Evidence map›Paper›PMID 37234088›Full record

ArticleiScience2023

Modeling breast cancer proliferation, drug synergies, and alternating therapies.

Wei He, Diane M Demas, Ayesha N Shajahan-Haq, William T Baumann

Open access · goldAbstract read
In one paragraph

Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Wei HeProgram in Genetics, Bioinformatics, and Computational Biology, VT BIOTRANS, Virginia Tech, Blacksburg, VA 24061, USA.
Diane M DemasDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.
Ayesha N Shajahan-HaqDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.
William T BaumannDepartment of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
Georgetown University Medical Center · USVirginia Tech · US

Funding

Tissue Culture Shared ResourceP30CA051008 · NCI · GEORGETOWN UNIVERSITY · PI MARCUS S NOEL · 1990 to 2026
$71.5M
Optimizing Targeted Breast Cancer Therapy by Mathematical Modeling and Experimental StudiesR01CA201092 · NCI · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI BAUMANN, WILLIAM THOMAS, SHAJAHAN-HAQ, AYESHA N. · 2016 to 2020
$2.0M
NCI NIH HHS P30 CA051008NCI NIH HHS R01 CA201092
6 · The paper itself

Abstract

Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance, driving the consideration of combination and alternating therapies. Toward this end, we developed a mathematical model that can simulate various mono, combination, and alternating therapies for ER + breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding Cdk4/6 inhibitors to anti-estrogen therapy. Furthermore, the model is used to optimize an alternating treatment protocol so it works as well as monotherapy while using less total drug dose.

Indexed as

CancerCancer systems biologyComputational bioinformaticsMathematical biosciencesPharmacoinformatics

Identifiers

PMID37234088
PMCPMC10206440
OpenAlexW4366778856

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.