Paper by Matthias Evers, Lucy Pérez, Lucas Robke, and Katarzyna Smietana: “Despite expanding development pipelines, many pharmaceutical companies find themselves focusing on the same limited number of derisked areas and mechanisms of action in, for example, immuno-oncology. This “herding” reflects the challenges of advancing understanding of disease and hence of developing novel therapeutic approaches. The full promise of innovation from data, AI, and ML has not yet materialized.
It is increasingly evident that one of the main reasons for this is insufficient high-quality, interconnected human data that go beyond just genes and corresponding phenotypes—the data needed by scientists to form concepts and hypotheses and by computing systems to uncover patterns too complex for scientists to understand. Only such high-quality human data would allow deployment of AI and ML, combined with human ingenuity, to unravel disease biology and open up new frontiers to prevention and cure. Here, therefore, we suggest a way of overcoming the data impediment and moving toward a systematic, nonreductionist approach to disease understanding and drug development: the establishment of trusted, large-scale platforms that collect and store the health data of volunteering participants. Importantly, such platforms would allow participants to make informed decisions about who could access and use their information to improve the understanding of disease….(More)”.
