QuantHealth taking a data-driven predictive approach to simulate clinical trials

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In drug discovery, the process of shooting for regulatory approval can feel less like a sprint and more like a marathon, but with no guarantee of crossing the finish line. Despite the hefty investment of time, effort and resources, the success rate for bringing new drugs to market hasn’t improved in recent decades. The American Council on Science and Health estimates an average success rate from 2000 to 2015 of only 13.8%, with costs reaching hundreds of millions per drug. The situation appears more grim with the average cost of developing a drug surpassing $2 billion, according to Deloitte. 

“Despite increased understanding of diseases and technologies for drug discovery, developing new medicines remains an unpredictable endeavor filled with many failures,” said David Dornstreich, chief commercial officer and general manager U.S. at QuantHealth.

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How AI-based technologies improve clinical trial design, site selection and competitive intelligence

[Photo by Tara Winstead on Pexels]

Clinical trials form the cornerstone of evidence-based medicine and are essential to establishing the safety and efficacy of new drugs. However, only some of the information in clinical trial reports is well-structured and searchable via keywords; much of the information is buried in unstructured text.

In the past, uncovering actionable insights from this unstructured text meant that documents such as clinical trial reports had to be searched and read individually, a process that can be time-consuming and subject to human error. It is estimated that 80% of clinical data is unstructured and difficult to analyze.

To overcome these limitations, life sciences companies are using natural language processing (NLP), an artificial intelligence-based technology that extracts and synthesizes high-value information hidden in unstructured text. NLP-based text mining solutions can an…

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