R & D

A wave of new personalized treatments for cancer have been introduced in recent years, mainly based on RCTs with novel biomarkers/mutations to guide specific targeted therapies. With this research project we aim to employ the target trial emulation framework to support various steps in drug R&D

With this research project we propose a novel, data-driven alternative for drug discovery and drug surveillance that combines machine learning hypothesis generation with subsequent rigorous pharmacoepidemiological hypothesis validation using longitudinal real-world data (RWD).

With this research project we use artificial intelligence (AI) to learn from real patient health data to help make drug development faster, safer, and smarter. To do this, we use language models and fine-tune them on health data. These models uncover patterns traditional statistical and machine learning methods often miss.

Alongside our ongoing R&D, we actively pursue new technologies that can drive progress and increase customer value in various fields of Real-world Evidence studies. Currently, our technology development focuses on delivering interactive dashboard solutions for studies, and utilization of synthetic data.