The Prix Galien is a welcome initiative to stimulate creative research and promote excellence. Barack Obama, the Galien award ceremony is considered the equivalent of the Oscars night for the innovators in the labs and awards every year, the best pharmaceutical product, the best biotechnology product, the best medical technology and the best digital health product. This is the right event on the right issue at the right time. I thank his excellency,
the president for bringing Prix Galien to Africa. And I look forward to the day when we will celebrate an African winner of the Prix Galien. I'm particularly grateful to receive this award, the awards among the highest honors in science and commerce, because they lead to improvement in the human condition. The prix Galien awards recognize the world's brightest minds and most innovative companies. They are true celebration of the hard work required to produce life-changing interventions. That is what makes us optimistic about the future.
Congratulations to all of you, make a difference. Join the Galien foundation, and now welcome to the Galien digital health webinars series. Hello, and welcome to this very special session from the Galien foundation. My name is Jessica Federer, and I will be leading you through the next hour.
I'm the managing director at Huma a health technology company in research and care, and was previously the chief digital officer at bayer leading the digital transformation across the enterprise. I'm very pleased to have you all here with us today, as we go through a topic that is very close to the mission of the Galien foundation, which has a 50 year history of fostering and recognizing scientific innovation that improves lives around the world. So today we're in for a treat with a very rich discussion on the role that technology played in enabling this rapid vaccine development. I would now like to welcome our esteemed panelists representing global manufacturers. We have a wonderful group with us today,
and I'd like to introduce the panelists, invite them to introduce themselves, in alphabetical order by last name. So we'll start with Dr. Rachel Berria Thank you, Jessica. Good afternoon good evening. Good morning, everyone. So I'm Rachel Berria I hit out the medical team in biopharmaceuticals at AstraZenica. I'm thrilled to be here with such esteemed panel of experts and with all of you on this very timely and impactful topic. Thank you Rachel. Dr. Hellio. Thank you, Jessica.
And good day, everybody. Thank you for having me. I am I tell you and I had clinical development and corporations in global product development at Pfizer. Thank you, Terry. Hi Good morning. Good afternoon. Good evening. I'm Terry Murphy. I'm the vice-president of enabling business information solutions, in global development and R&D and that's the pharmaceutical sector of Johnson and Johnson. Thank you, Terry Shri Shri. Would you like to introduce yourself?
Shri you're on mute. Hello. Hi. My name is Srivatsan. I'm the chief digital officer for IQVIA technologies. I managed and drive our digitization portfolio for the CRO as well as launch digital products in the market. I'm super excited to be here.
Thank you, Shri. And Dr. Zaks. Good day everybody. I'm Dr. Zaks the chief medical officer at Moderna. Great, welcome. All right, let's dive into the conversation here.
the first question we have, is really straight forward, What is the role that digital technology has played in the development and the clinical trials and the production of the COVID-19 vaccines at your companies? Marie Pierre let's start with you. Thank you. So I think digital played an immense role in speeding up the clinical development of the COVID vaccine. at Pfizer with our partners at Biotech, we develop our vaccines in just 266 days from the announcement of the pandemic to the first emergency use authorization. And that is remarkably fast compared to usual development of the vaccine that was only possible really because we had access to digital technologies. one of our biggest challenge was really to manage the massive amount of information in a very short timeframe. and just to give a few numbers,
we enrolled 44,000 participants in about four months at 150 sites in six countries. So digital really enabled us to manage the sheer volume of data in real time from acquisition to reporting and all the while maintaining quality, a few numbers again, because I think it really helps understanding where we were managing. We receive more than half a million electronic diaries from participants 7.5 million data management. We got nearly 34 million total data points. So the volume and the speed at which we were working was totally unprecedented. What we decided to do was to create operational dashboards that provided real time data visibility into the study conduct and the data quality so that we could drive faster informed decision-making.
And these dashboards, leveraged Pfizer insights platform, which is a data Lake that enables real-time access to, all of the global solutions that are used in the clinical trial process, such as (inaudible) electronic data capture and other sources. And it enabled us to pool key data metrics and insights across all of the functions of the clinical trials. So for example, site startup enrollment, diversity, they are increase entry, and they like cleaning. And what we did is that we built one highly customized dashboard that went into very specific detail and was it dated every four hours so that we could see in real time what was happening across the trial. And we could actually use our own personal strategies as necessary. So we could see very quickly if we had to apply a new strategy to enrollment or to diversity, or whether we, we stood at some sites, had some difficulty in entering the data in real-time and we could provide additional resources.
