ArticlePharmaceuticals (Basel, Switzerland)2022
Bruceine D Identified as a Drug Candidate against Breast Cancer by a Novel Drug Selection Pipeline and Cell Viability Assay.
Article in Pharmaceuticals (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Crosstalk between S-nitrosylation and glycation defines a metabolic vulnerability in liver and renal cancers.Nature communications · 2026Article
- Sono-Controllable Janus Hydrogel Platform for Sequential Tumor Eradication and Bone Regeneration in Metastatic Breast Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Bruceine D inhibits hepatocellular carcinoma by regulating the STAT3 signaling pathway through HSP70.European journal of medical research · 2025Article
- Bruceine A Inhibits Cell Proliferation by Targeting the USP13/PARP1 Signalling Pathway in Multiple Myeloma.Basic & clinical pharmacology & toxicology · 2025Article
- Bruceine D and Narclasine inhibit the proliferation of breast cancer cells and the prediction of potential drug targets.PloS one · 2024Article
- Effect of protease inhibitors on the intraerythrocytic development of Babesia microti and Babesia duncani, the causative agents of human babesiosis.The Journal of eukaryotic microbiologyArticle
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The multi-target effects of natural products allow us to fight complex diseases like cancer on multiple fronts. Unlike docking techniques, network-based approaches such as genome-scale metabolic modelling can capture multi-target effects. However, the incompleteness of natural product target information reduces the prediction accuracy of in silico gene knockout strategies. Here, we present a drug selection workflow based on context-specific genome-scale metabolic models, built from the expression data of cancer cells treated with natural products, to predict cell viability. The workflow comprises four steps: first, in silico single-drug and drug combination predictions; second, the assessment of the effects of natural products on cancer metabolism via the computation of a dissimilarity score between the treated and control models; third, the identification of natural products with similar effects to the approved drugs; and fourth, the identification of drugs with the predicted effects in pathways of interest, such as the androgen and estrogen pathway. Out of the initial 101 natural products, nine candidates were tested in a 2D cell viability assay. Bruceine D, emodin, and scutellarein showed a dose-dependent inhibition of MCF-7 and Hs 578T cell proliferation with IC
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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.