Our research
Why do most drugs tested in animals and humans never receive regulatory approval?
We study this gap from animal testing, via clinical trials to regulatory approval and how to reduce it. A key challenge is that relevant data from trial registries, publications, regulatory documents, and real world health care are vast, fragmented, and thus difficult to use and integrate. We work on data engineering solutions that apply natural language processing, including large language models, to transform this information into analysis ready evidence. Our aim is to feed these streams into systematic reviews that can run as living pipelines, keeping results continuously updated and usable for research and clinical decision making. Through this approach we aim to identify the drivers of successful drug development and make testing more relevant for both patients and animals.
Check out our group webpage.