And it really enabled us to manage our operations internally throughout the day, so that there was never any time lost. we created also a series of functional dashboards that deep dived into very specific aspects of the trial. So just an example, we had quality metrics and oversight dashboards, data management, data cleaning, and we even had a dashboard to track, nasal swabs from the time the sample was collected to the time we had the results of the PCR. Wow. Dashboards updated every four hours and a global pharmaceutical company. That's,
that's an incredible change in, in operations and how an organization works. Terry, would you like to share some from your side at Janssen? Sure. maybe starting in the development side we from, from candidate identification to well, final selection, we, we moved very quickly. So in a period of about three months, we, we moved to, to selecting a candidate. We used extensive,
simulation software to look at ways really, to, to increase yields, and also assess the benefits of different molecular scaffolds, aiding the development process or candidate selection was condensed into a very short period of time. and again, we, we immediately commenced the process development and scaling operations to produce at scale and support the trials, as well as obviously getting ready to fulfill emergency use authorization demands. So that was, that was very, very novel in the sense of the, the simulation software in the development side of the business. similar to,
to, Marie Pierre in the clinical trials space, we utilize the new integrated digital health suite. So we have similar numbers were greater than 45,000 patients recruited in our phase three study and actually just one data point. that's interesting in one day alone, we recruited just over 3000 patients, which is unprecedented. We haven't seen that in the past really. but we,
we used a new integrated digital health suite in partnership with our colleagues from IQVIA industries on the call here today, representing that capability. But we use that to identify, recruit and manage patients in our trials, that allowed us to move much, much more quickly, and also manage the, the large patient volumes, in terms of data over about, generated over a very short period of time. the digital suite covered care, consent, randomization, diaries, biometric collection, as well as, used for patient engagement.
And we used our company standard electronic data capture technology in addition to, all of our integrated backend data management and statistical programming capabilities to ensure that we had high data quality and obviously near real time flow as well. So you hear a very similar vein. We were looking at our data, in, in very, very close to real time from the digital health suite, as well as from our own backend.and, and digital really was central to our capabilities to cover, you know, adverse events, sample collection testing, and really ensure the completeness of the data package to support database lock and our recent application for emergency use authorization. I will say in, in one other area, we, we utilize data science models to predict where the COVID-19 outbreaks will occur. So if you remember back in August, we were starting to say, okay, where is COVID going to be? Because it was tailing off at the time.
Obviously we're in a very different situation now with variants and, and with pretty significant outbreak globally, but we use data science to predict, and that helped us in our country and site selection process, for our large phase three trial. And, we, we got it right. from two points, I guess the data science helped us to go there. And then we, we saw the events during the trial as well. So digital has played a significant role. Yeah. And rolling 3000 patients in one day. That's incredible. tell, talk to us about what, what you used at Moderna.
Yeah, so it's,it's really, fundamental topic for us. Our company is really digital as the backbone of Moderna of the way we think about it and really starts with our science. If you think about what an MRD molecule is, it's an information molecule and you know, all of our drugs are basically different information, different sequences that lead different applications. And in the case of COVID, that's what enabled us within 48 hours of the sequence being available on the web to start production, because this was all enabled by the digital infrastructure to begin with. It goes through manufacturing, our manufacturing infrastructure, from the get-go has been built from the ground up with no paper, all digital technologies and continuous use of, tools to improve manufacturing in yields. When you think about the sequence,
the choosing an MRI and a to go to make a protein and evolution has chosen one sequence, but the number of possible sequences are greater than you could ever experimentally address, you know, more then items in the sand, on our beaches. And, how do you do that? And that's where we leverage routinely have been for years, machine learning and artificial intelligence to do better than what evolution would come up with so that when we put a sequence into production, we're assured of the yield and have the ability to translate it. And I think as I then look at the application in the real world certainly things that have been mentioned before, like, understanding where the pandemic is intended to go so that we can direct the clinical trial recruitment real-time monitoring of patient characteristics to ensure we have diversity in the trial. But for me, what had underlined that ability is really the tremendous collaboration and tools that technology has enabled that, you know, five, 10 years ago would probably, be really challenging. It is now relatively straightforward to con to connect data pipes, if you will, at the source and enabled different companies to be looking at the same data simultaneously, and for us as a small biotechnology company, that is highly leveraging CRO partners, that was really a key enabler and continues to do so. So where we lack the infrastructure technology has enabled us to tap into the infrastructure of our CRO partners to achieve what we have.
