Identification of a Novel Oral Small-Molecule JAK1/2 Inhibitor and Its Potent Synergistic Drug Combinations Against Cervical Cancer

2019 ◽  
Author(s):  
Shiqi She ◽  
Yang Zhao ◽  
Bo Kang ◽  
Cheng Chen ◽  
Xinyu Chen ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Small Molecule ◽  
Drug Combinations ◽  
Synergistic Drug Combinations

scholarly journals Biomolecular Network-Based Synergistic Drug Combination Discovery

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangyi Li ◽  
Guangrong Qin ◽  
Qingmin Yang ◽  
Lanming Chen ◽  
Lu Xie
Keyword(s):  
Drug Combination ◽  
Complex Disease ◽  
Recent Decade ◽  
Drug Combinations ◽  
Network Medicine ◽  
Disease Therapy ◽  
Biomolecular Network ◽  
Disease Related Genes ◽  
Disease Specific ◽  
Synergistic Drug Combinations

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

Abstract 1053: A large cellular screen charting the landscape of synergistic drug combinations in lung cancer

2021 ◽  
Author(s):  
Nishanth Ulhas Nair ◽  
Adam Friedman ◽  
Patricia Greninger ◽  
Avinash D. Sahu ◽  
Ellen Murchie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Drug Combinations ◽  
Synergistic Drug Combinations

scholarly journals Synergistic Drug Combinations with a CDK4/6 Inhibitor in T-cell Acute Lymphoblastic Leukemia

2016 ◽  
Vol 23 (4) ◽  
pp. 1012-1024 ◽  
Author(s):  
Yana Pikman ◽  
Gabriela Alexe ◽  
Giovanni Roti ◽  
Amy Saur Conway ◽  
Andrew Furman ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
T Cell ◽  
Lymphoblastic Leukemia ◽  
Drug Combinations ◽  
Cell Acute Lymphoblastic Leukemia ◽  
Synergistic Drug Combinations

DDRE-28. IDENTIFICATION OF SYNERGISTIC DRUG COMBINATIONS FOR THE THERAPY OF IDHmut GLIOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi80-vi80
Author(s):  
Rolf Warta ◽  
Florian Stammler ◽  
Andreas Unterberg ◽  
Christel Herold-Mende
Keyword(s):  
Single Agent ◽  
Therapy Response ◽  
Drug Combinations ◽  
Drug Concentrations ◽  
Brain Barrier Permeability ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Biological Differences ◽  
Synergistic Drug Combinations

Abstract OBJECTIVE Isocitrate Dehydrogenase (IDH) mutation in glioma results in a multitude of biological differences with consequences for survival and therapy response. Therefore, IDH mutated (IDHmut) and wildtype (IDHwt) tumors are regarded as separate entities with the need for adjusted therapy like the combination of procarbazine, CCNU and vincristine (PCV). However, as vincristine has often severe side effects like neuropathy new effective therapy options are required. Therefore, we searched for combinations of FDA-approved drugs which effectively inhibit the growth of IDHmut cells in vitro. METHODS We tested different drug combinations of a drug library consisting of 146 FDA-approved drugs on two established IDHmut GSC lines. Based on a previous single agent drug screen, six drugs were selected (Idarubicin, Ixazumib, Ponatinib, Neratinib, Romidepsin) to be combined with all 146 drugs of the library. Cell viability was assessed by the CellTiterGlo 3D assay (Promega) in 96 well plates, while Caspase-Glo 3/7 3D assay was used to measure induction of apoptosis. RESULTS Out of 1460 drug combinations tested altogether 21 synergistic drug combinations could be identified and validated. The combination with the highest blood-brain-barrier permeability score was further investigated. Finally, drug-concentrations elucidating the highest synergistic effect on proliferation was further studied in a 8-point dose-response matrix followed by validation in additional four IDHmut GSC lines. CONCLUSION This work can lay the foundation for future improvements of the therapy of patients suffering from LGGs.

scholarly journals Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction

2019 ◽  
Vol 35 (19) ◽  
pp. 3709-3717 ◽  
Author(s):  
Lei Huang ◽  
David Brunell ◽  
Clifford Stephan ◽  
James Mancuso ◽  
Xiaohui Yu ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Cancer Patients ◽  
Functional Module ◽  
B Cell Lymphoma ◽  
Drug Combinations ◽  
Supplementary Information ◽  
Gene Copy ◽  
Omics Data ◽  
Multiple Cancer ◽  
Synergistic Drug Combinations

Abstract Motivation Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data. Results This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans. Availability and implementation DrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction. Supplementary information Supplementary data are available at Bioinformatics online.

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