The key to drug discovery is high-quality human data. Using Eisai's proprietary clinical trial data, in addition to the vast amount of biological data and cohort data generated daily around the world, Eisai constructs new drug discovery hypotheses based on the true cause of disease for unmet medical needs, and turns them into drugs using the most appropriate drug discovery modalities. The data generated in our drug development process is utilized in the next drug discovery process to seek maximum efficiency. This knowledge circulation is the strength of Eisai’s drug discovery.
We aim to implement the data science function, the core of our R&D, in-house. In order to create innovation through data science, data scientists need to have deep discussions on a daily basis with people who have drug discovery expertise (domain knowledge), empathize with them about the issues that are truly important for them and work toward the same goals, rather than just having a superficial and fragmented understanding. The Tsukuba Research Laboratories has "Digital Centromere" areas (see photo) as the cross-point of daily interaction between data scientists and the wet researchers who actually create the data.
Not only biologists on the same floor, but also chemistry, drug metabolism and pharmacokinetics (DMPK), and analytical researchers are grouped together in the same building, providing easy access to data science at any time. To resolve major issues that take a long time, close communication is essential to bring all information and data to the discussion.
To deliver medicines as quickly as possible, we are constantly making efforts to achieve both "right" and "quick" work processes. In order to generate accurate data at a faster pace, we continue to develop automation systems using robotics and improving computational power with high-performance computers. AI is also a revolutionary technology that can support the human brain in tasks such as decision-making, generation, and prediction, and can improve its functions through learning. The way drug discovery is being conducted is changing with AI that mimics expert's complex decision-making criteria that cannot be programmed, and generates appropriate compound structures based on huge amount of information that humans cannot possibly possess, with speed and ideas that are unparalleled in the past. The use of AI and other digital technologies is becoming more prevalent in various settings to solve the challenging problems linked from drug discovery to diagnosis and disease prediction to relieve the anxieties of patients suffering from dementia, cancer, and neglected tropical diseases as well as people in the daily living domain.