Well, that's, that's the 48 hours from sequencing being available on the web to production is, is just so historic. but you gave the perfect segue into our wonderful CRO partners. So Shri, can you talk to us, we've heard a little bit about some of the work that IQ via has done here. Can, can you share with us what role the digital technologies played at IQVIA and enabling some of these studies. Thank you. And we're,
humble and honored to be supporting several sponsors through the vaccine trials. And I break up the clinical trial process into, different stages. The first one of course is using digital technology to recruit the right sites and patients. And as many of my colleagues have talked about here, that requires a large amount of data, but also, AI and machine learning to really help you identify what is it? I patients that are they coming from, how do you anticipate and recruit, especially at such large volumes, each of the sponsors were recruiting over 40 to 45,000 people in the trial. And so you need to make sure that you know, you're taking care of those types of dynamics as the market is evolving. The second part of it is now that you've identify the patient, it's actually to get them into the trial and into the site.
And that by no means is, uh you know, it's quite difficult to make sure that they attack the right sites. They know where they're going and bringing the patient into the door as a pretty important one. And digital technology played a significant role in bringing that into the door. But once you come in, it's all the things that you do, but digital technologies, all the way from consenting to randomization, to supporting it from connected devices and diabetes and the large volumes of these in millions of diary, entries and millions of patients coming in and using technology every day and then the near and then the back end part of it, as the data comes in, how do you then have the dashboards and the dynamics of knowing what's going on and the data surveillance to make sure that when in bad, you need to intervene because many, a time, a certain sites may have different behaviors where you need to go and help and support them. whether it's a recruiting issue, audit a data management issue, audit, it's a collaboration issue sort of quickly identifying and, getting there, is pretty important.
And last but not the least is all the technology around adverse event reporting because we're really trying to roll things out so quickly, but safety is paramount in our, industry and therefore really looking at adverse events and how you can be proactive about it, on all critical areas of how technology is applied to make these trials work in an expeditious manner. Thank you, Shri. I think all of you have underlined how critical technology was to meet these timelines and to enable these parallel work streams and, to, to be the assist in the speed that we were able to achieve.
the second question we have for all the panelists is about the specific technologies that were most helpful and accelerating these phases and collapsing the development timelines. And you've touched on some of them, but I'll go to Raquel first. Sure. Yeah. Thanks. Just,
indeed technology helped us deliver the data with unprecedented speed. One thing to highlight is that we did not cut any corners, by, by doing so. And if we reflect and, my colleagues, gave great examples already, but there's at least four elements that were essential, to accomplish this first in the way we chase the virus with interactive heat-maps to have a global picture of the affected areas, the speed of the direction of the COVID-19 infection spreading.
we at AstraZeneca did it in collaboration with IQVIA, with the governments, with the universities, as well as with our own predictive analytic modeling. And so this of course allowed us to quickly open clinical sites in areas where we could have the maximum impact. And so there was an immediate efficiency in the speed of enrollment and, and of course, in the diversity of, of the volunteers that, that were engaged second wheel braided after what I called vicious electricity to point out. So we'll use electronic consent forms. Electronic diaries implemented a system whereby patients were given wearable devices that would continuously stream vitals, such as oxygen saturation, heart rate, body temperature, and other more sophisticated measures and make use of tele-health, thereby limiting the study visits and increasing the efficiency of the study. So more volunteers could actually take time and participate in the study. And so, again, enrolling faster third, we improved the connectivity really 24/7 to enable us all our scientists to say efficiently connected with each other, with the many collaborators that, that we have all around the globe and to their labs, so that they could, for example, run and mass spectrometry analysis remotely, which is you know, it was (inaudible) of, before only a few years ago.
