Modeling the mitotic regulatory network identifies highly efficient anti-cancer drug combinations

2015 ◽  
Vol 11 (2) ◽  
pp. 497-505 ◽  
Author(s):  
Yiran Wu ◽  
Xiaolong Zhuo ◽  
Ziwei Dai ◽  
Xiao Guo ◽  
Yao Wang ◽  
...  
Keyword(s):  
Mammalian Cell ◽  
Network Model ◽  
Regulatory Network ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Aurora B ◽  
Highly Efficient ◽  
Anti Cancer ◽  
Microtubule Formation

A mammalian cell mitotic network model was built and two effective anti-cancer drug combinations, Aurora B/PLK1 and microtubule formation/PLK1, were identified.

scholarly journals In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Remzi Celebi ◽  
Oliver Bear Don’t Walk ◽  
Rajiv Movva ◽  
Semih Alpsoy ◽  
Michel Dumontier
Keyword(s):  
In Silico ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Omics Data ◽  
In Silico Prediction ◽  
Anti Cancer

Growth and Chemosensitivity of Gastric Adenocarcinoma and Non-Malignant Cell Lines in Response to Novel Anti-Cancer Drug Combinations

Chemotherapy ◽  
2014 ◽  
Vol 60 (5-6) ◽  
pp. 346-352 ◽  
Author(s):  
Jürgen Weinreich ◽  
Rami Archid ◽  
Khaled Bajaeifer ◽  
Anita Hack ◽  
Alfred Königsrainer ◽  
...  
Keyword(s):  
Gastric Adenocarcinoma ◽  
Cell Lines ◽  
Malignant Cell ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Anti Cancer

scholarly journals SynToxProfiler: an approach for top drug combination selection based on integrated profiling of synergy, toxicity and efficacy

2019 ◽  
Author(s):  
Aleksandr Ianevski ◽  
Alexander Kononov ◽  
Sanna Timonen ◽  
Tero Aittokallio ◽  
Anil K Giri
Keyword(s):  
Drug Combination ◽  
Ebola Virus ◽  
Clinical Success ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Integrated Analysis ◽  
Web Based ◽  
Prolymphocytic Leukemia ◽  
Anti Cancer ◽  
Healthy Control

AbstractDrug combinations are becoming a standard treatment of many complex diseases due to their capability to overcome resistance to monotherapy. Currently, in the preclinical drug combination screening, the top hits for further study are often selected based on synergy alone, without considering the combination efficacy and toxicity effects, even though these are critical determinants for the clinical success of a therapy. To promote the prioritization of drug combinations based on integrated analysis of synergy, efficacy and toxicity profiles, we implemented a web-based open-source tool, SynToxProfiler (Synergy-Toxicity-Profiler). When applied to 20 anti-cancer drug combinations tested both in healthy control and T-cell prolymphocytic leukemia (T-PLL) patient cells, as well as to 77 anti-viral drug pairs tested on Huh7 liver cell line with and without Ebola virus infection, SynToxProfiler was shown to prioritize synergistic drug pairs with higher selective efficacy (difference between efficacy and toxicity level) as top hits, which offers improved likelihood for clinical success.

scholarly journals Validating Aurora B as an anti-cancer drug target

2006 ◽  
Vol 119 (17) ◽  
pp. 3664-3675 ◽  
Author(s):  
Fiona Girdler ◽  
Karen E. Gascoigne ◽  
Patrick A. Eyers ◽  
Sonya Hartmuth ◽  
Claire Crafter ◽  
...  
Keyword(s):  
Drug Target ◽  
Cancer Drug ◽  
Aurora B ◽  
Anti Cancer

scholarly journals GraphSynergy: Network Inspired Deep Learning Model for Anti–Cancer Drug Combination Prediction

2021 ◽  
Author(s):  
Jiannan Yang ◽  
Zhongzhi Xu ◽  
William Wu ◽  
Qian Chu ◽  
Qingpeng Zhang
Keyword(s):  
Deep Learning ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Combination ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Ppi Network ◽  
Convolutional Network ◽  
Anti Cancer ◽  
Protein Modules

