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Drug discovery & beyond
While RWE is becoming increasingly valuable in clinical practice and regulatory decision-making we see adoption of RWE usage across the entire drug development pipeline, including new applications of the existing molecules and expanding labels to minor or underserved patient populations.
Overall, the recent advances in data generation, standardization and analysis are bringing us closer to the world where new treatments will be produced “ex situ”, without involvement of humans and living organisms and at the speed of though or even faster.
Target identification and validation
RWD can be used to identify potential disease targets and to validate existing targets through the analysis of large-scale patient data.
Drug repurposing
Label expansion is one of the most proliferate use case for real world data in drug development. With perhaps some of the most famous examples from Flatiron and Pfizer in use of Palbociclib in male breast cancer patients. Overall, real world data is essential when it comes to label expansion specifically in areas with rare diseases or small / patient populations and, hence, too costly economically to run a proper RCT.
Drug repositioning
Drug discovery is a very costly and time-consuming process and at best only 20% of clinical trials are successful, with this figure as low as 10% in Oncology and some other TAs.
With artificial intelligence and machine learning we have got the chance to increase these numbers and give molecules and substances a second chance. This is especially important since many of the failed drugs have proven to be safe and looking into applications beyond original label will save us significant resources.
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