The fourth element is that we ran systems in parallel instead of sequentially, which is the essence of design thinking. So as all the previous, elements that I just described to you were happening, our manufacturing facilities we're starting to ramp up at risk. And so instead of waiting for the trial results, they started to mass produce and assemble every element that that is needed from the vial stopper to the patient leaflet. Also at the same time, we work on our web structure for the gazing desc using automation and artificial intelligence, so that every key word could be analyzed for patterns and really allow a fast routing to the appropriate channel. And so this is how we could have a day zero launch with all system ready at the time of regulatory approval with such magnitude that is Marie Pierre was saying, it was unheard of before. Now,
there is a fifth elements that help creating this unprecedented speed, which is less on the technology side, that nonetheless absolutely critical. And it's the so-called rolling like regulatory review process so that the data was provided or an ongoing basis as it became available with really done with the intention of speeding the review process, during such such emergency. So this is how the timelines were accelerated without compromising in any way in our commitment to safety and really strict ethical standards. Thank you, Rachel, and for our participants who are joining us today, that aren't familiar with the typical drug development timelines.
when you talk about how you did it sequentially, can you also say a few words about how it's normally done so people can see where that time save is coming from? Absolutely. So it all starts in, in R and D. I think Terry is the one that described it, the best thinking about the best candidate. So once R and D is done with selecting the right molecule, then there's all the studies from phase one to do two three. Then there's the regulatory submission, all the discussions with the, with the regulatory and only at, at such point, really there's the manufacturing facilities that, that start the production and really all the supply chain kicks in for, for the final product will be delivered to patients.
And so instead of going from A to B to C, etc, we went all in parallel. That's, that's the major difference, Risky really bad. you know, there was this during this unprecedented times, really that's, that's the decision that was the right one to date. Nice thank you for clarifying it's. I think it's helpful when people see the speed, it's helpful for them to know what happened was that everything still happened the same way, but in parallel at the same time, instead of one at a time.
And, that explains that the time savings that we were able to achieve, Shri can you share some more about the technologies that, that shorten the timelines? Absolutely.as, several of us talked here, artificial intelligence and machine learning was a critical depart, in the early part, both in patient recruitment identification sites, as well as, as Ricardo talked about in the adverse events, looking at keywords and keyword searches so that we can really, compliment the human thinking around these things with, technology to really accelerate, quite a bit of, the effort which has needed to be done. The second area of digital technologies was the whole continuum of patient engagement. And we seen that, that has really exploded quite a bit and having digital technologies across that patient engagement continuum, help all of us collect data in a much more proactive manner, but it's also the flip when data was not coming the data surveillance, like what Marie Pierre and others talked about, the dashboards.
One was able to react quickly to what needs to be done. So the gentle push and pull mechanism was working, and that was enabled quite a significantly,\ with digital technology. And, these two, I would say in combination has really helped us engage more, specifically in the marketplace on value need to intervene and what you need to do. And I think that's the power of digital, the ability to know what needs to be done, and then to be able to quickly react to what needs to be done.
Absolutely. Terry, can you share with us some more of what you were doing at the onset for these timelines? Sure. I guess as already said, we, we did utilize, an integrated digital health suite. and, I guess that has been a tremendous really in terms of, as Shri said, getting the, the real-time access, and, identifying where we have any gaps, or not put that's probably one of the big, significant ones. I don't have any more to build on, on what Shri. That's great. So we,
we have these great efficiencies that we're seeing. um, let's talk a little bit more about the technologies that worked really well, and, and maybe also the ones that didn't and have a transparent discussion there as we're sharing. Terry, you were, you were talking about how you've reduced the timelines, you've improved patient continuity, you're getting daily data quality at speed. maybe, do you want to share some more about what technologies didn't work so well? Yeah, I guess, what, what, like, just, just to maybe talk about what, what worked well first, I mean, we, we obviously put significant strain on, on the platforms that we had given the, the amount of data coming at the speed it was coming at here, and, you know, the required quality. I think what the, the part that we underestimated and didn't work very well was the investigator and training needs on new technologies. So, so we put an awful lot of effort into getting the technology into the hands of the patients in trial getting it to sites.
