The client is conducting a first-in-human trial of a treatment for osteoarthritis and needs to assess its performance against competing therapies and determine the appropriate sample size for the pivotal trial. There are too many articles to review manually and identify the information relevant to this specific question.
We extracted structured information from public-domain literature using our proprietary Bio|Reflect® tool, with iterative refinement of the outcomes and rigorous data quality checks. In total, we extracted 150 relevant data points from more than 5,000 that were collected from 900 publications, achieving 90% precision. confidence in the path to market.
Our work enabled a more accurate, cost-effective trial design through data-driven power calculations, helping accelerate the path to market.
The approach gave the client a data-driven view of the competitive landscape and clinical evidence. By enabling a more accurate trial design and sample size calculation, it helped reduce unnecessary costs and timelines while increasing confidence in the path to market.
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AI has quickly become part of everyday biotech research, helping scientists navigate information, write code and make sense of complex data. But chat-based tools have their limits. They can provide answers and guidance, but they cannot always do the actual scientific work. As biotech moves beyond the chatbox, the next step is putting AI to work directly within research workflows.
BioLizard is proud to have received the Best Poster Award at the European Society for Spatial Biology (ESSB) conference, recognising our work on benchmarking cell segmentation methods for single-cell spatial transcriptomics.
KMOinsider and Netwerkers.be, host a panel of four perspectives on leadership, with discussions spanning strategy, communication, trust, autonomy and the role of people in organisational growth.
