platform can be readily reproduced, expanded for new species of interest, and applied in clinical trial simulation.

Indexed as

BiophysicsCancerImmunologyPharmacology

Identifiers

PMID35856032
PMCPMC9287616
OpenAlexW4283730247

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.