scholarly journals Breast Cancer Drug Repurposing a Tool for a Challenging Disease

2022 ◽  
Author(s):  
Jonaid Ahmad Malik ◽  
Rafia Jan ◽  
Sakeel Ahmed ◽  
Sirajudheen Anwar
Keyword(s):  
Breast Cancer ◽  
Drug Discovery ◽  
Molecular Targets ◽  
Drug Repurposing ◽  
Current Therapy ◽  
Cancer Drug ◽  
Drug Development Process ◽  
Current Therapies ◽  
Treat Breast Cancer ◽  
Ovarian Syndrome

Drug repurposing is one of the best strategy for drug discovery. There are several examples where drug repurposing has revolutionized the drug development process, such as metformin developed for diabetes and is now employed in polycystic ovarian syndrome. Drug repurposing against breast cancer is currently a hot topic to look upon. With the continued rise in breast cancer cases, there is a dire need for new therapies that can tackle it in a better way. There is a rise of resistance to current therapies, so drug repurposing might produce some lead candidates that may be promising to treat breast cancer. We will highlight the breast cancer molecular targets, currently available drugs, problems with current therapy, and some examples that might be promising to treat it.

scholarly journals PheWAS-Based Systems Genetics Methods for Anti-Breast Cancer Drug Discovery

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 154
Author(s):  
Min Gao ◽  
Yuan Quan ◽  
Xiong-Hui Zhou ◽  
Hong-Yu Zhang
Keyword(s):  
Breast Cancer ◽  
Drug Discovery ◽  
Association Studies ◽  
Biological Significance ◽  
Cancer Drug ◽  
Systems Genetics ◽  
Cancer Genes ◽  
Genome Wide ◽  
A Genome ◽  
Breast Cancer Genes

Breast cancer is a high-risk disease worldwide. For such complex diseases that are induced by multiple pathogenic genes, determining how to establish an effective drug discovery strategy is a challenge. In recent years, a large amount of genetic data has accumulated, particularly in the genome-wide identification of disorder genes. However, understanding how to use these data efficiently for pathogenesis elucidation and drug discovery is still a problem because the gene–disease links that are identified by high-throughput techniques such as phenome-wide association studies (PheWASs) are usually too weak to have biological significance. Systems genetics is a thriving area of study that aims to understand genetic interactions on a genome-wide scale. In this study, we aimed to establish two effective strategies for identifying breast cancer genes based on the systems genetics algorithm. As a result, we found that the GeneRank-based strategy, which combines the prognostic phenotype-based gene-dependent network with the phenotypic-related PheWAS data, can promote the identification of breast cancer genes and the discovery of anti-breast cancer drugs.

Abstract 2675: Characterization of human thioesterase 2 and its potential for breast cancer drug discovery

2010 ◽  
Author(s):  
Melissa K. Ritchie ◽  
Lynnette C. Johnson ◽  
Laura A. Watts ◽  
Steven J. Kridel ◽  
W. Todd Lowther
Keyword(s):  
Breast Cancer ◽  
Drug Discovery ◽  
Cancer Drug ◽  
Cancer Drug Discovery

scholarly journals STAT proteins: novel molecular targets for cancer drug discovery

Oncogene ◽  
2000 ◽  
Vol 19 (56) ◽  
pp. 6613-6626 ◽  
Author(s):  
James Turkson ◽  
Richard Jove
Keyword(s):  
Drug Discovery ◽  
Molecular Targets ◽  
Cancer Drug ◽  
Stat Proteins ◽  
Cancer Drug Discovery

scholarly journals Chemical-induced Gene Expression Ranking and its Application to Pancreatic Cancer Drug Repurposing

2021 ◽  
Author(s):  
Thai-Hoang Pham ◽  
Yue Qiu ◽  
Jiahui Liu ◽  
Steven Zimmer ◽  
Eric O'Neill ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Drug Discovery ◽  
Large Scale ◽  
Missing Values ◽  
De Novo ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Drug Repurposing ◽  
Cancer Drug

Chemical-induced gene expression profiles provide critical information on the mode of action, off-target effect, and cellar heterogeneity of chemical actions in a biological system, thus offer new opportunities for drug discovery, system pharmacology, and precision medicine. Despite their successful applications in drug repurposing, large-scale analysis that leverages these profiles is limited by sparseness and low throughput of the data. Several methods have been proposed to predict missing values in gene expression data. However, most of them focused on imputation and classification settings which have limited applications to real-world scenarios of drug discovery. Therefore, a new deep learning framework named chemical-induced gene expression ranking (CIGER) is proposed to target a more realistic but more challenging setting in which the model predicts the rankings of genes in the whole gene expression profiles induced by de novo chemicals. The experimental results show that CIGER significantly outperforms existing methods in both ranking and classification metrics for this prediction task. Furthermore, a new drug screening pipeline based on CIGER is proposed to select approved or investigational drugs for the potential treatments of pancreatic cancer. Our predictions have been validated by experiments, thereby showing the effectiveness of CIGER for phenotypic compound screening of precision drug discovery in practice.

Repurposing of Benzimidazole Scaffolds for HER-2 Positive Breast Cancer Therapy: An In-Silico Approach

2020 ◽  
Vol 12 ◽  
Author(s):  
Selvaraj Jubie ◽  
Uma Durai ◽  
Subbiah Latha ◽  
Selvaraj Ayyamperumal ◽  
Ashish Wadhwani ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Discovery ◽  
In Silico ◽  
Safety Data ◽  
Cancer Drug ◽  
Anticancer Activities ◽  
Binding Studies ◽  
Discovery Studio ◽  
Cytotoxicity Activity ◽  
Her 2

Background: A newer trend has been hoisted recently to reuse of the conventional drugs with distinct indications for the newer applications to speed up the drug discovery and development based on earlier acquaintance and safety data. Most of the non-cancerous agents could afford a little or tolerable side effects in individual. However, repositioning of these non-cancerous agents for successful anticancer therapy is an outstanding strategy for future anti-cancer drug development. Since more diverse and selective cancer drug targets are being discovered and developed, the approved drug collections are particularly useful to quickly identify clinically advanced anticancer drugs against those targets. Objective: Antihelminthic drugs such as Mebendazole and Albendazole (Benzimidazole class) have been reported to exhibited cytotoxicity (or anticancer activities) against several types of cancer. Therefore, this study aims to repurpose the benzimidazole scaffold for breast cancer treatment. Method: In the present study, three hydrazone analogs having benzimidazole motif in their structural frame were synthesized. Their in-silico binding studies against HER-2 receptor (PDB ID: 4LQM) and ADMET studies were carried out using Accelrys drug discovery studio 4.1. Cytotoxicity of the synthesized compounds against HER-2 overexpressed MCF-7 cell lines was determined by MTT assay. Result: One of the compound 2-[2-(2,4-dinitrophenyl)hydrazinylidene]-2,3-dihydro-1H-benzimidazole (U1) has shown good cytotoxicity activity when compared to the standard lapatinib which is a well known HER-2 inhibitor. Conclusions: Thus, the designed benzimidazole scaffold might serve as the best leads for treating the breast cancer, which is additionally confirmed by performing their docking study via Accelrys discovery studio.

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