Pierre Fabre Group

Chièze Claude,

Until 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 numerous options for each of those category, numerous potential partners and variable expertise required depending on therapeutic area, modality, expertise, research or development phase. 

With this, one needs to optimise the panel of external contributors/partners/co-developers/providers for each compound and adapt while progressing in development. Due diligences that were episodic in previous periods become systematic. Functions such as business development, legal, purchasing, and alliance management become central and agility as well as speed are of the essence.

"The pharma industry has shown its ability to adapt to changes in regulation, financial challenges, payers requirements, intellectual protection reduction"

Likewise, management requires different skills. Key competencies need to move from internal expertise building to acquisition, mastering due diligence becomes specific know-how that makes the difference, and priorities need to shift from internal property protection to flow efficiency and common benefit. Even at the centre (or top) of the organisation, adaptations to efficient open innovation are required. Fast, easy-to-access decision-making governance, adapted financing flow, completely different external/internal cost ratios, and different risk management are key enablers of the model.

One remaining question is around the management of the drastic changes required so that it does not destroy value but eventually works and is productive. One can expect inertia, passive or even active resistance as the move will require giving up some well-established knowledge, structures, and processes. Some well-defined objectives and KPIs were enablers but will become showstoppers. Defining the innovation strategy precisely, including the core expertise to be retained, is a must, as well as adhesion from the leadership. To avoid value destruction on existing programs, progressive changes on a steady slope rather than revolution is recommended, starting with a narrow field of strategic importance closely linked to data science supported by a protected neutral due diligence team and adequate business development and legal resources. This can be the seed where to attach resources progressively, enabling a soft revolution.

Overall, what was a medieval castle before becomes an island of strategic core know-how bounded to multiple partners. This is an important change to be made but one that could well be vital to pharma research and development.