gm326.pdf

Drug Discovery Today, 2011

Drug Discovery Today: Therapeutic Strategies, 2011

BMC Systems Biology, 2012

Background The process of drug discovery and development is time-consuming and costly, and the probability of success is low. Therefore, there is rising interest in repositioning existing drugs for new medical indications. When successful, this process reduces the risk of failure and costs associated with de novo drug development. However, in many cases, new indications of existing drugs have been found serendipitously. Thus there is a clear need for establishment of rational methods for drug repositioning. Results In this study, we have established a database we call “PharmDB” which integrates data associated with disease indications, drug development, and associated proteins, and known interactions extracted from various established databases. To explore linkages of known drugs to diseases of interest from within PharmDB, we designed the Shared Neighborhood Scoring (SNS) algorithm. And to facilitate exploration of tripartite (Drug-Protein-Disease) network, we developed a graphical...

The development of new drugs has become challenging as the necessary investments in time and money have increased while drug approval rates have decreased. A potential solution to this problem is drug repositioning which aims to use existing drugs to treat conditions for which they were not originally intended. One approach that may enhance the likelihood of success is to reposition drugs against a target that has a genetic basis. The multitude of genome-wide association studies (GWASs) conducted in recent years represents a large potential pool of novel targets for drug repositioning. Although trait-associated variants identified from GWAS still need to be causally linked to a target gene, recently developed functional genomic techniques, databases, and workflows are helping to remove this bottleneck. The pre-clinical validation of repositioning against these targets also needs to be carefully performed to ensure that findings are not confounded by off-target effects or limitations of the techniques used. Nevertheless, the approaches described in this review have the potential to provide a faster, cheaper and more certain route to clinical approval.

Journal of chemical information and modeling, 2015

Drug repositioning, or the application of known drugs to new indications, is a challenging issue in pharmaceutical science. In this study, we developed a new computational method to predict unknown drug indications for systematic drug repositioning in a framework of supervised network inference. We defined a descriptor for each drug-disease pair based on the phenotypic features of drugs (e.g., medicinal effects and side effects) and various molecular features of diseases (e.g., disease-causing genes, diagnostic markers, disease-related pathways, and environmental factors) and constructed a statistical model to predict new drug-disease associations for a wide range of diseases in the International Classification of Diseases. Our results show that the proposed method outperforms previous methods in terms of accuracy and applicability, and its performance does not depend on drug chemical structure similarity. Finally, we performed a comprehensive prediction of a drug-disease associatio...

Cancer, 2007

ISRN Oncology, 2012

IP innovative publication pvt. ltd, 2019

The humongous cost and long-time duration of new drug development surpasses the rewards in form benefit to patients and cost recovered by the pharmaceutical firms. The problem which this situation gives rise to are productivity gap, pressure by sky soaring prices, incompetency with respect to beneficial generics and issues from regulatory authorities. One advanced approach of drug development which shows potential to tackle these issues is what we refer to as drug repositioning. Drug repurposing is an economical option, time duration to bring a new drug to the market is lesser. There are different approaches to drug repositioning including two broad categories – data driven (computational approaches) and experimental approach. Data driven approaches include - signature matching, molecular docking, genetic mapping, pathway mapping, retrospective clinical analysis, novel data sources while the experimental approach include assays defining target drug interactions, phenotypic screening. Drug repositioning is associated with challenges like chances of failure, regulatory barriers, patency issues and lack of financial incentives. For maximizing the drug repositioning process and to increase its productivity challenges posed to drug repositioning need to be addressed. Keywords: Drug repositioning, Computational approach, Experimental approach.

Naunyn-Schmiedeberg's Archives of Pharmacology, 2022

Cancer is a complex disease affecting millions of people around the world. Despite advances in surgical and radiation therapy, chemotherapy continues to be an important therapeutic option for the treatment of cancer. The current treatment is expensive and has several side effects. Also, over time, cancer cells develop resistance to chemotherapy, due to which there is a demand for new drugs. Drug repurposing is a novel approach that focuses on finding new applications for the old clinically approved drugs. Current advances in the high-dimensional multiomics landscape, especially proteomics, genomics, and computational omics-data analysis, have facilitated drug repurposing. The drug repurposing approach provides cheaper, effective, and safe drugs with fewer side effects and fastens the process of drug development. The review further delineates each repurposed drug's original indication and mechanism of action in cancer. Along with this, the article also provides insight upon artificial intelligence and its application in drug repurposing. Clinical trials are vital for determining medication safety and effectiveness, and hence the clinical studies for each repurposed medicine in cancer, including their stages, status, and National Clinical Trial (NCT) identification, are reported in this review article. Various emerging evidences imply that repurposing drugs is critical for the faster and more affordable discovery of anti-cancerous drugs, and the advent of artificial intelligence-based computational tools can accelerate the translational cancer-targeting pipeline.