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Case study: Target identification, validation and selection

Selecting the best targets in the drug discovery process is crucial for optimizing return on R&D spend across a portfolio of research projects. Researchers use text mining to establish target ranking based on efficacy and safety. Methods include providing links to biological pathways and processes, and supporting gene expression analysis in specific tissues and species.

A variety of pathway databases exist but their scope may be limited to a small number of premier journals, and there may be a lack of contextual information to focus the specific pathway analysis.Text mining approaches complement pathway database searches by providing both target context and access to up-to- date results from a much more comprehensive range of documents.

At a top-10 global pharmaceutical company, Linguamatics I2E forms part of a standard reusable framework for novel target selection in use for a variety of R&D projects. Our advanced NLP capabilities and intuitive reporting enable scientists to see assertions and drill down to supporting evidence in source documents without needing to be specialist users.

I2E customers have reported time savings for this type of literature analysis of a factor of 10 or better. For 100 scientists, this is equivalent to savings of 10 FTE years or approximately $1m/year.

Examining gene-disease associations using gene expression for target selection and ranking