there was significant lift needed at sites to help our, our, our sites come up to speed with, what is essentially a platform that had bought a web based and, mobile, capability embedded in it. now, again, not being critical of the technology in any way, but I think there's, there's a kind of barrier to adoption that, that we had to come over. We have done that.we learnt a significant amount and actually we're, you know, right now we're running our, our, 3009 study, which is the two doors study. And we're taking all of the learnings from that, into that. So we've, we've learned along the way, but it's been,
it's been a challenge to essentially recreate how we've executed clinical trials, and, run at speed. And at scale with, with tech and the tech hasn't really necessarily been the barrier. It's been the, learning curve that we've had to bring, our sites and investigators and staff on onboard. And the other thing I would say is that the, other significant, thing that worked well was the, digital health suite, really helped us in terms of the diversity recruitment. So we were watching the diversity numbers in very near real time. and we actually throttled, recruitment in certain areas. And,
in order to when we met our quarters in certain areas, we then drove after a kind of, some of the more diverse populations to ensure that we were meeting our exceeding expectations on that, which, which we've done. So it was really, really good to be able to see that in near real time, because in a typical trial, you know, you talked about the typical sequence, which is more of a series, in a typical trial, you're, you're looking, really post trial or post phase. You're looking to see how well did we do. We were actually looking right during the trial to see how well are we doing? Are we meeting our requirements? And can we actually change things now in order to meet our requirements, which we did. So that was, that was really good. And that worked very well.
I know you've asked me what did not work well, I couldn't really point a technology that didn't work well. It was, it was really the (inaudible) for, for folks to embrace the tech. Yeah, the learning is always something that takes some time. Did you, and you met you called that out as something that it was harder to expedite.
Did, did you manage to find any, tricks? Did you find anything really effective that helped to expedite the learning of, of the staff or of the teams and using new technology? We, we deployed in partnership with IQVIA we deployed, virtual study coordinators at the sites who were trailed up, trained up on the tech, and they helped significantly to, you know, bring, bring folks up to speed with, with the, the new mobile devices help with, the back office. In addition, we had significant, help desk support, and, and Shri can talk to the volumes of calls that were going through the help desk. you know, we had, we had significant numbers, but, but again, it was about having the answers quickly and being able to diagnose and triage, you know, issues very, very fast at site.
So we placed the folks at the sites to perform a role that in the past we wouldn't have had. So we would never have somebody that was the tech coordinator at a site. For example, you, you have your, your CRA's, you have folks at the site that are looking more at the execution of the trial, but that was something we did, we've learned from it. I think it worked really, really well. you know, we we've closed a database, which is obviously under review now right now, but, we're very happy with the content of that and with the quality of it. I, if I could add to that a point,Jessica, I think one of the fascinating learnings for us has been that the adaptation to some of these technologies, and I'm talking now about remote monitoring of sites. I mean,
some of it was just real life. People couldn't get in, or people were getting sick at the sites, and it's hard over estimate. We use all the predictive analytic technology to make sure the study is launched in places where people are going to get sick. Well, people did get sick,
but that included the site staff on the monitors. And so reacting to that and figuring out ways in which to digitalize and do some of the work remotely, including trial operations. I think that is in the end, going to allow for quality improvements and efficiencies over time. And those gains should be with us for the long run. The second point is, like my colleagues we had from the get-go, through our strong partnership with PPD, put in place the ability to look at real time metrics from the diversity and the patient characteristics.
We translated that into actually more transparency to the public. we were the first ones out there. There was a tremendous pressure to understand how these trials were performing and ensure that indeed we were recruiting the right populations in terms of diversity, minority people at risk for disease, not just infection and being able to provide the public a weekly snapshot because we had the data with that kind of frequency and immediacy, I think was tremendously reassuring to the public in a very strong, a stepping stone, if you will, to building trust in what it is that that we're doing.
So the immediacy of the data, and that enables a level of transparency. And I think that transparency then becomes critical to building trust with, with our key stakeholders. Absolutely. Anyone else want to chime in on the transparency and trust discussion? I'm sure that's something what Rick Keller and Marie Pierre you want to add too? Yes. well, we hope it was a little bit clarified when we spoke about the fact that we acted in parallel and the studies, really ran fast. And, and so there was a good reason for that, and we did not cut any corners so that, that should help then Durham, of transparency and trust in the pharmaceutical industry.
I was personally actually thrilled to see that the science was in the headlines. And for the first time in many years, the pharmaceutical industry was part of the solution. And,, and not in fact the, the problem, if, if you will, I don't know what the other think.
