A network paradigm predicts drug synergistic effects using downstream protein–protein interactions
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 fi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-11-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12861 |
| _version_ | 1828193678732034048 |
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| author | Jennifer L. Wilson Ethan Steinberg Rebecca Racz Russ B. Altman Nigam Shah Kevin Grimes |
| author_facet | Jennifer L. Wilson Ethan Steinberg Rebecca Racz Russ B. Altman Nigam Shah Kevin Grimes |
| author_sort | Jennifer L. Wilson |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T09:11:23Z |
| format | Article |
| id | doaj.art-40b2b23cd4074e4bb96082a9c2d0926e |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2024-04-12T09:11:23Z |
| publishDate | 2022-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-40b2b23cd4074e4bb96082a9c2d0926e2022-12-22T03:38:57ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062022-11-0111111527153810.1002/psp4.12861A network paradigm predicts drug synergistic effects using downstream protein–protein interactionsJennifer L. Wilson0Ethan Steinberg1Rebecca Racz2Russ B. Altman3Nigam Shah4Kevin Grimes5Department of Bioengineering University of California Los Angeles Los Angeles California USACenter for Biomedical Informatics Research Stanford University Palo Alto California USADivision of Applied Regulatory Science US Food and Drug Administration Silver Spring Maryland USADepartment of Bioengineering Stanford University Palo Alto California USACenter for Biomedical Informatics Research Stanford University Palo Alto California USADepartment of Chemical and Systems Biology Stanford University Palo Alto California USAAbstract 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.https://doi.org/10.1002/psp4.12861 |
| spellingShingle | Jennifer L. Wilson Ethan Steinberg Rebecca Racz Russ B. Altman Nigam Shah Kevin Grimes A network paradigm predicts drug synergistic effects using downstream protein–protein interactions CPT: Pharmacometrics & Systems Pharmacology |
| title | A network paradigm predicts drug synergistic effects using downstream protein–protein interactions |
| title_full | A network paradigm predicts drug synergistic effects using downstream protein–protein interactions |
| title_fullStr | A network paradigm predicts drug synergistic effects using downstream protein–protein interactions |
| title_full_unstemmed | A network paradigm predicts drug synergistic effects using downstream protein–protein interactions |
| title_short | A network paradigm predicts drug synergistic effects using downstream protein–protein interactions |
| title_sort | network paradigm predicts drug synergistic effects using downstream protein protein interactions |
| url | https://doi.org/10.1002/psp4.12861 |
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