ArticleScientific reports2024
In-vivo studies of targeted and localized cancer drug release from microporous poly-di-methyl-siloxane (PDMS) devices for the treatment of triple negative breast cancer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
- Engineering Functional Biomaterials for Targeted and Localized Cancer Drug Delivery: A Structure-Property-Performance Design Perspective.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Targeting Molecular Pathways in Breast Cancer Using Plant-Derived Bioactive Compounds: A Comprehensive Review.Journal of experimental pharmacology · 2025Review
- Luteinizing Hormone-Releasing Hormone (LHRH)-Conjugated Cancer Drug Delivery from Magnetite Nanoparticle-Modified Microporous Poly-Di-Methyl-Siloxane (PDMS) Systems for the Targeted Treatment of Triple Negative Breast Cancer Cells.Journal of functional biomaterials · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 6 institutions in 3 countries.
Funding
Abstract
Triple-negative breast cancer (TNBC) treatment is challenging and frequently characterized by an aggressive phenotype and low prognosis in comparison to other subtypes. This paper presents fabricated implantable drug-loaded microporous poly-di-methyl-siloxane (PDMS) devices for the delivery of targeted therapeutic agents [Luteinizing Hormone-Releasing Hormone conjugated paclitaxel (PTX-LHRH) and Luteinizing Hormone-Releasing Hormone conjugated prodigiosin (PG-LHRH)] for the treatment and possible prevention of triple-negative cancer recurrence. In vitro assessment using the Alamar blue assay demonstrated a significant reduction (p < 0.05) in percentage of cell growth in a time-dependent manner in the groups treated with PG, PG-LHRH, PTX, and PTX-LHRH. Subcutaneous triple-negative xenograft breast tumors were then induced in athymic female nude mice that were four weeks old. Two weeks later, the tumors were surgically but partially removed, and the device implanted. Mice were observed for tumor regrowth and organ toxicity. The animal study revealed that there was no tumor regrowth, six weeks post-treatment, when the LHRH targeted drugs (LHRH-PTX and LHRH-PGS) were used for the treatment. The possible cytotoxic effects of the released drugs on the liver, kidney, and lung are assessed using quantitative biochemical assay from blood samples of the treatment groups. Ex vivo histopathological results from organ tissues showed that the targeted cancer drugs released from the implantable drug-loaded device did not induce any adverse effect on the liver, kidneys, or lungs, based on the results of qualitative toxicity studies. The implications of the results are discussed for the targeted and localized treatment of triple negative breast cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.