DECEMBER - JANUARYLIFE SCIENCES REVIEW8OPEN INNOVATION IN PHARMA R&DBy Chièze Claude, Deputy Head R&D, Pierre Fabre Groupntil the end of the previous century, pharma companies were looking like highly protected medieval castles, with limited, protected, and controlled flows of information authorised to spread outside. During an intermediate phase starting in the 90s, precisely defined perimeters of experimental data collection, analyses, and reporting have been progressively outsourced. It started with toxicology and was then extended to other areas of expertise, such as PK-PD or formulation and analytics. During the last decade, an intensive new shift has occurred with open innovation shaking well-established paradigms, and one could say that it is not anymore the data that one owns that makes the difference but rather the ability to triage, compute, and give meaning to the huge quantity of data openly available.New biotech companies that have been built in this environment have relative competitive strength, showing narrow focus, quicker access to data that makes the difference, higher agility, and lower capital immobilisation. Bigger pharma companies which have heavily invested over time in building internal know-how, structures, and processes that were efficient in previous settings but slowing them now have a vital need to quickly adapt and catch up. The easy way seems to consist of adding a layer for data flow and integration to existing settings. However, going in this direction will quickly limit productivity and decrease competitiveness. Agility will be at stake, and new paradigms triggered by the fluidity required by data sciences will be competing with existing infrastructures, enabling and protecting internal knowledge acquisition and difficulty to connect. The pharma industry has shown its ability to adapt to changes in regulation, financial challenges, payers requirements, and intellectual protection reduction. The combination of lower return on investment pricing limitation, together with the heavy consequences of the data revolution, creates a vital need for a change in depth. In this context, open innovation should neither be a cosmetic adaptation nor an additional layer in existing organisations. Instead, it requires that structures, processes, and interfaces are redefined to be built around a focused, well-defined strategic core know-how and portfolio. Data sources are multiple: research units in public institutions, biotech companies, technology platforms, function-based expert CROs, open, collaborative models, and patient data, and there are Chièze ClaudeUIN MY OPINION
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