scholarly journals A REVIEW ON DRUG NIMBUKA AS ANTIBACTERIAL, ANTICANCEROUS AND ADJUNCTIVE FOR CHEMOTHERAPY

2020 ◽  
pp. 333-334
Author(s):  
Dr. Naveena K S ◽  
Dr. Shrinath M. Vaidya
Keyword(s):  
Mortality Rate ◽  
Key Words ◽  
Cell Line ◽  
Vital Statistics ◽  
Cancer Drug ◽  
Citrus Limon ◽  
Abnormal Cell ◽  
Anti Cancer ◽  
Citrus Medica

In present scenario Cancer contributes to highest mortality rate. According to the vital statistics of 2016, around 14 lakh deaths have occurred due to Cancer. Cancer is conditions were new growth and division of abnormal cell is going to happen. Nimbuka is the drug explained in Ayurveda by Acharya Bhavaprakasha as Krimigna and Prakruti-sthapaka. Nimbuka is anti-cancerous and adjunctive for chemotherapy and Rasayana (as per the information obtained from cell line studies). Nimbuka belongs to Rutaceae family which itself is proved having the anti-cancer drug property. KEY WORDS: Nimbuka, Anticancerous, Citrus medica, Citrus limon etc

Thymoquinone Effects on Cell Viability, Apoptosis and VEGF-A Gene Expression Level in AGS(CRL-1739) Cell Line

2019 ◽  
Vol 19 (6) ◽  
pp. 820-826 ◽  
Author(s):  
Mohsen Rashid ◽  
Forough Sanjarin ◽  
Farzaneh Sabouni
Keyword(s):  
Gene Expression ◽  
Cell Proliferation ◽  
Cell Line ◽  
Real Time ◽  
Mtt Assay ◽  
Nigella Sativa ◽  
Angiogenic Factor ◽  
Cancer Drug ◽  
Anti Cancer ◽  
Ags Cell Line

Background: Cancer is one of the most fatal diseases across the world and it was reported that 90% of cancer fatality depends on its angiogenesis potential. Black seed or Nigella sativa L. is a medicinal plant native to southwest Asia. N. sativa has been used for medicinal purposes for centuries and predominantly has bioactive components like Thymoquinone, which is used as a candidate for anti-cancer and anti-angiogenesis drugs. Methods: Callus was induced from leaf tissue, after that alcoholic extracts were prepared from three-month-old calluses. Thymoquinone content was measured by HPLC methods. AGS cell line was cultured and treated with standard Thymoquinone and extracts from callus. Then, cell proliferation, expression of angiogenic factor (VEGF-A gene), and apoptosis test were done by MTT assay, real-time PCR and Annexin-v kit, respectively. Results: HPLC found the maximum amount of Thymoquinone in the extract of leaf calluses, which were grown in the dark. MTT assay revealed that particular doses of extracts reduced cell proliferation. Real-time and Fluorescence- Activated Cell Sorting (FACS) results demonstrated that standard Thymoquinone and callus extracts down-regulated the VEGF-A gene expression, and all three induced apoptosis in the AGS cell line. Conclusion: It has been shown that TQ has pro-apoptotic and anti-metastatic effects on stomach cancer cell line, and these properties can introduce it as an anti-cancer drug in the near future.

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.

scholarly journals Characterization of a New Small Bowel Adenocarcinoma Cell Line and Screening of Anti-Cancer Drug against Small Bowel Adenocarcinoma

2015 ◽  
Vol 185 (2) ◽  
pp. 550-562 ◽  
Author(s):  
Hirobumi Suzuki ◽  
Yoshihiro Hirata ◽  
Nobumi Suzuki ◽  
Sozaburo Ihara ◽  
Kosuke Sakitani ◽  
...  
Keyword(s):  
Small Bowel ◽  
Cell Line ◽  
Cancer Drug ◽  
Small Bowel Adenocarcinoma ◽  
Adenocarcinoma Cell Line ◽  
Adenocarcinoma Cell ◽  
Anti Cancer

scholarly journals Isolation and Anti-Leukemic Characterization of Extracellular L-asparaginase From Endophytic Bacterium, Brevibacterium sp. M-R21 Isolated Glycyrrhiza glabra Root

2020 ◽  
Vol 11 (2) ◽  
pp. 9113-9125
Keyword(s):  
Cell Line ◽  
Leukemia Cell ◽  
Glycyrrhiza Glabra ◽  
Leukemia Cell Line ◽  
Leukemia Cells ◽  
Normal Lung ◽  
Cancer Drug ◽  
Lung Cells ◽  
Potential Candidate ◽  
Anti Cancer

