• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4301-4305
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• 2014

Tanshinone IIA is a pharmacologically active ingredient extracted from Danshen, a Chinese traditional medicine. Its molecular mechanisms are still unclear. The present study utilized computational approaches to uncover the potential targets of this compound. In this research, PharmMapper server was used as the inverse docking tool andnd the results were verified by Autodock vina in PyRx 0.8, and by DRAR-CPI, a server for drug repositioning via the chemical-protein interactome. Results showed that the retinoic acid receptor alpha ($RAR{\alpha}$), a target protein in acute promyelocytic leukemia (APL), was in the top rank, with a pharmacophore model matching well the molecular features of Tanshinone IIA. Moreover, molecular docking and drug repurposing results showed that the complex was also matched in terms of structure and chemical-protein interactions. These results indicated that $RAR{\alpha}$ may be a potential target of Tanshinone IIA for APL. The study can provide useful information for further biological and biochemical research on natural compounds.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.131-138
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• 2017

New drug development is time-consuming and costly. Hence, it is necessary to repurpose old drugs for finding new indication. We suggest the way that repurposing old drug using massive literature data and biological network. We supposed a disease-drug relationship can be available if signal pathways of the relationship include significant genes identified in literature data. This research is composed of three steps-identifying significant gene using co-occurrence in literature; analyzing the shortest path on biological network; and scoring a relationship with comparison between the significant genes and the shortest paths. Based on literatures, we identify significant genes based on the co-occurrence frequency between a gene and disease. With the network that include weight as possibility of interaction between genes, we use shortest paths on the network as signal pathways. We perform comparing genes that identified as significant gene and included on signal pathways, calculating the scores and then identifying the candidate drugs. With this processes, we show the drugs having new possibility of drug repurposing and the use of our method as the new method of drug repurposing.

• Journal of Microbiology and Biotechnology
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• v.32 no.1
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• pp.1-5
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• 2022

Diagnostics, vaccines, and drugs are indispensable tools and control measures employed to overcome infectious diseases such as coronavirus disease 2019 (COVID-19). Diagnostic tools based on RT-PCR were developed early in the COVID-19 pandemic and were urgently required for quarantine (testing, tracing and isolation). Vaccines such as mRNA vaccines and virus-vectored vaccines were also successfully developed using new platform technologies within one year after identifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the causative agent of COVID-19. Drug development has been conducted in various ways including drug repurposing, convalescent plasma therapy, and monoclonal antibody development. Among the above efforts, this review examines COVID-19 drug development along with the related and upcoming challenges.

• Biomolecules & Therapeutics
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• v.30 no.5
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• pp.427-434
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• 2022

The drug repurposing strategy has been applied to the development of emergency COVID-19 therapeutic medicines. Current drug repurposing approaches have been directed against RNA polymerases and viral proteases. Recently, we found that the inhibition of the interaction between the SARS-CoV-2 structural nucleocapsid (N) and spike (S) proteins decreased viral replication. In this study, drug repurposing candidates were screened by in silico molecular docking simulation with the SARS-CoV-2 structural N protein. In the ChEMBL database, 1994 FDA-approved drugs were selected for the in silico virtual screening against the N terminal domain (NTD) of the SARS-CoV-2 N protein. The tyrosine 109 residue in the NTD of the N protein was used as the center of the ligand binding grid for the docking simulation. In plaque forming assays performed with SARS-CoV-2 infected Vero E6 cells, atovaquone, abiraterone acetate, and digoxin exhibited a tendency to reduce the size of the viral plagues without affecting the plaque numbers. Abiraterone acetate significantly decreased the accumulation of viral particles in the cell culture supernatants in a concentration-dependent manner. In addition, abiraterone acetate significantly decreased the production of N protein and S protein in the SARS-CoV-2-infected Vero E6 cells. In conclusion, abiraterone acetate has therapeutic potential to inhibit the viral replication of SARS-CoV-2.

• Journal of Integrative Natural Science
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• v.15 no.4
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• pp.145-152
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• 2022

Colon rectal cancer is one of the frequently diagnosed cancers worldwide. In recent times the drug discovery for colon cancer is challenging because of their speedy metastasis and morality of these patients. C-jun N-terminal kinase signaling pathway controls the cell cycle survival and apoptosis. Evidence has shown that JNK1 promotes the tumor progression in various types of cancers like colon cancer, breast cancer and lung cancer. Recent study has shown that inhibiting, JNK1 pathway is identified as one of the important cascades in drug discovery. One of the recent approaches in the field of drug discovery is drug repurposing. In drug repurposing approach we have virtually screened ChEMBL dataset against JNK1 protein and their interactions have been studied through Molecular docking. Cross docking was performed with the top compounds to be more specific with JNK1 comparing the affinity with JNK2 and JNK3.The drugs which exhibited higher binding were subjected to Conceptual - Density functional theory. The results showed mainly Entrectinib and Exatecan showed better binding to the target.

