Evidence map›Paper›PMID 37921419›Full record

ArticleCancer research communications2023

Mathematical Modeling Identifies Optimum Palbociclib-fulvestrant Dose Administration Schedules for the Treatment of Patients with Estrogen Receptor-positive Breast Cancer.

Yu-Chen Cheng, Shayna Stein, Agostina Nardone, Weihan Liu, Wen Ma, Gabriella Cohen, Cristina Guarducci, Thomas O McDonald, Rinath Jeselsohn, Franziska Michor

Open access · goldAbstract read
In one paragraph

Article in Cancer research communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.8field-weighted citation impact, top 8% 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

6 citing papers in PubMed, 11 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

10 authors at 3 institutions in 1 country.

Yu-Chen ChengDepartment of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0003-1869-4760
Shayna SteinDepartment of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0009-0008-3939-9019
Agostina NardoneDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston Massachusetts.ORCID 0000-0003-3277-1101
Weihan LiuDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston Massachusetts.ORCID 0000-0002-1023-7955
Wen MaDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston Massachusetts.ORCID 0000-0002-1427-7671
Gabriella CohenDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston Massachusetts.ORCID 0000-0001-8460-6250
Cristina GuarducciDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston Massachusetts.ORCID 0000-0002-3234-564X
Thomas O McDonaldDepartment of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0002-5272-0187
Rinath JeselsohnDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston Massachusetts.ORCID 0000-0001-7996-7529
Franziska MichorDepartment of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0003-4869-8842
Dana-Farber Cancer Institute · USHarvard University · USBroad Institute · US

Funding

Project 3: Single Cell Measures of Intratumor Diversity for Optimal Breast Cancer TherapyU54CA193461 · NCI · DANA-FARBER CANCER INST · PI MICHOR, FRANZISKA · 2015 to 2020
$11.2M
Optimizing CDK7 Inhibitor Therapeutic Strategies for ER+ Breast CancerR01CA237414 · NCI · DANA-FARBER CANCER INST · PI Rinath M. Jeselsohn · 2019 to 2026
$2.3M
Elucidating and overcoming endocrine resistance driven by ESR1 mutationsK08CA191058 · NCI · DANA-FARBER CANCER INST · PI JESELSOHN, RINATH M. · 2015 to 2019
$886k
NCI NIH HHS K08 CA191058NCI NIH HHS R01 CA237414NCI NIH HHS U54 CA193461
6 · The paper itself

Abstract

Cyclin-dependent kinases 4/6 (CDK4/6) inhibitors such as palbociclib are approved for the treatment of metastatic estrogen receptor-positive (ER+) breast cancer in combination with endocrine therapies and significantly improve outcomes in patients with this disease. However, given the large number of possible pairwise drug combinations and administration schedules, it remains unclear which clinical strategy would lead to best survival. Here, we developed a computational, cell cycle-explicit model to characterize the pharmacodynamic response to palbociclib-fulvestrant combination therapy. This pharmacodynamic model was parameterized, in a Bayesian statistical inference approach, using in vitro data from cells with wild-type estrogen receptor (WT-ER) and cells expressing the activating missense ER mutation, Y537S, which confers resistance to fulvestrant. We then incorporated pharmacokinetic models derived from clinical data into our computational modeling platform. To systematically compare dose administration schedules, we performed in silico clinical trials based on integrating our pharmacodynamic and pharmacokinetic models as well as considering clinical toxicity constraints. We found that continuous dosing of palbociclib is more effective for lowering overall tumor burden than the standard, pulsed-dose palbociclib treatment. Importantly, our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment strategies in search of optimal combination dosing strategies of other cell-cycle inhibitors in ER+ breast cancer. SIGNIFICANCE: We created a computational modeling platform to predict the effects of fulvestrant/palbocilib treatment on WT-ER and Y537S-mutant breast cancer cells, and found that continuous treatment schedules are more effective than the standard, pulsed-dose palbociclib treatment schedule.

Indexed as

Breast NeoplasmsBayes TheoremFemaleFulvestrantHumansPiperazinesPyridinesReceptors, EstrogenFulvestrantpalbociclibPiperazinesPyridinesReceptors, Estrogen

Identifiers

PMID37921419
PMCPMC10652811
OpenAlexW4388291181

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.