Signals Analytics, the next-generation advanced analytics platform that leverages external data to uncover trends and predictive insights, today announces the addition of 1,600 rare diseases, deepening its already extensive coverage of medical conditions and therapeutic areas
| NEW YORK, Feb. 20, 2020 /PRNewswire/ -- Signals Analytics, the next-generation advanced analytics platform that leverages external data to uncover trends and predictive insights, today announces the addition of 1,600 rare diseases, deepening its already extensive coverage of medical conditions and therapeutic areas. The enhancement of rare diseases coverage offers pharmaceutical companies a comprehensive view of this hard-to-capture landscape with deep, relevant insights that can unleash opportunities for drug development, repurposing, and partnering.
Rare diseases are defined in the Orphan Drug Act of 2010 (ODA) as diseases with fewer than 200,000 diagnosed individuals. According to the U.S. Food and Drug Administration (FDA), there are about 7,000 known rare diseases today that, when added together, affect an estimated 30 million people in the United States – more than almost all other chronic conditions. As a result of the incentives put in place by the ODA to further research and development of drugs to combat rare diseases, this area has become one of the fastest growing and important targets for pharmaceutical companies. Still, bringing new drugs to market for these diseases presents significant challenges; research indicates that the average orphan drug takes more than two years longer to develop than other medicines. Uncovering potential areas and methods of innovation are critical for accelerating the cycle. “Signals Analytics is revolutionizing the way life science companies understand and connect external data to develop and perfect their strategies for both the short and long term,” said Gil Sadeh, CEO of Signals Analytics. “Dealing with unstructured data sets from research papers, grants, clinical trials, patents, conferences, and more is particularly challenging as key innovation may surface through various naming conventions across not only the data itself, but also through different applications in the drug development lifecycle. Connecting and finding commonalities across disparate data is a major challenge innovators face given the amount of information that is generated all the time. Using patented natural language processing techniques, our platform cuts through the noise and extracts the relevant information that a drug manufacturer or biotech firm needs to place the right bets, and the expansion of our rare disease coverage ensures that the analysis involves connecting the most comprehensive data sets available.” Signals Analytics is a configurable data platform that allows companies to select data sources, apply new taxonomies, and define integrations and visualizations in a way that meets the needs of the business and therapeutic areas of focus. Life sciences companies use the platform to find new sources of academic research, surface early signals of innovation, identify new and promising areas for investment that have less risk, and benchmark rare disease assets against others in the same class. “One of the most promising areas for advanced analytics is in pharmaceuticals, where the high failure rate for drugs coupled with the huge cost and time commitment required to see a drug through the development cycle, is driving manufacturers to look for ways to lower their risk,” said Hiten Shah, Research Analyst, Frost & Sullivan. “Nowhere is this more pronounced than with rare diseases. By combining domain expertise with unique taxonomies and patented natural language processing capabilities, the Signals Analytics platform, winner of our 2019 Technology Innovation Award in Data Analytics, is able to deliver deep insights as to potential new opportunities, saving pharma companies huge amounts of money in drug development and ensuring they make the right investment decisions that will keep them competitive in the long run.” About Signals Analytics:
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