“We are proud to participate in these ambitious endeavors to support clinicians and the patients living with serious chronic diseases. The high quality real world data available empowers us to obtain key insights in the treatment of type 2 diabetics in connection with COVID-19 substantially faster with the ultimate aim of supporting our patients worldwide” — Martin Lange, senior vice president for Global Development, Novo Nordisk.
In this podcast Novo Nordisk researcher Dr. Kajsa Kvist and Dr. John Buse from the University of North Carolina at Chapel Hill (UNC Health) discuss how and why Novo Nordisk became an early participant of the National COVID-19 Collaborative (N3C), as a tool to study the pandemic. The Novo Nordisk patient analytics team led by Dr. Kvist is studying how COVID-19 affects patients with diabetes to learn how their products can help infected patients. The collaborators discuss the working model they established to leverage the strengths of academic and industry partners through the N3C.
Dan Housman 0:01
Hi everyone, this is Dan Housman, and I’m here with John Buse and Kajsa Kvist. John is from the University of North Carolina at Chapel Hill, and Kajsa is from Novo Nordisk. We’re here to talk about the National COVID Cohort Collaborative (N3C), and especially focus on how life sciences companies and other commercial groups can work and collaborate with the academic community to do interesting projects that are beneficial for patients with COVID. So, John could you introduce yourself a bit and tell us about your background.
John Buse 0:37
Yes, I’m a diabetes clinical trialist. I have been working in the field for about 30 years. I also play the role of Co-PI of the Clinical and Translational Science Awards (CTSA) Program at UNC-Chapel Hill, and was involved very early with Melissa Haendel and others in in putting together the N3C collaboration and now work with the so-called diabetes, obesity task team that’s actually leveraging the N3C enclave to do science.
1:16 And how about you Kajsa?
Kajsa Kvist 1:18
Yeah, so my name is Kajsa and I’m working from Novo Nordisk I have a background in statistics and now heading up a small unit that does data science in connection to Real World Data. I got introduced to N3C through John Buse. I read a paper about the differential outcomes of COVID in different drug therapies and reached out to John who connected me to Melissa and this ambitious and exciting endeavor initiated.
Dan Housman 1:58
Excellent. So, what’s your team at Novo Nordisk looking to do with COVID-19 and N3C
Kajsa Kvist 2:06
So the overall intention from my team is basically to support clinicians and patients living with serious chronic diseases and since the vast majority of data available to us is in the area of type 2 diabetes. This is of course where we have our focus. And in connection to COVID. There is so much information continuously being published so we just wanted to do some high-quality use of high-quality data to support the treatment of type 2 diabetics in connection with COVID-19 and the potential differential impact on COVID-19 outcomes depending on various anti diabetic treatment regimens.
Dan Housman 2:52
And how’s the Real World Data really helping you here.
Kajsa Kvist 2:56
So, for the moment, at least from my point of view, there is no large-scale data sources other than Real World Data.
And they an RCT in this setting would have to be very long and very large to answer questions on outcomes. So, the availability of high-quality Real World Data is essential. And with the potential of allowing us to draw similar conclusions, but substantially faster, and to the benefit of our patients. And I think in general Real World Data is of major relevance because it gives us insights as to how patients are using our therapies, and how they perform in the real world as opposed to the more controlled setting of an open RCT randomized control trial.
John Buse 3:52
Yeah, maybe I can just jump in a second. I think as a clinician, the problem has been that most of the publications that address the important clinical questions that we could do something about today. Namely, I as a doctor when I see my patients, later this afternoon, are there things that I would know from the literature that I could say look if we do this today, if you do get COVID-19, I would hope that you would have a better outcome for having done this today. There’s very little we can say with certainty mostly because even though there are literally thousands of papers that arguably are relevant to that decision making, most of them are either very small, you know, dozens to a few hundred patients, or they’re single center experiences, or they occurred in healthcare systems outside the United States where the dynamics of the epidemic were very different and certainly didn’t reflect the treatment of today including with Remdesivir and steroids. And so that’s the beauty of N3C it’s real time data and relevant to the United States. And it includes hundreds of thousands of patients with COVID-19 so it’s a huge data set. So, I have lots of questions, but minimal skills. Kajsa has lots of questions, but she has great skills. And so it’s fun to work together. And frankly, there’s this diabetes, obesity task team that I mentioned it’s now about a dozen or so people that get together once a month and try and come up with what the important questions are.
Dan Housman 5:48
And I was going to ask John, so what do you sort of see as the benefits for working with a commercial group collaborating with your team.
John Buse 5:58
You know, I think the big problem for investigators today is that everybody had a full-time job before COVID-19. And so now whenever we have a sort of COVID-19 interest we have to sort of squeeze that in. On top of what was a pretty demanding career to begin with. You know the folks at Novo Nordisk have amazing skills, but on top of that they also have some time and energy to devote to this so frankly the analysis that we’re doing we couldn’t have, we just couldn’t do without them. Both because of their skills, but also being able to devote time to the effort
Dan Housman 6:39
And Kajsa in working with John and the rest of the team and N3C, what have you found beyond the data? What have you gotten out of the relationship?
Kajsa Kvist 6:51
I think it’s been actually an amazing opportunity. I think it’s fantastic, I mean the whole the whole setup, all the people that you get to know, the meetings the discussions. Listening to research hypothesis from a range of fields. So, the opportunity to learn from my point of view and my team has been amazing. Discussing the data set up for the very sort of hardcore data scientists and then learning from other areas, being part of the journey has been amazing and to John’s point and i think that coming with the commercial entity like Novo Nordisk in the back there is just a lot of resources to tap into that can be sort of diverted into, into high quality science in this aspect.
