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 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.

The Design and Synthesis of Novel Phenothiazine Derivatives as Potential Cytotoxic Agents

2019 ◽  
Vol 17 (1) ◽  
pp. 57-67
Author(s):  
Yepeng Luan ◽  
Jinyi Liu ◽  
Jianjun Gao ◽  
Jinhua Wang
Keyword(s):  
Cell Lines ◽  
High Efficiency ◽  
Human Cancer ◽  
Human Tumor ◽  
Cytotoxic Agents ◽  
Cancer Drug ◽  
Human Cancer Cell Lines ◽  
Incidence And Mortality ◽  
Anti Cancer

Background: Cancer incidence and mortality have been increasing and cancer is still the leading cause of death all over the world. Despite the enormous progress in cancer treatment, many patients died of ineffective chemotherapy and drug resistance. Therefore, the design and development of anti-cancer drugs with high efficiency and low toxicity is still one of the most challenging tasks. Tricyclic heterocycles, such as phenothiazine, are always important sources of scaffolds for anti-cancer drug discovery. Methods: In this work, ten new urea-containing derivatives of phenothiazine coupled with different kinds of amine motifs at the endpoint through a three carbon long spacer were designed and synthesized. The structures of the synthesized compounds were elucidated and confirmed by 1H NMR and HRMS. All the synthesized compounds were tested for their antitumor activity in vitro against the proliferation of PC-3 cells, and the compounds with best potency entered further cytotoxicity evaluations against other 22 human tumor cell lines. Mechanism was also studied. Results: From all data, it showed that among all 10 target compounds, TTi-2 showed the best effect in inhibiting the proliferation of 23 human cancer cell lines while TTi-2 without obvious inhibitory effect on normal cell. Furthermore, our results also showed that TTi-2 could inhibit migration, invasion and colony formation of MDA-MB-231 cells. Finally, TTi-2 can induce arrest of cell cycle at G0/G1 phase and cell apoptosis by activating the caspase 3 activity. Conclusion: All these results suggested that TTi-2 might be used as a promising lead compound for anticancer drug development.

scholarly journals Furanoic Lipid F-6, A Novel Anti-Cancer Compound that Kills Cancer Cells by Suppressing Proliferation and Inducing Apoptosis

Cancers ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 960 ◽  
Author(s):  
Jassim M. Al-Hassan ◽  
Yuan Fang Liu ◽  
Meraj A. Khan ◽  
Peiying Yang ◽  
Rui Guan ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Neutrophil Extracellular Trap ◽  
Cancer Drug ◽  
Drug Candidate ◽  
Cell Recovery ◽  
Network Analyses ◽  
Anti Cancer

Identifying novel anti-cancer drugs is important for devising better cancer treatment options. In a series of studies designed to identify novel therapeutic compounds, we recently showed that a C-20 fatty acid (12,15-epoxy-13,14-dimethyleicosa-12,14-dienoic acid, a furanoic acid or F-6) present in the lipid fraction of the secretions of the Arabian Gulf catfish skin (Arius bilineatus Val.; AGCS) robustly induces neutrophil extracellular trap formation. Here, we demonstrate that a lipid mix (Ft-3) extracted from AGCS and F-6, a component of Ft-3, dose dependently kill two cancer cell lines (leukemic K-562 and breast MDA MB-231). Pure F-6 is approximately 3.5 to 16 times more effective than Ft-3 in killing these cancer cells, respectively. Multiplex assays and network analyses show that F-6 promotes the activation of MAPKs such as Erk, JNK, and p38, and specifically suppresses JNK-mediated c-Jun activation necessary for AP-1-mediated cell survival pathways. In both cell lines, F-6 suppresses PI3K-Akt-mTOR pathway specific proteins, indicating that cell proliferation and Akt-mediated protection of mitochondrial stability are compromised by this treatment. Western blot analyses of cleaved caspase 3 (cCasp3) and poly ADP ribose polymerase (PARP) confirmed that F-6 dose-dependently induced apoptosis in both of these cell lines. In 14-day cell recovery experiments, cells treated with increasing doses of F-6 and Ft-3 fail to recover after subsequent drug washout. In summary, this study demonstrates that C-20 furanoic acid F-6, suppresses cancer cell proliferation and promotes apoptotic cell death in leukemic and breast cancer cells, and prevents cell recovery. Therefore, F-6 is a potential anti-cancer drug candidate.

Screening of 129 FDA approved anti-cancer drugs in colorectal cancer cell lines resistant to oxaliplatin or irinotecan (SN38).

