ArticleNPJ systems biology and applications2019
Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.
Article in NPJ systems biology and applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Integrating AI, mechanistic modelling and network approaches in systems biology for translational research.NPJ systems biology and applications · 2026Article
- ZAK kinase at the crossroads of cellular stress: functional plasticity, human pathobiology, and precision therapeutics.Frontiers in cell and developmental biology · 2026Review
- Multi agent large language models for biomedical hypothesis generation in drug combination discovery.iScience · 2025Article
- Localizing Synergies of Hidden Factors in Complex Systems: Resting Brain Networks and HeLa GeneExpression Profile as Case Studies.Entropy (Basel, Switzerland) · 2025Article
- AI-Driven Polypharmacology in Small-Molecule Drug Discovery.International journal of molecular sciences · 2025Review
- Dysregulated ribosome quality control in human diseases.The FEBS journal · 2025Review
- Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers.Endocrine oncology (Bristol, England) · 2025Review
- Simulate Scientific Reasoning with Multiple Large Language Models: An Application to Alzheimer's Disease Combinatorial Therapy.medRxiv : the preprint server for health sciences · 2024Article
- CRISPR/Cas9-mediated silencing of CD44: unveiling the role of hyaluronic acid-mediated interactions in cancer drug resistance.Naunyn-Schmiedeberg's archives of pharmacology · 2024Review
- Predicting anti-cancer drug combination responses with a temporal cell state network model.PLoS computational biology · 2023Article
- Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.Frontiers in systems biology · 2023Article
- Nanomaterial-assisted CRISPR gene-engineering - A hallmark for triple-negative breast cancer therapeutics advancement.Materials today. Bio · 2022Review
- Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments.Briefings in bioinformatics · 2022Article
- Network-based modeling of herb combinations in traditional Chinese medicine.Briefings in bioinformatics · 2021Article
- DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal.Nucleic acids research · 2021Article
- Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside.Clinical epigenetics · 2021Review
- Anticancer drug synergy prediction in understudied tissues using transfer learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- In Vitro Bioassay-Guided Identification of Anticancer Properties fromPharmaceuticals (Basel, Switzerland) · 2020Article
- Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?Biomolecules · 2020Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.
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