Evidence map›Paper›PMID 42394905›Full record

ArticleBioengineering & translational medicine2026

Dynamic pillar-perfusion platform for screening enzyme-induced self-assembling peptide therapeutics in 3D breast cancer spheroids.

Andrea Escobar Martinez, Pranav Joshi, Emily Carney, Faye Fouladgar, Robert Powell, Manav Goud Vanga, Victoria Gnenema, Sarah Hripko, Moo-Yeal Lee, Neda Habibi

Abstract read
In one paragraph

Article in Bioengineering & translational medicine, 2026. 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

10 authors.

Andrea Escobar MartinezBiomedical Engineering Department University of North Texas Denton Texas USA.
Pranav JoshiBioprinting Laboratories Inc. Dallas Texas USA.
Emily CarneyBiomedical Engineering Department University of North Texas Denton Texas USA.
Faye FouladgarBiomedical Engineering Department University of North Texas Denton Texas USA.
Robert PowellBiomedical Engineering Department University of North Texas Denton Texas USA.
Manav Goud VangaBioprinting Laboratories Inc. Dallas Texas USA.
Victoria GnenemaBiomedical Engineering Department University of North Texas Denton Texas USA.
Sarah HripkoBiomedical Engineering Department University of North Texas Denton Texas USA.
Moo-Yeal LeeBiomedical Engineering Department University of North Texas Denton Texas USA.
Neda HabibiBiomedical Engineering Department University of North Texas Denton Texas USA.ORCID https://orcid.org/0000-0002-0120-4887

Funding

New approach based on enzyme stimulating of peptides for targeting drug resistance breast cancersR16GM150848 · NIGMS · UNIVERSITY OF NORTH TEXAS · PI HABIBI, NEDA · 2023 to 2025
$645k
NIGMS NIH HHS R16 GM150848
6 · The paper itself

Abstract

Enzyme-induced self-assembling peptides (EISAPs) are a promising class of enzyme-activated anticancer therapeutics, yet their translational screening is limited by the lack of 3D tumor models that effectively capture drug penetration, self-assembly dynamics, and treatment response. To address this need, we developed a pillar-perfusion 3D breast cancer spheroid platform to screen a six-peptide panel-P1 (Fmoc-FF-pTyr), P2 (Fmoc-FF-pThr), P3 (RGD-FF-pTyr), P4 (NBD-FF-pTyr), P5 (Nap-FF-pTyr), and P6 (Nap-FF-pThr)-under static and dynamic flow. Hydrogel optimization identified a 2% gelatin/1% alginate matrix enabling

Indexed as

breast cancerpeptidepillar‐plateself‐assemblingspheroid

Identifiers

PMID42394905
PMCPMC13327601

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.