Big Data: Challenge and Opportunity for Translational and Industrial Research in Healthcare

Research and innovation are constant imperatives for the healthcare sector: medicine, biology and biotechnology support it, and more recently computational and data-driven disciplines gained relevance to handle the massive amount of data this sector is and will be generating. To be effective in translational and healthcare industrial research, big data in the life science domain need to be organized, well annotated, catalogued, correlated and integrated: the biggest the data silos at hand, the stronger the need for organization and tidiness. The degree of such organization marks the transition from data to knowledge for strategic decision making. Medicine is supported by observations and data and for certain aspects medicine is becoming a data science supported by clinicians. While medicine defines itself as personalized, quantified (precision med) or in high-definition, clinicians should be prepared to deal with a world in which Internet of People paraphrases the Internet of Things paradigm. Integrated use of electronic health records (EHRs) and quantitative data (both clinical and molecular) is a key process to develop precision medicine. Health records collection was originally designed for patient care and billing and/or insurance purposes. The digitization of health records facilitates and opens up new possibilities for science and research and they should be now collected and managed with this aim in mind. More data and the ability to efficiently handle them is a significant advantage not only for clinicians and life science researchers, but for drugs producers too. In an industrial sector spending increasing efforts on drug repurposing, attention to efficient methods to unwind the intricacies of the hugely complex reality of human physiology, such as network based methods and physical chemistry computational methods, became of paramount importance. Finally, the main pillars of industrial R&D processes for vaccines, include initial discovery, early—late pre clinics, pre-industrialization, clinical phases and finally registration—commercialization. The passage from one step to another is regulated by stringent pass/fail criteria. Bottlenecks of the R&D process are often represented by animal and human studies, which could be rationalized by surrogate in vitro assays as well as by predictive molecular and cellular signatures and models