John Buse 7:46
Now one thing we forgot to mention Kajsa is, you know, the other real magic to our little effort is that we also have MD, PhD student involved Anna Kahkoska, and you know she happens to be in a stage in her career where she’s got some time and very smart, very efficient and the other special ingredient is having trainees involved who, you know, who also have a really deep interest in answering important questions. And so she has done a lot of the heavy lifting with regards to the actual writing of protocols. The actual drafting of what we hope will be a paper soon. You know, Kajsa’s team has done the vast majority of the analysis. And then the other clinicians they’re bringing questions and trying to make things real in the effort so, you know, I don’t think this would happen without this team being together and the resource to work on is obviously the most important ingredient.
Dan Housman 8:57
So, in terms of the process to get involved and maybe if there are any barriers what they look like. Can you just describe what it took to get started and running Kajsa.
Kajsa Kvist 9:07
And so as I initiated, John informed me about this ambitious, exciting endeavor that is the N3C. And then, I mean as Covid in the spring was and is new to us so was N3C so of course there were, I mean, there’s some initial, you know hiccups figuring out who to talk to and how to do but otherwise, I think it’s been amazing the way the speed with which data comes in, the cleaning, the amount of people you can talk to and get advice from and the sharing of where you are and the different task force teams. So, I think it’s been amazing to be part of it. It really is sharing best practices and learning from each other. And from our point we haven’t had any, any issues or technical challenges, getting started.
John Buse 10:07
I mean, the short answer, you know, particularly for somebody from the pharmaceutical industry that wants to get involved, it’s about making a data use request. You can do that online. It’s, you know there, there’s nothing that difficult about it. If you want to do it in the context of one of the task teams there are now about 15 or so task teams that are focused in different areas, ours is diabetes and obesity, but they have some that are on chronic kidney disease, heart disease, social determinants of health, pharmacoepidemiology, I mean there’s a bunch. The clinicians are looking for people to help answer the questions. They’re more clinicians involved and fewer people that actually you know have skills, with regards to, you know, developing the code and doing the analyses. You know, we also have a fair number of hardcore informaticians that are less good at sort of developing the question so it’s a village, and there’s plenty of room for more people to participate.
Dan Housman 11:18
And what have you learned so far? Maybe you learned in the process either some early results or at least even things you should or shouldn’t do to make this work well?
Kajsa Kvist 11:30
So, I think that it’s a really exciting thing that is out there to the world to be used if you have the time. And it’s open to all of us and we can get in and investigate our research hypothesis. So I would say, you know, the learning is that it’s there and it gets better the more you use it because it is like open software that just becomes better the more people who uses it and develop it, and is continuously growing at an alarming rate.
Dan Housman 12:04
And what do you see happening in the next six months, seeing as COVID is still in full swing in Europe and in the US. What kind of research might change from what you originally started to do?
Kajsa Kvist 12:18
From our side, well, so we have set out three, as I see, relevant questions and we are now well underway with the first one and writing a paper and then I hope that everybody still wants to join and we’ll continue on to the next ones, and from Novo’s point of view, you know, we learn more about the indications that we focus on, we learn more about products so we can develop better products and then we get more details about the data and more information about the patient so maybe it would be more detailed a niche hypothesis that we step into. After these first three that we put up and agreed to in in John’s task team.
John Buse 13:03
I think the other thing that’s going to be really important moving forward, I mean even if they develop a drug that just absolutely cures people, even if we have a vaccine tomorrow, there’s still this issue of the long term effects of COVID-19 and because the N3C enclave will continue to have data contributed on the people who have already had COVID for the next five years. I think there is a long-term project to understand the long-term consequences and, you know, are there certain practices that are associated with better outcomes or worse outcomes in the long run. So, you know, even if the pandemic ends tomorrow, the resource is going to be very valuable for years to come.
Dan Housman 13:51
Thanks, um, was there anything you’d like to leave the audience with, in terms of a thought or an action they could take as we close out this podcast?
Kajsa Kvist 14:03
I think that it is repeat again, I think that the N3C is there and it gets better the more who uses it and it really is and an amazing a resource to tap into for the reasons that John just mentioned.
John Buse 14:18
And it’s not too late to get involved, I mean that I think we have learned a lot and working with it over the last three months and you know what I would say is it has been challenging. It isn’t as easy as I hoped but the truth of the matter is, it would be easier to get involved today than it was three months ago, and two months from now, it still won’t be too late. And it’ll just be easier still and six months from now, you know, my guess is there’ll be many publications, and then the only challenging thing is, you know, how to think about something that isn’t already being done, you know, once we have hundreds and thousands of people who are involved in the process. So, I think this is a great time to get involved.
Kajsa Kvist 15:10
Right. Now that there’s more than a million patients in there with, you know, high quality and detailed data so the number of research hypothesis, even if the pandemic went away tomorrow, is a good resource.
John Buse 15:28
And it’s free.
Dan Housman 15:29
It’s great. I think I think I echo all of your thoughts that, you know, come along at least give it a try if you’re in a commercial organization. And then, even if you’re a citizen scientist you should come along and figure out ways to make this data useful. There is a working group. I Kajsa and I are both on it, in terms of commercial groups that are interested so you can also join that if you’re not sure where to go first. And you’ll see it on the Website but please, come along if you’re listening to this podcast and, you know, start getting involved, but uh John and Kajsa thank you so much for joining me today and I look forward to reading your research as it comes out.
John Buse 16:11
Kajsa Kvist 16:12
The project described was supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR002489. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.