Evidence map›Paper›PMID 31492947›Full record

ArticleScientific reports2019

Experimentally-driven mathematical modeling to improve combination targeted and cytotoxic therapy for HER2+ breast cancer.

Angela M Jarrett, Alay Shah, Meghan J Bloom, Matthew T McKenna, David A Hormuth, Thomas E Yankeelov, Anna G Sorace

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.6field-weighted citation impact, top 9% 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

20 citing papers in PubMed, 54 citations in OpenAlex.

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  13. Optimizing combination therapy in a murine model of HER2+ breast cancer.Computer methods in applied mechanics and engineering · 2022
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  17. Towards integration ofScientific reports · 2020
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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

7 authors at 4 institutions in 1 country.

Angela M JarrettInstitute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA.ORCID http://orcid.org/0000-0002-6346-9557
Alay ShahDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA.
Meghan J BloomDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA.
Matthew T McKennaDepartment of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, 37232, USA.ORCID http://orcid.org/0000-0001-5003-539X
David A HormuthInstitute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA.ORCID http://orcid.org/0000-0002-9643-1694
Thomas E YankeelovInstitute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA. thomas.yankeelov@utexas.edu.
Anna G SoraceDepartment of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35209, USA. asorace@uabmc.edu.ORCID http://orcid.org/0000-0002-3640-3966
Livestrong Foundation · USThe University of Texas at Austin · USUniversity of Alabama at Birmingham · USVanderbilt University · US

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007347 · NIGMS · VANDERBILT UNIVERSITY · PI WILLIAMS, CHRISTOPHER S. · 1985 to 2023
$26.3M
Quantitative Multiscale Imaging to Optimize Cancer Treatment StrategiesR01CA186193 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, RERICHA, ERIN C · 2014 to 2019
$3.1M
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast CancerU01CA174706 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, YANKEELOV, THOMAS E · 2013 to 2018
$2.5M
Development and Validation of a Predictive Model of Chemotherapy in Breast CancerF30CA203220 · NCI · VANDERBILT UNIVERSITY · PI MCKENNA, MATTHEW T. · 2016 to 2019
$134k
NCI NIH HHS F30 CA203220NCI NIH HHS R01 CA186193NCI NIH HHS U01 CA174706NIGMS NIH HHS T32 GM007347
6 · The paper itself

Abstract

The goal of this study is to experimentally and computationally investigate combination trastuzumab-paclitaxel therapies and identify potential synergistic effects due to sequencing of the therapies with in vitro imaging and mathematical modeling. Longitudinal alterations in cell confluence are reported for an in vitro model of BT474 HER2+ breast cancer cells following various dosages and timings of paclitaxel and trastuzumab combination regimens. Results of combination drug regimens are evaluated for drug interaction relationships based on order, timing, and quantity of dose of the drugs. Altering the order of treatments, with the same total therapeutic dose, provided significant changes in overall cell confluence (p < 0.001). Two mathematical models are introduced that are constrained by the in vitro data to simulate the tumor cell response to the individual therapies. A collective model merging the two individual drug response models was designed to investigate the potential mechanisms of synergy for paclitaxel-trastuzumab combinations. This collective model shows increased synergy for regimens where trastuzumab is administered prior to paclitaxel and suggests trastuzumab accelerates the cytotoxic effects of paclitaxel. The synergy derived from the model is found to be in agreement with the combination index, where both indicate a spectrum of additive and synergistic interactions between the two drugs dependent on their dose order. The combined in vitro results and development of a mathematical model of drug synergy has potential to evaluate and improve standard-of-care combination therapies in cancer.

Indexed as

Models, BiologicalAntineoplastic Combined Chemotherapy ProtocolsBreast NeoplasmsCalibrationCell DeathCell Line, TumorComputer SimulationErb-b2 Receptor Tyrosine KinasesFemaleHumansPaclitaxelTrastuzumabTreatment OutcomeErb-b2 Receptor Tyrosine KinasesPaclitaxelTrastuzumab

Identifiers

PMID31492947
PMCPMC6731321
OpenAlexW2971820981

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.