ArticleCPT: pharmacometrics & systems pharmacology2022
A network paradigm predicts drug synergistic effects using downstream protein-protein interactions.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Curated and Structure-Based Drug-Target Interactions Improve Underprediction of Drug Side Effects in Network Models.Journal of chemical information and modeling · 2026Article
- Uncovering New Therapeutic Targets for Amyotrophic Lateral Sclerosis and Neurological Diseases Using Real-World Data.Clinical pharmacology and therapeutics · 2025Article
- Across preclinical and clinical platforms, approved and investigational psychiatric drugs share pathways and associate with similar molecular functions.Frontiers in drug discovery · 2025Article
- Delineating CYP2C19-mediated Interactions: Network Pharmacology Investigation of Ilaprazole and ClopidogrelCurrent drug discovery technologies · 2025Article
- Synergistic Effects of Korean Mistletoe and Apple Peel Extracts on Muscle Strength and Endurance.Nutrients · 2024Article
- Preclinical side effect prediction through pathway engineering of protein interaction network models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Identification of Molecular Markers Associated with Prostate Cancer Subtypes: An Integrative Bioinformatics Approach.Biomolecules · 2024Article
- A network paradigm predicts drug synergistic effects using downstream protein-protein interactions.CPT: pharmacometrics & systems pharmacology · 2022Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In some cases, drug combinations affect adverse outcome phenotypes by binding the same protein; however, drug-binding proteins are associated through protein-protein interaction (PPI) networks within the cell, suggesting that drug phenotypes may result from long-range network effects. We first used PPI network analysis to classify drugs based on proteins downstream of their targets and next predicted drug combination effects where drugs shared network proteins but had distinct binding proteins (e.g., targets, enzymes, or transporters). By classifying drugs using their downstream proteins, we had an 80.7% sensitivity for predicting rare drug combination effects documented in gold-standard datasets. We further measured the effect of predicted drug combinations on adverse outcome phenotypes using novel observational studies in the electronic health record. We tested predictions for 60 network-drug classes on seven adverse outcomes and measured changes in clinical outcomes for predicted combinations. These results demonstrate a novel paradigm for anticipating drug synergistic effects using proteins downstream of drug targets.
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