Drug repurposing custom reports

Finding new indications for drugs can breathe new life into an outdated therapy or expand the profitable life cycle of an approved one. Elsevier’s Professional Services Group (PSG) provides detailed custom reports and predictive analytics to support critical drug repurposing decisions. PSG offers a range of specialized and custom informatics solutions to help accelerate costly and time-consuming drug development life cycles through the use of drug repurposing techniques.

Our custom reports and predictive analytics help answer the following:

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Which clinical trials, publications, and patents describe potential indications for a particular drug?

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What are known indications for similar drugs (e.g., TNF inhibitors)?

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Which diseases are identified with TNF as a contributing factor?

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In which diseases is TNF known to be up-regulated and linked to targets in the TNF pathways?

Professional services solutions

PSG helps customers by addressing challenges related to efficiently identifying and mining all available evidence to extract clinical trial, publication, and patent references; and fills resource gaps related to performing comprehensive pathway analyses and scoring mechanisms. These solutions include:

Drug-centric repurposing:

  • Text mining for possible drug indications in the scientific literature
  • Prediction of new drug indications based on drug target biology and efficacy of other drugs in the same class
  • Scoring mechanism to identify most promising repurposing candidates

Disease-centric repurposing:

  • Pathway analysis to identify signaling affected in a disease, the compounds targeting proteins in the disease pathway, and known drugs to inhibit proteins

Assay

Key outcomes

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Innovation

Increased ability to identify novel approaches through a comprehensive understanding of drug target interaction

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Efficiency

Time saved due to scoring mechanisms based on binding affinity between target and all possible drugs for repurposing

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Evaluation

Rapid assessment of large data sets and external validation of existing research hypotheses