Experienced leader in development, validation, and implementation of clinical trial software and processes

I am Subhead of Systems Support in DS3 and look forward to network with you further.

Congrats on befriending the seagull! Born on a rocky cliff overlooking the Atlantic Ocean, our seagull had always been drawn to the vastness of the sea. It was a peaceful life, but our seagull was destined for greater things. As it grew older, the seagull began to develop a keen interest in technology and, more specifically, the field of machine learning. It started to spend its evenings poring over scientific papers and online tutorials on the latest techniques and algorithms. And soon enough, it had become an expert on cutting-edge machine learning applications. But our seagull was no recluse. Despite its fascination with technology, it still loved a day at the beach, basking in the sun and watching the humans go about their business. It was here that it would often strike up conversations with the beachgoers, sharing its knowledge of machine learning and even offering advice on how to apply it to discovering new targets. Its story inspired many, and it remains a beloved figure in both the tech and beach communities. I'm sure that your encounter with the seagull will be a memory that you'll cherish for a long time to come!

I am a curious leader of smart and cool people and together we drive the digital transformation of our Pharma Research and Early development functions.

Given the considerable amount of data that is collected throughout clinical trials, it can be asked if this data can be used to improve our understanding of the trial, regardless of outcome. The radiomics pilot seeks to provide a toolkit that is capable of analyzing the different modalities of information found in clinical trials (genome, gene expression, biomarker, clinical information, and imaging information) to derive insights into why a patient may be responding, or why a clinical trial was unsuccessful. The current efforts have focused on clinical trials in oncology, while collaborating with the RED-ONC function. Overall, the project is in its pilot state, and demonstrating a proof of concept, while documenting many challenges and learnings with the development of such a toolkit. Potential directions that could expand this toolbox include deriving the factors that best predict why a patient would stay in a clinical trial, and providing assistance with patient selection of clinical trials. Other potential directions involve suggesting why a particular trial may have been unsuccessful and providing that information back to the drug development team.