I guess it's, it's just a comment I'd make, on a more general basis. It's, it's amazing how the world has become so educated on clinical trials over the last year. you go to family gatherings, you, you meet friends and everybody's, I don't want to say an expert, but everybody knows what happens. They know it's about phase one, phase two. How are you, how's your data looking? I think that that in a way has helped. And I, I think people globally have come on the journey of clinical trials with us as we've all tried to, to cure this, this terrible pandemic. And I think it's, it certainly has led to building trust. And from a data sharing perspective,
we obviously have a big data sharing problem at J and J and Jansen, we, we, we share, we will share all our data back for academic researcher, et cetera. And, you know, I think it's important that we do that and we open up the transparency discussion, but it's, it's actually quite, it's, it's, it's exciting to have been part of this journey. and it's exciting that that, that the globe is, is, is rooting for us in, in the main and, and that people are, are, are very understanding now of what it takes to, to actually go finding a problem and finding a cure and getting it into the arms. I see some of the questions and answers. People are asking why it takes so long to get it into the arms. And, you know, it's, it's a very, very complex supply chain problem, to go from development, to manufacturing, to actually supply and distribution. And, certainly there's nobody dragging their heels on this, but I'm very excited.
And the transparency is the transparency relationship with the public is really just getting started because the post emergency use commitments are going to be asking many of us and many of your participants in the public when you get a vaccine to be part of studies, to continue to follow these emergency use authorizations, and continue to share data and continue to be part of this ongoing research in these, in these post-approval commitment studies, Shri, can you share with us also what, what you saw and what worked well and perhaps even what didn't from your side? Oh, that's a good question. And as a chief digital officer, I would say technology work, but I actually, technology had to be complimented with humans, as Terry was talking about, you know, whether it's AI or whether it's a digital technologies, you do need that last mile touch and doing it. And one of the things we realized in the scale-up is how do you bring the human and technologies together complementing each other so that he can make sure that this delivers the promise, what it's designed for.
So that was one big lesson we learned. And second is, as you start to scale at this, volume you know, we're in a regulated environment. And of course with patients and consumerism, people are very used to, what we call a B2C model, Amazon, or Google or others or Facebook. And so, working in a tech within a regulated environment is also one where you have to go and educate and saying what you can do and what you cannot do and how you can do it, but making it easy.it was a critical component of the positives, but also making sure that we're doing that in a very regulated and secure environment was a critical component. So it's digital with human, enabling both in a complementary manner and making sure that, as we're doing this fast, we're doing it in a very regulated environment so that we can make sure that what we're collecting is appropriate for submissions.
Thank you, Shri. So talking now about just the data we've been talking about the technology. Now let's go a little bit deeper on the data that's collected by that technology. How, how for those,
those participants on the call that, that aren't from the pharmaceutical industry. can you also share a little bit about how the data itself is used, the data that all this technology is gathering and empowering? How has that data helping us to overcome risk and address risk and ensure the safety and the speed in parallel. Sri? Would you like to start? Yeah. there are several aspects of technology, Vitra which was used, especially data second point, right? We talked about really using data upfront, in, recruitment and understanding that target population that required massive amounts of real world data to figure out what is going on as well as appropriate feedback on data and datasets. And then as we start to continue in building the data, surveillance, there was a massive set of data coming in, almost on a daily basis on patient engagement, patient behavior in the trials, which had to be constantly looked at, and that was real time data coming in.
And then as we go into the adverse effect models right now, both post marketed, but also, during the trial being collecting massive amounts of data, on this. So I would say that fundamentally, these trials have bullet leverage data to recruit and do things better, but also are the beginnings of collecting all this complex set of data to make sure patient engagement as well as safety as taken care of. so I just wanted to say that this is being almost fundamental that the digital created the data platform, which then can now be used and leverage for, you know, really a better understanding of what's going on with all the vaccine trials.
Raquel would you like to add to that? Yeah, so. Multiple challenges really, that we, we encounter were overcome, overcome with, with data. So one, COVID related challenge was actually making sure that all the other clinical trials would not be stopped for example, that, that we have running, and that the supply of our medicines for patients would not get disrupted in any way. And so the way we use data and technology was through a system that we have it's called control tower, and really we have a fully automated, visual analytics of all our studies all around the world. And so we predict recruitment as well as we're able to amend the studies with when needed. And so we were largely, really able to keep them running.
And also we were using our own databases to establish which areas were critically affected by COVID and make sure that the supply of medicines, some of, some of them in fact, are life saving drugs would not be disruptive. And other challenge that we face what was with the electronic, patient diaries and the fact that especially the less tech savvy people let's remember about, a quarter of them, were over 65 year old would not necessarily respond to, the super techie reminders. So we implement that one, an extra level of care to enforce their, their response, but also as Shri was mentioning.
