Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks

A major challenge in developing anticancer therapies is determining the efficacies of drugs and their combinations in physiologically relevant microenvironments. We describe here our application of “constrained fuzzy logic” (CFL) ensemble modeling of the intracellular signaling network for predictin...

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Veröffentlicht in:CPT: pharmacometrics and systems pharmacology Jg. 5; H. 10; S. 544 - 553
Hauptverfasser: Morris, MK, Clarke, DC, Osimiri, LC, Lauffenburger, DA
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States John Wiley & Sons, Inc 01.10.2016
John Wiley and Sons Inc
Schlagworte:
Cancer therapies
Carcinoma, Hepatocellular - drug therapy
Carcinoma, Hepatocellular - metabolism
Computer Simulation
Cytokines
Cytokines - metabolism
Drug Combinations
Drug dosages
Drug Therapy, Combination
Experiments
Fuzzy Logic
Growth factors
Hep G2 Cells
Humans
Kinases
Knowledge
Liver cancer
Liver Neoplasms - drug therapy
Liver Neoplasms - metabolism
Methods
Models, Theoretical
Original
Phosphorylation - drug effects
Physiology
Proteins
R&D
Research & development
Signal Transduction - drug effects
Software
Systems Analysis
Transcription Factors - metabolism
Tumor Microenvironment - drug effects
United States > US
ISSN:2163-8306, 2163-8306
Online-Zugang:Volltext
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Zusammenfassung:A major challenge in developing anticancer therapies is determining the efficacies of drugs and their combinations in physiologically relevant microenvironments. We describe here our application of “constrained fuzzy logic” (CFL) ensemble modeling of the intracellular signaling network for predicting inhibitor treatments that reduce the phospho‐levels of key transcription factors downstream of growth factors and inflammatory cytokines representative of hepatocellular carcinoma (HCC) microenvironments. We observed that the CFL models successfully predicted the effects of several kinase inhibitor combinations. Furthermore, the ensemble predictions revealed ambiguous predictions that could be traced to a specific structural feature of these models, which we resolved with dedicated experiments, finding that IL‐1α activates downstream signals through TAK1 and not MEKK1 in HepG2 cells. We conclude that CFL‐Q2LM (Querying Quantitative Logic Models) is a promising approach for predicting effective anticancer drug combinations in cancer‐relevant microenvironments.
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ISSN:2163-8306
2163-8306
DOI:10.1002/psp4.12104