L-Asparaginase (L-ASPase) is known as a potent anti-cancer drug against L-Asparagine-auxotroph tumor cells. In this study, an endophytic L-ASPase producing bacterium of the genus Bervibacillus from the root of Glycyrrhiza glabra was screened and characterized. After purification of the enzyme by ammonium sulfate precipitation, dialysis, and silica gel column chromatography, anti-cancer studies were performed against MRC-5 (normal lung cells) and U937 cell (leukemia cell line). Additionally, optimization fermentation was performed in terms of significant variables screened from a one-factor-at-the-time (OFAT) approach. The interactions of different experimental parameters were investigated using the response surface methodology (RSM) with the central composite design (CCD) algorithm. Cytotoxicity study showed that the dose-dependent effect of the L-ASPase at 100 IU/ml had a lethality of about 80% against leukemia cells. Therefore, the IC50 of the enzyme for leukemia cells was calculated to be approximately 33.54 IU/ml. Interestingly, the cytotoxicity of L-ASPase against normal lung cells was only about 20% at L-ASPase activity of 60-100 IU/ml. Based on the quadratic model, the optimal fermentation conditions were predicted to be 2% glucose, 2% NaCl, pH7, and incubation temperature 30 °C. Under these conditions, the highest enzyme activity was 90 IU/ml, which had an efficiency of about 30% compared to non-optimized conditions. The results showed that L-ASPase isolated from Brevibacterium sp. M-R21 with selective cytotoxicity against the leukemia cell line may be a potential candidate as an anti-cancer drug after further study.

In Silico Target Identification and Molecular Docking Studies of Natural Cytotoxic Compound Borivilianoside H

2020 ◽  
Vol 8 (2) ◽  
pp. 127-137 ◽  
Author(s):  
S. Dutta Gupta ◽  
P. Kohli
Keyword(s):  
Molecular Docking ◽  
Cell Line ◽  
Binding Affinity ◽  
Dynamics Simulation ◽  
Bone Morphogenetic Protein 2 ◽  
Dimensional Structure ◽  
Colorectal Cancer Cell Line ◽  
Cancer Drug ◽  
Target Proteins ◽  
Anti Cancer

Background: Borivilianoside H is a naturally occurring anti-cancer compound with known cytotoxicity against human colorectal cancer cell line (HCT-116) and human adenocarcinoma cell line (HT-29). The present study describes the pharmacophore modelling, molecular docking, and molecular dynamics simulation approaches to predict the target proteins of borivilianoside H along with its binding affinity to the selected proteins. Methods: A 3-dimensional structure of borivilianoside H was constructed using Avogadro from its 2-D coordinates retrieved from the Pubchem Compound database. Target proteins associated with cancers were identified based on the 95% normalized fit score of PharmMapper. The crystal structures of the targets were retrieved from Protein Data Bank and molecular docking was performed with Autodock Vina 1.1.2. MD simulations were carried out via Google Cloud Platform. ADMET characteristics for borivilianoside H were determined using admetSAR web server. Results: Among the selected 7 top-ranked target proteins, fibroblast activation protein (FAP) exhibited the highest binding affinity followed by serum albumin (ALB), bone morphogenetic protein 2 (BMP2) and kinesin-like protein 11 (KIF11). However, the best fit was found with KIF11, where both the steroidal and oligosaccharide moieties of borivilianoside H were involved in interacting with the protein cavity. KIF11 was thus found to be the most suitable target for the anti-cancer effect of borivilianoside. ADMET analysis revealed its suitability as an intravenous drug. Conclusions: The targets predicted using this approach will serve as leads for the possible use of borivilianoside H, one of the active ingredients of Chlorophytum borivilianum as an anti-cancer drug.

scholarly journals A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250620
Author(s):  
Fatemeh Ahmadi Moughari ◽  
Changiz Eslahchi
Keyword(s):  
Machine Learning ◽  
Cell Line ◽  
Drug Sensitivity ◽  
State Of The Art ◽  
Computational Method ◽  
Cancer Drug ◽  
Learning Approaches ◽  
Efficient Treatment ◽  
Proposed Model ◽  
Anti Cancer

Determining sensitive drugs for a patient is one of the most critical problems in precision medicine. Using genomic profiles of the tumor and drug information can help in tailoring the most efficient treatment for a patient. In this paper, we proposed a classification machine learning approach that predicts the sensitive/resistant drugs for a cell line. It can be performed by using both drug and cell line similarities, one of the cell line or drug similarities, or even not using any similarity information. This paper investigates the influence of using previously defined as well as two newly introduced similarities on predicting anti-cancer drug sensitivity. The proposed method uses max concentration thresholds for assigning drug responses to class labels. Its performance was evaluated using stratified five-fold cross-validation on cell line-drug pairs in two datasets. Assessing the predictive powers of the proposed model and three sets of methods, including state-of-the-art classification methods, state-of-the-art regression methods, and off-the-shelf classification machine learning approaches shows that the proposed method outperforms other methods. Moreover, The efficiency of the model is evaluated in tissue-specific conditions. Besides, the novel sensitive associations predicted by this model were verified by several supportive evidence in the literature and reliable database. Therefore, the proposed model can efficiently be used in predicting anti-cancer drug sensitivity. Material and implementation are available at https://github.com/fahmadimoughari/CDSML.

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