• Molecules and Cells
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• v.44 no.9
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• pp.688-695
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• 2021

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has become a global health concern. Various SARS-CoV-2 vaccines have been developed and are being used for vaccination worldwide. However, no therapeutic agents against coronavirus disease 2019 (COVID-19) have been developed so far; therefore, new therapeutic agents are urgently needed. In the present study, we evaluated several hepatitis C virus direct-acting antivirals as potential candidates for drug repurposing against COVID-19. Theses include asunaprevir (a protease inhibitor), daclatasvir (an NS5A inhibitor), and sofosbuvir (an RNA polymerase inhibitor). We found that asunaprevir, but not sofosbuvir and daclatasvir, markedly inhibited SARS-CoV-2-induced cytopathic effects in Vero E6 cells. Both RNA and protein levels of SARS-CoV-2 were significantly decreased by treatment with asunaprevir. Moreover, asunaprevir profoundly decreased virion release from SARS-CoV-2-infected cells. A pseudoparticle entry assay revealed that asunaprevir blocked SARS-CoV-2 infection at the binding step of the viral life cycle. Furthermore, asunaprevir inhibited SARS-CoV-2 propagation in human lung Calu-3 cells. Collectively, we found that asunaprevir displays broad-spectrum antiviral activity and therefore might be worth developing as a new drug repurposing candidate for COVID-19.

• Biomolecules & Therapeutics
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• v.29 no.3
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• pp.268-272
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• 2021

Novel coronavirus (SARS-CoV-2) has caused more than 100 million confirmed cases of human infectious disease (COVID-19) since December 2019 to paralyze our global community. However, only limited access has been allowed to COVID-19 vaccines and antiviral treatment options. Here, we report the efficacy of the anticancer drug pralatrexate against SARS-CoV-2. In Vero and human lung epithelial Calu-3 cells, pralatrexate reduced viral RNA copies of SARS-CoV-2 without detectable cytotoxicity, and viral replication was successfully inhibited in a dose-dependent manner. In a time-to-addition assay, pralatrexate treatment at almost half a day after infection also exhibited inhibitory effects on the replication of SARS-CoV-2 in Calu-3 cells. Taken together, these results suggest the potential of pralatrexate as a drug repurposing COVID-19 remedy.

• The Korean Journal of Physiology and Pharmacology
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• v.27 no.5
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• pp.471-479
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• 2023

Disulfiram (DSF), a medication for alcoholism, has recently been used as a repurposing drug owing to its anticancer effects. Despite the crucial role of dendritic cells (DCs) in immune homeostasis and cancer therapy, the effects of DSF on the survival and function of DCs have not yet been studied. Therefore, we treated bone marrow-derived DCs with DSF and lipopolysaccharide (LPS) and performed various analyses. DCs are resistant to DSF and less cytotoxic than bone marrow cells and spleen cells. The viability and metabolic activity of DCs hardly decreased after treatment with DSF in the absence or presence of LPS. DSF did not alter the expression of surface markers (MHC II, CD86, CD40, and CD54), antigen uptake capability, or the antigen-presenting ability of LPS-treated DCs. DSF decreased the production of interleukin (IL)-12/23 (p40), but not IL-6 or tumor necrosis factor-α, in LPS-treated DCs. We considered the granulocyte-macrophage colony-stimulating factor (GM-CSF) as a factor to make DCs resistant to DSF-induced cytotoxicity. The resistance of DCs to DSF decreased when GM-CSF was not given or its signaling was inhibited. Also, GM-CSF upregulated the expression of a transcription factor XBP-1 which is essential for DCs' survival. This study demonstrated for the first time that DSF did not alter the function of DCs, had low cytotoxicity, and induced differential cytokine production.

• Journal of Integrative Natural Science
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• v.16 no.3
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• pp.81-95
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• 2023

Colorectal cancer is one of the most common types of cancer worldwide, ranking third after lung and breast cancer in terms of global prevalence. With an expected 1.93 million new cases and 935,000 deaths in 2020, it is more prevalent in males than in women. Evidence has shown that during the later stages of colon cancer, STAT1 promotes tumor progression by promoting cell survival and resistance to chemotherapy. Recent studies have shown that inhibiting STAT1 pathway leads to a reduction in tumor cell proliferation and growth, and can also promote apoptosis in colon cancer cells. One of the recent approaches in the field of drug discovery is drug repurposing. In drug repurposing approach we have virtually screened FDA database against STAT1 protein and their interactions have been studied through Molecular docking. Cross docking was performed with the top 10 compounds to be more specific with STAT1 comparing the affinity with STAT2, STAT3, STAT4, STAT5a, STAT5b and STAT6. The drugs that showed higher affinity were subjected to Conceptual - Density functional theory. Besides, the Molecular dynamic simulation was also carried out for the selected leads. We also validated in-vitro against colon cancer cell lines. The results showed mainly Acetyldigitoxin has shown better binding to the target. From this study, we can predict that the drug Acetyldigitoxin has shown noticeable inhibitory efficiency against STAT1, which in turn can also lead to the reduction of tumor cell growth in colon cancer.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.258-270
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• 2022

Cancer has become a common disease for the past two decades throughout the globe and there is significant increase of cancer among women. Breast cancer and ovarian cancers are more prevalent among women. Majority of the patients approach the physicians only during their final stage of the disease. Early diagnosis of cancer remains a great challenge for the researchers. Although several drugs are being synthesized very often, their multi-benefits are less investigated. With millions of drugs synthesized and their data are accessible through open repositories. Drug repurposing can be done using machine learning techniques. We propose a feature selection technique in this paper, which is novel that generates multiple populations for the grey wolf algorithm and classifies breast cancer drugs efficiently. Leukemia drug dataset is also investigated and Multilayer perceptron achieved 96% prediction accuracy. Three supervised machine learning algorithms namely Random Forest classifier, Multilayer Perceptron and Support Vector Machine models were applied and Multilayer perceptron had higher accuracy rate of 97.7% for breast cancer drug classification.