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Drug discovery is one of the slowest, costliest processes in enterprise R&D. Developing a single FDA-approved therapy typically takes 10 to 15 years, according to workshop proceedings published by the National Academies of Sciences, Engineering, and Medicine, and roughly 90 percent of drug candidates that enter development fail before ever reaching patients, according to research published in JAMA, often after years of investment in a single research direction.
Much of that cost and delay traces back to how research data is managed. Across biotech and pharma, discovery data is often distributed across multiple platforms, formats, and organizations, creating challenges for data sharing, reproducibility, and scientific reuse that researchers from the University of Pennsylvania have identified as persistent barriers to efficient biomedical research. In practice, that fragmentation appears as assay data stored in separate systems, limited interoperability between research platforms, and teams working from inconsistent data sources.
