Using Real World Data, an interview with Elise Bordet – RWD and Analytics Lead

Every month, Resolving Pharma interviews the stakeholders who shape the health and pharmaceutical industries of tomorrow. In this first interview, Elise Bordet honors us with her participation, many thanks for your time and your insights!

“Data access and analytics capabilities will become an increasingly important competitive advantage for pharmaceutical companies.”

Resolving Pharma] To begin with, could you introduce yourself and talk about your background? Why did you choose to work at the intersection of Data and Pharma?

[Elise Bordet] I am an agronomist, I did a PhD in Immunology-Virology and I then did an MBA before joining my current company. I am passionate about very technical and cutting-edge topics, and the implementation of new research approaches. I was very impressed by a conference on Artificial Intelligence and the notion of a 4th industrial revolution, I didn’t want to miss this subject.

I was very attached to fundamental research in the public sector, but I still wanted to form my own opinion about the pharmaceutical industry, and I am not disappointed at all. I think that it is a great place to contribute to research and the common good.

I love the ever-changing topics, where everything changes on a daily basis, where you always have to challenge yourself to stay updated on the latest innovations. Pharma, Data and AI subjects are heaven for me!

Can you tell us what Real World Data is and how the pharmaceutical industry uses it?

Real World Data is defined as data that is not collected in a randomized clinical trial. Therefore, it is a huge topic. It ranges from data collected in registries to larger databases such as medico-administrative databases.

This data allows the pharmaceutical industry to create drugs that are better adapted to the reality of Health systems. It also allows the creation of new research approaches, to support “drug repurposing” approaches for example.

How do Real World Evidence-based approaches differ from traditional pharmaceutical industry approaches? What are their added values?

Actually, these approaches have existed for a long time, particularly in Pharmacovigilance (the famous Phase IV). However, the amount of data available, its quality, our calculation and analysis capacities have been turned upside down. All these changes allow us to answer new research questions. Questions that remained unanswered because we did not have the capacity to look at what was happening in reality. The second subject is the major contributions of Artificial Intelligence: scientifically, we will be able to go much further.

In your opinion, how is the pharmaceutical industry going to balance the use of Real World Evidence with more traditionally generated clinical and pre-clinical data in the future?

Real World data will play an increasingly important role. Each type of data has its advantages and disadvantages. In fact, it is not a question of opposing data against each other, quite the contrary, the most interesting thing is to be able to bring all these data together and extract the most of information from them.

What impact could this type of data have on the drug value chain and the partnerships that the pharmaceutical industry needs to put in place?

Data access and analysis capabilities will become an increasingly important competitive advantage for pharmaceutical companies. The Data strategy of companies is one of the essential pillars. I imagine that in the future we will look not only at the value of a company’s portfolio, but also at the value and the impact of the analytics that can be performed by the company. Data is going to play so much on the projects’ probability of success that it is difficult to imagine not taking it into account in the metrics of economic valuation.

You recently gave a presentation on digital twin technology. Can you explain what it is?

Digital twin is a very elegant concept that can be summarized as follows: with each development, we generate new data we have to rely on for the next projects. This data should allow us to model most of the levels of biological organization: molecular, cellular, tissular and then at the scale of organs or even of organisms. This modeling will prevent replicating knowledge that has already been created and will notably allow us to accelerate pre-clinical and clinical development, and why not to model the first Phase I results very precisely.

How do you see the pharmaceutical industry in 30 years’ time?

Wow! Everything is going to be different! First of all, I think that, as in all industries, technology will have enabled a profound transformation of all decision making, what we call “data-driven decision making”. Science will have made incredible progress, calculation and prediction capacities will have been multiplied, there will be new approaches in Artificial Intelligence that we do not know today. We will have made immense progress in the interoperability of the various health databases that are fragmented today. It is a good exercise to try projecting ourselves in 30 years’ time. We won’t remember how we did things before, that’s the principle of technological revolutions; we’ve already forgotten how we lived without cell phones and the Internet! We will no longer see ourselves without Data and AI at the center of our decisions and projects. From a more organizational point of view, data sharing will have facilitated public and private scientific collaborations and the implementation of projects that will accelerate research, such as the Health Data Hub in France or the European Health Data Space that will be launched by the European Union.

Do you have any advice for someone who wants to work in Data Science in the Healthcare sector?

We scientists learned through doubt and are still haunted by it. Just because you have expertise in one field (clinical trials, laboratory research, etc.) does not mean that you cannot acquire other skills in Data Science or Artificial Intelligence, for example. Versatile profiles are and will be the most sought after. So my advice is: don’t panic!

If you can, start quickly to train yourself, the Internet puts us at a click of the best courses on programming, Data Science and many other advanced subjects, take advantage of it!

Go ahead and start tomorrow!