Trends & Insights: Spotlight Winter Edition

Discover how academic researchers are using DrugBank to power groundbreaking research

Trends & Insights: Spotlight Winter Edition

DrugBank is passionate about equipping leading researchers with the data they need for new discoveries. As a trusted partner to world-renowned researchers, we are proud to be contributing to groundbreaking research and aiding in solving some of the industry's most pressing issues.

Our team is excited to spotlight a handful of impressive academic researchers, their work, and how they've used DrugBank to power their research.

Check out previous Trends & Insights Spotlights

Academic User Spotlight

Subhajit Dutta1,2 & Ted Natoli3

  • Laboratory of Cellular Differentiation & Metabolic Disorder, Department of Biotechnology, National Institute of Technology Durgapur, India.1
  • Present address: Functional Genomics and Metabolism Research Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Denmark.2
  • Cancer Program, Broad Institute of MIT and Harvard, USA.3

Research Focus: Network-based drug repositioning strategy to identify drugs targeting obesity and type 2 diabetes.

DrugBank Powered Research: The differentially expressed genes (i.e., disease genes) generated from patients inflicted with both obesity and type 2 diabetes, were mapped to tissue-specific protein–protein interactome and gene coexpression network to generate tissue-specific disease modules. Dutta et al. retrieved the drug targets from DrugBank, which they used to establish the relationship with the disease modules, finally predicting drugs that can target these modules effectively.

Summary: The paper digs into the link between type 2 diabetes and obesity, which are often referred to as "diabesity" due to the shared molecular connections between them. The research describes the hallmarks of type II diabetes and the ways in which hyperinsulinemia promotes fat synthesis and storage, ultimately leading to weight gain and secondary metabolic disorders while showcasing the limitations of current medications. Throughout their research, Dutta et al. explored network-based strategies to repurpose previously approved drugs that can efficiently target the connecting link between two complex phenotypes. They selected the most promising drugs that targeted the deregulated signaling axes in obese-diabetic individuals and reversed their disease signatures.

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Scientific researchers in biotech and healthtech

Jörn Lötsch

  • Biomedical scientist at the Goethe - University Frankfurt am Main, Germany

Research Focus: Knowledge discovery by combining artificial and human intelligence for information reduction of biomedical data with a focus on data science, pain, and clinical pharmacology.

DrugBank Powered Research: Pharmacological knowledge about drug targets was analyzed based on a query of DrugBank.

Summary: This paper discusses the use of a data science approach to synthesize current knowledge on the pharmacological implications of epigenetic regulation of gene expression. The approach involves querying information from multiple publicly available sources to identify enzymes involved in epigenetic processes, screen original biomedical scientific publications, and identify drugs that interact with epigenetic enzymes, including their additional non-epigenetic targets.

Drug interactions with the classical epigenetic mechanism of DNA methylation or histone modification are increasingly being used to develop novel classes of epigenetic therapeutics. This focus, on the discovery and development of these therapeutics, has proven quite successful. Throughout this paper, the researchers also state that epigenetic effects of non-epigenetic drugs or epigenetically induced changes in the targets of common drugs have not yet received the necessary systematic attention in the context of pharmacological plasticity.

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Researchers with a range of focuses from across the globe choose DrugBank as their source for reliable, verified drug knowledge. From using our structured data to validate machine learning models to finding potential drug candidates, we offer data-driven solutions for many use cases.

DrugBank's knowledge base includes more than 21,000 drug-protein interactions, 130,000 drug product listings, drug-target data that includes proteins, gene identifiers, sequences, and so much more. With both paid and free academic licenses available, there are options to suit all of your research needs.

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Academic drug datasets