Sramana Mitra: You put in context what might happen with the existing workflow of the drug development as we introduce more capability from a scalability point of view. Instead of a drug going through three or four stages of clinical trials, maybe it will go through five or ten stages of clinical trials going into broader and broader populations. But the data has to come in, so that there is a continuous monitoring of what is the impact of the drug on various populations.
Tarek Sherif: By the way, this isn’t just from a negative perspective. You can look at it from a positive perspective. You bring a drug to market and you see that it has positive side effects or it impacts patients who have a different diseases. We see drug repurposing all the time where it was designed for one thing, but it actually has an impact on something else. Those are the kinds of effects that you want.
There’s one other piece I’d add to your thinking. Quality-of-life data has historically not been a big focus because the data has been difficult to get in qualitatively. I wear an Apple Watch now. I monitor how many steps I take. I look at my heart rate when I’m working out. There are a lot of sensors that are being developed over the last few years. The sophistication of those sensors is going to go up.
Historically, when you run a clinical trial, quality of life and how you measure it has not necessarily been an endpoint that regulators and payers have looked at, but they are increasingly interested in it. Now, because you have this IoT and sensors, you can now start to collect qualitative data that may impact the decision making of the regulator and the payers.
You may have a drug that, on the one hand, doesn’t look like it did what you thought it should do, but it improved the quality of life of the patients who received it. Well that’s actually very important. Did you develop a drug that, in theory, helps to solve a problem that a patient has, but their quality of life goes down so much that the impact is negligible?
Those are the kinds of conversations that are starting to happen today and that just involves more data and more complexity. That’s why, again, the move to the cloud, the digital transformation, and the use of artificial intelligence are such dominant themes at the most senior levels in life sciences today.
Sramana Mitra: If you have observed, one of the most successful cloud companies in history is Salesforce.com. They did a platform strategy right from the beginning, which allows them to open their stack, their data, and their API’s to other third-party developers. Many very interesting and very powerful companies have been built based on this strategy. Some of them, they have acquired since.
Now as a major player in the clinical trial space and the pharmaceutical space drug development, what are your thoughts about doing that with your ecosystem? Are you thinking about a Platform-as-a-Service strategy? Are you going to do something like this and invite entrepreneurs to work with you on your platform perhaps?
Tarek Sherif: Given what we were doing, historically, the answer would have been ‘no’ because of some of the regulatory challenges. It is a regulated industry. It creates certain hurdles that are quite difficult to get over. I would say that given the direction the industry is moving toward from a digital platform perspective or a data science perspective, it creates opportunities for us to open up the platform more to working with other organizations. That, I think, is very legitimate.
Sramana Mitra: But it’s still in the early days. You haven’t gotten to that yet.
Tarek Sherif: No, we have not. By the way, there’s something that popped into the back of my mind when you were asking about the competitive environment differentiators and all those things. One of the things that we did starting about eight or nine years ago was when we came to the realization that we had so much life science industry data flowing through our pipes. We started to ask our customers for data rights – the right to use the data.
First of all, it was just the transactional data. Over time, we started to evolve that to scientific data as well. What we actually did is amass a repository of the largest unified repository in the world of clinical data. It spans across all therapeutic areas. It spans across all global locations and all different kinds of companies.
It’s unique. It doesn’t exist anywhere else in the world. It can’t be replicated easily because of the scale that we have. So having that capability or having that data wall that we’ve created, that is a very interesting and unique competitive situation because it creates a competitive moat. It is a unique asset.