ArticleCPT: pharmacometrics & systems pharmacology2021
Network model-based screen for FDA-approved drugs affecting cardiac fibrosis.
Article in CPT: pharmacometrics & systems pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
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Who cites it
18 citing papers in PubMed, 27 citations in OpenAlex.
- Computational modeling identifies protective mechanisms of estrogen and testosterone against atrial fibrosis.American journal of physiology. Heart and circulatory physiology · 2026Article
- Genetic drivers of congenital cardiac fibrosis.Communications biology · 2026Review
- Therapeutic Targets for Myocardial Fibrosis: A Comprehensive Review of Current and Emerging Approaches.Cardiovascular & hematological disorders drug targets · 2026Review
- AI-based Predictive Signaling Pathway Profiling in Cardiac Fibrosis Suggests a Novel Combinatorial Treatment Strategy.bioRxiv : the preprint server for biology · 2025Article
- Contributions of mechanical loading and hormonal changes to eccentric hypertrophy during volume overload: A Bayesian analysis using logic-based network models.PLoS computational biology · 2025Article
- Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Multiscale computational model predicts how environmental changes and treatments affect microvascular remodeling in fibrotic disease.PNAS nexus · 2025Article
- A Graphical Approach to Visualize and Interpret Biochemically Coupled Biomechanical Models.Journal of biomechanical engineering · 2024Article
- Multiscale computational model predicts how environmental changes and drug treatments affect microvascular remodeling in fibrotic disease.bioRxiv : the preprint server for biology · 2024Article
- Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Virtual drug screen reveals context-dependent inhibition of cardiomyocyte hypertrophy.British journal of pharmacology · 2023Article
- Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.bioRxiv : the preprint server for biology · 2023Article
- Computational screen for sex-specific drug effects in a cardiac fibroblast signaling network model.Scientific reports · 2023Article
- New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements.Scientific reports · 2023Article
- Review
- Diversity of cells and signals in the cardiovascular system.The Journal of physiology · 2023Article
- Systems biology of angiogenesis signaling: Computational models and omics.WIREs mechanisms of disease · 2022Review
- Sacubitril/Valsartan contributes to improving the diabetic kidney disease and regulating the gut microbiota in mice.Frontiers in endocrinology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.
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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.