We are one of the very few research based companies that have successfully combined the traditional sciences of chemistry and biology with genomics and computer science into a drug development platform.
We have developed an effective way to monitor individual responses to drug action, using a selection of 'hot-spot' SNPs, as well as non-genetic markers.
We use machine learning algorithms like artificial neural networks to stratify patients in clinical studies. Drug actions are simulated along the biochemical pathways.
We discover novel, synergistic drug combinations to overcome currently unmet medical needs.
During my graduate studies at Oxford and Texas, I investigated human cognition in order to optimize artificial intelligence. Insights from hands-on research in an indigenous community, neural-networks and agent models led to a machine learning model that detects irony in social media. After rounding off my skills with commercial experience at a big data start-up, I now use data science at BEROCEUTICA.
I am a trained biochemist with expertise in chemical synthesis and action of drug molecules. While deeply immersed in several experiments during my Marie Curie Fellowship at the University of Cambridge, I realized the great potential of developing effective, synergistic drug combinations. This experience led to the development of a companion diagnostic kit and the foundation of BEROCEUTICA.
I obtained my doctorate in biochemistry from Oxford University, using biophysical studies and computation, e.g. simulations and structural predictions of Notch-1 with artificial neural network analysis. After a short stay at a small CRO in Nice, I realized the opportunity of turning "non-responders" into "responders" using computational methods.