Forward-thinking pharmaceutical companies are improving the drug development pipeline by applying semantics to the problems of information spread and overload. By leveraging sophisticated technologies that automatically categorize and classify content, they can provide clinical pathologists, bio statisticians and other knowledge workers with the tools they need to discover critical insights buried in years of existing drug research.
These insights can inform all stages of drug development, from improving the development of molecules to identifying appropriate experiments during animal and pre-clinical studies to designing clinical trials. Unlocking the intelligence in their information allows pharmaceutical companies to leverage the immense knowledge accumulated through years of research to improve drug efficacy and identify failures early.
This trend of early attrition - identifying drugs that will not be successful as early as possible - saves money and frees up valuable resources to pursue new treatments that will be successful. Progress has been made in increasing the rate of attrition in Phase II with model-driven drug development techniques, but forward-looking pharmaceutical companies are trying to increase the attrition rate even earlier in the cycle, reaching all the way into molecule design to identify candidates that will not live up to their promise. And they realize that while model-driven development is part of the answer, a tremendous store of knowledge that informs the models is also locked away in the documentation of earlier research projects – lab notes, clinical trial reports, earlier models and results of previous experiments. The information is hidden away in disparate repositories and formats, with no way of getting to the knowledge inside except by guessing what documents might be relevant and opening them to see if they are.
Find out how you can improve the drug development process with Smartlogic Semaphore
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