Abstract Compared with monotherapy, anti-cancer drug combination can provide effective therapy with less toxicity in cancer treatment. Recent studies found that the topological positions of protein modules related to the drugs and the cancer cell lines in the protein-protein interaction (PPI) network may reveal the effects of drugs. However, due to the size of the combinatorial space, identifying synergistic combinations of drugs from PPI network is computationally difficult. To address this challenge, we propose an end-to-end deep learning framework, namely Graph Convolutional Network for Drug Synergy (GraphSynergy), to make synergistic drug combination predictions. GraphSynergy adapts a spatial-based Graph Convolutional Network component to encode the high-order structure information of protein modules targeted by a pair of drugs, as well as the protein modules associated with a specific cancer cell line in the PPI network. The pharmacological effects of drug combinations are explicitly evaluated by their therapy and toxic scores. By introducing an attention component to automatically allocate contribution weights to the proteins, we show the ability of GraphSynergy to capture the pivotal proteins that play a part in both PPI network and biomolecular interactions between drug combinations and cancer cell lines. Experiments on two latest drug combination datasets demonstrate that GraphSynergy outperforms the state-of-the-art in predicting synergistic drug combinations. This study sheds light on using machine learning to discover effective combination therapies for cancer and other complex diseases.

scholarly journals Personalized anti-cancer drug combination prediction by an Integrated Multi-level Network

2020 ◽  
Author(s):  
Fangyoumin Feng ◽  
Zhengtao Zhang ◽  
Guohui Ding ◽  
Lijian Hui ◽  
Yixue Li ◽  
...  
Keyword(s):  
Drug Combination ◽  
Hepatocellular Cancer ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Candidate Drug ◽  
Anti Cancer ◽  
Cancer Sample ◽  
Auc Value ◽  
Multi Level ◽  
Better Than

AbstractAnti-cancer drug combination is an effective solution to improve treatment efficacy and overcome resistance. Here we propose a network-based method (DComboNet) to prioritize the candidate drug combinations. The level one model is to predict generalized anti-cancer drug combination effectiveness and level two model is to predict personalized drug combinations. By integrating drugs, genes, pathways and their associations, DComboNet achieves better performance than previous methods, with high AUC value of around 0.8. The level two model performs better than level one model by introducing cancer sample specific transcriptome data into network construction. DComboNet is further applied on finding combinable drugs for sorafenib in hepatocellular cancer, and the results are verified with literatures and cell line experiments. More importantly, three potential mechanism modes of combinations were inferred based on network analysis. In summary, DComboNet is valuable for prioritizing drug combination and the network model may facilitate the understanding of the combination mechanisms.

scholarly journals In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
M. Kashif ◽  
C. Andersson ◽  
S. Hassan ◽  
H. Karlsson ◽  
W. Senkowski ◽  
...  
Keyword(s):  
Therapeutic Index ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Anti Cancer

scholarly journals Direct reductive coupling of indoles to nitrostyrenes en route to (indol-3-yl)acetamides

RSC Advances ◽  
2016 ◽  
Vol 6 (96) ◽  
pp. 93881-93886 ◽  
Author(s):  
Alexander V. Aksenov ◽  
Nicolai A. Aksenov ◽  
Zarema V. Dzhandigova ◽  
Dmitrii A. Aksenov ◽  
Leonid G. Voskressensky ◽  
...  
Keyword(s):  
Direct Access ◽  
Cancer Drug ◽  
Reductive Coupling ◽  
One Pot ◽  
Drug Candidates ◽  
Highly Efficient ◽  
Anti Cancer

A highly efficient one-pot method for the reductive coupling of indoles to nitrostyrenes in PPA doped with PCl3 was developed. This method allows direct access to primary (indol-3-yl)acetamides, interesting as anti-cancer drug candidates.

Deactivation of anti-cancer drug letrozole to a carbinol metabolite by polymorphic cytochrome P450 2A6 in human liver microsomes

Xenobiotica ◽  
2009 ◽  
Vol 00 (00) ◽  
pp. 090901052053001-8
Author(s):  
K. Murai ◽  
H. Yamazaki ◽  
K. Nakagawa ◽  
R. Kawai ◽  
T. Kamataki
Keyword(s):  
Cytochrome P450 ◽  
Human Liver ◽  
Liver Microsomes ◽  
Human Liver Microsomes ◽  
Cancer Drug ◽  
Anti Cancer ◽  
Cytochrome P450 2A6
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