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 642-642 ◽  
Author(s):  
Jan Stenvang ◽  
Christine Hjorth Andreassen ◽  
Nils Brünner
Keyword(s):  
Colorectal Cancer ◽  
Drug Resistance ◽  
Cell Viability ◽  
Cell Line ◽  
Cell Lines ◽  
Resistant Cell ◽  
Cancer Drug ◽  
Cancer Drugs ◽  
Drug Candidates ◽  
Anti Cancer

642 Background: In metastatic colorectal cancer (mCRC) only 3 cytotoxic drugs (oxaliplatin, irinotecan and fluorouracil (5-FU)) are approved and the first and second line response rates are about 50% and 10-15%, respectively. Thus, new treatment options are needed. Novel anti-cancer drug candidates are primarily tested in an environment of drug resistance and the majority of novel drug candidates fail during clinical development. Therefore, “repurposing” of drugs has emerged as a promising strategy to apply established drugs in novel indications. The aim of this project was to screen established anti-cancer drugs to identify candidates for testing in mCRC patients relapsing on standard therapy. Methods: We applied 3 parental (drug sensitive) CRC cell lines (HCT116, HT29 and LoVo) and for each cell line also an oxaliplatin and irinotecan (SN38) resistant cell line. We obtained 129 FDA approved anti-cancer drugs from the Developmental Therapeutics Program (DTP) at the National Cancer Institute (NCI) ( https://dtp.cancer.gov/ ). The parental HT29 cell line and the drug resistant sublines HT29-SN38 and HT29-OXPT were exposed to 3 concentrations of each of the anti-cancer drugs. The effect on cell viability was analyzed by MTT assays. Nine of the drugs were analyzed for effect in the LoVo and HCT116 and the SN38- and oxaliplatin-resistant derived cell lines. Results: None of the drugs caused evident differential response between the resistant and sensitive cells or between the SN38 and oxaliplatin resistant cells. The screening confirmed the resistance as the cells displayed resistance to drugs in the same class as the one they were made resistant to. Of the drugs, 45 decreased cell viability in the HT29 parental and oxaliplatin- or SN-38 resistant cell lines. Nine drugs were tested in all nine CRC cell lines and eight decrease cell viability in the nine cell lines. These included drugs in different classes such as epigenetic drugs, antibiotics, mitotic inhibitors and targeted therapies. Conclusions: This study revealed several possible new “repurposing” drugs for CRC therapy, by showing that 45 FDA-approved anti-cancer drugs decrease cell viability in CRC cell lines with acquired drug resistance.

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

scholarly journals Selective colony area method for heterogeneous patient-derived tumor cell lines in anti-cancer drug screening system

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0215080 ◽  
Author(s):  
Jang Ho Cho ◽  
Ju-Sun Kim ◽  
Seung Tae Kim ◽  
Jung Yong Hong ◽  
Joon Oh Park ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Cell Lines ◽  
Drug Screening ◽  
Cancer Drug ◽  
Screening System ◽  
Tumor Cell Lines ◽  
Area Method ◽  
Anti Cancer ◽  
Colony Area

scholarly journals Design and Synthesis of Flavonoidal Ethers and Their Anti-Cancer Activity In Vitro

Molecules ◽  
2019 ◽  
Vol 24 (9) ◽  
pp. 1749 ◽  
Author(s):  
Lu Jin ◽  
Meng-Ling Wang ◽  
Yao Lv ◽  
Xue-Yi Zeng ◽  
Chao Chen ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Cancer Drug ◽  
Hydroxyl Groups ◽  
Drug Candidates ◽  
Design And Synthesis ◽  
Anti Cancer ◽  
Clinical Drugs

Flavonoids are well-characterized polyphenolic compounds with pharmacological and therapeutic activities. However, most flavonoids have not been developed into clinical drugs, due to poor bioavailability. Herein, we report a strategy to increase the drugability of flavonoids by constructing C(sp2)-O bonds and stereo- as well as regioselective alkenylation of hydroxyl groups of flavonoids with ethyl-2,3-butadienoate allenes. Twenty-three modified flavonoid derivatives were designed, synthesized, and evaluated for their anti-cancer activities. The results showed that compounds 4b, 4c, 4e, 5e, and 6b exhibited better in vitro inhibitory activity against several cancer cell lines than their precursors. Preliminary structure–activity relationship studies indicated that, in most of the cancer cell lines evaluated, the substitution on position 7 was essential for increasing cytotoxicity. The results of this study might facilitate the preparation or late-stage modification of complex flavonoids as anti-cancer drug candidates.

scholarly journals Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance

2011 ◽  
Vol 108 (46) ◽  
pp. 18708-18713 ◽  
Author(s):  
J.-P. Gillet ◽  
A. M. Calcagno ◽  
S. Varma ◽  
M. Marino ◽  
L. J. Green ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Cancer Drug ◽  
Cancer Drug Resistance ◽  
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.

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