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The certainty of uncertainty

Posted on July 17th, 2017 by in Pharma R&D

politics

The recent election results in France and the UK once again show that a week is a long time in politics. Who would have thought when the UK voted to Brexit that Emmanuel Macron would sweep into power following the creation of his new political party in April 2016? Similarly, who would have thought that Theresa May would lose her overall majority after calling for a snap election? And all of this occurring against the backdrop of President Trump’s election last year.

This results in some difficulty in estimating what might happen next, which creates some risks in confident prediction. However, the political scientist Jane Green suggests another approach. Instead of trying to predict, the scientific focus should shift to understanding why elections are unpredictable (1). The parallels of the risks of predictions in political science with the life sciences involved with medicines development seem quite striking to me. In many ways, both approaches are trying to gather data from complex systems with a prediction of future success, whether this be political or in terms of choosing the right innovation for investment

The recent Biotechnology report published by Ernst and Young (2) highlights that uncertainty is the only certainty and that biotechs need to stay the course through the highs and lows of the innovation process. The long cycle times of medicines development may provide some insulation against the changes in regulatory guidelines and healthcare policies, and companies will need to continue to adapt. The report underlines the need for R&D to improve ROI and shows how some are using technology to adapt (2). Adapting to what is happening is a crucial aspect of staying on course. If individuals and organizations don’t adapt to their situation or environment, then they will drift off course quickly.

Amongst uncertainty, how do we know we are staying on course? I think following Jane Green’s advice seems an appropriate way to move forward. If we can start to understand why the drug discovery process is unpredictable then we can start to understand the complexity of the system. However, this leads to another tough question: how can we start to understand?

There are many ways to potentially answer the question, and before you answer, perhaps another question may be useful: What to do with the understanding? The short answer is to stay on course, which no doubt starts a closed loop of thinking and has a feeling associated with one of Escher’s picture of Ascending and Descending. One potential way out of this “bind” is to perhaps learn from developments in safety systems engineering. Here, specific control systems are put in place to allow feed forward and feedback mechanisms to understand the system with the intent of staying on the course of safety to operators, users and the wider society in general.

Safety systems approach to define the appropriate control mechanisms to manage the highly dynamic interface with humans and the interrelated component technical parts.  Advances in developing safer engineering systems (3) focus on the system and a variety of control principles that control the emergent properties—in this case, safety. These control points need to be defined and measures developed to understand them. This might be a different way to think about the R&D system. In my experience, scientists and technologists prefer to focus on what might be possible rather than how to control.  However, it is also my experience that we focus on quality and getting it right. Focusing on how we constrain our activities to environmental and situational events might just be a way we can stay on course and deal with the certainty of uncertainty.

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