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 ◽  
...  
2021 ◽  
Author(s):  
Jiannan Yang ◽  
Zhongzhi Xu ◽  
William Wu ◽  
Qian Chu ◽  
Qingpeng Zhang

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.


2019 ◽  
Vol 17 (1) ◽  
pp. 57-67
Author(s):  
Yepeng Luan ◽  
Jinyi Liu ◽  
Jianjun Gao ◽  
Jinhua Wang

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.


Cancers ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 960 ◽  
Author(s):  
Jassim M. Al-Hassan ◽  
Yuan Fang Liu ◽  
Meraj A. Khan ◽  
Peiying Yang ◽  
Rui Guan ◽  
...  

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.


2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 642-642 ◽  
Author(s):  
Jan Stenvang ◽  
Christine Hjorth Andreassen ◽  
Nils Brünner

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.


2015 ◽  
Vol 11 (2) ◽  
pp. 497-505 ◽  
Author(s):  
Yiran Wu ◽  
Xiaolong Zhuo ◽  
Ziwei Dai ◽  
Xiao Guo ◽  
Yao Wang ◽  
...  

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


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Remzi Celebi ◽  
Oliver Bear Don’t Walk ◽  
Rajiv Movva ◽  
Semih Alpsoy ◽  
Michel Dumontier

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 ◽  
...  

Molecules ◽  
2019 ◽  
Vol 24 (9) ◽  
pp. 1749 ◽  
Author(s):  
Lu Jin ◽  
Meng-Ling Wang ◽  
Yao Lv ◽  
Xue-Yi Zeng ◽  
Chao Chen ◽  
...  

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.


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

2019 ◽  
Author(s):  
Aleksandr Ianevski ◽  
Alexander Kononov ◽  
Sanna Timonen ◽  
Tero Aittokallio ◽  
Anil K Giri

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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