We actually added at times the human touch to really compliment the technology, when, when needed. But I have to say the, the app side of having so much technology embedded in our trials is that we have an increased real-time digital monitoring. as you were mentioning just of the post-approval safety and effectiveness of, of vaccines which is absolutely unprecedented. Yeah. If I could add to that.
so we talk about, the tremendous data richness that we had during the trials. And I think you've seen examples of all the companies converging really on, on modern tools. And I think all of us struggling with the same elements of the more tools you deploy, it's actually easier to deploy a tool than to teach somebody how to use a new tool.and so, that quickly shifts to being the bottleneck and as was mentioned, you know, the more fancy the tools actually now you have to contend with the diversity of people, some of whom will find it easier to use a tool than others.
So using an electronic diary versus a paper diary, sure. The electronic is easier for us, but for many people around the world, actually putting Xs on a piece of paper is easier than, then finding the app on their iPhone. the real challenge though, I think is, is the one that we're facing now and, to come, which is the massive amounts of data and,, Shri alluded to them. We're working with his team when looking at safety. So in our trial, we had about 30,000 participants analyzed deeply, you know, in a, in the context of a clinical trial with a lot of, of investment on every individual, to make sure we get the data right. Well, as of this morning, there's been, you know, almost 27 million people just in the United States, dosed with our vaccine know we were approved, what less than three months ago, that's already a thousand fold increase in deployment.
The number of people dose with Pfizer's vaccine is similar. It's just above 28 million, that's just in the US. And so we have to adapt how we're looking at information now to make sure that we understand signal to noise and that our data capture systems are actually up to par.
And it's really become interesting because we're doing it in a world where everybody is tech enabled and connected in a way that was never that way before. Right.so we have to understand how to, how to analyze our signals. When the funnel for finding signals is suddenly much larger, much more furious, much more intense, and much more connected through social media,than it ever has been before. And I think these are some of challenges that lie ahead of us, where we actually have to catch up with our technology and our, frameworks for looking at analyzing the data to where the public already is ahead of us in this regard.
Yeah. Tell you said that, well, we have to catch up with our technology. that's something that we hear from, from so many pharmaceutical companies and manufacturers that we're using new technology, but we're retrofitting our old processes and we're trying to do the same things. We've always done using new tech and that clash isn't is creating a complexity and it's not shortening the timelines and it's causing barriers.
And the challenge for us as an industry is try to do new things with the new tech instead of the same old ways. We've always done it. and maybe, can you say a little bit of, of what you recommend to organizations who are looking at just a much broader signal to noise ratio then they've ever seen before? Well, when there's a lot of signal, then people talk about artificial intelligence.frankly, I still struggled with the non artificial bit of it. and I think as we intake these massive amounts of data, I think we will learn how to better process it. And I think some of the emerging tools of natural language understanding, etcetera we're, we're looking at ways in which to leverage them. But I think these are still early days, because we still need the, a significant investment in the non artificial intelligence to shape those tools and help guide them to where, to where we need them to be. Now, that being said,
one of the phenomenal, I think truism of, of our day and age in this domain is the acceleration of pace. So what looks like a lot of work and a long time, I actually expect that with the right amount of investment upfront. And we certainly had Madrona are, now very actively engaged in looking at this. we should be able to improve our tools, and use modern technology in that way to accelerate our learnings compared to what we've done in the past.
Looking forward to sink more of that. And Marie Pierre Can you share with us a little bit of how you're approaching the data challenge thing, the speed and the scope of the data at, at Pfizer, you mentioned, you know, internally you you've set up whole new processes and dashboards refresh every four hours, which is incredible for a global Pharma company, but also how you're, looking at that increased amount of noise for those signals that are so important to find. Um, I go everything that was said before by my colleagues, I think, what aspects that we really leveraged was the use of real-world data to accelerate the development of our clinical trials. it is environment and as Terry and others were saying earlier, one of the key questions we wanted to answer was, where would be COVID-19 when we would start our Kinko trials and conduct the study. so we, again, we, we use some of the dashboard that we had created in the spring, when the pandemic really hit Europe and the us, and we were, um, following the global spread of the virus to really understand how it affected activities at sites in our clinical trials globally. And, we were using various sources of information including, the confirmed cases, the mortality rates hospitalizations, and the use of ICU at the, not only at the country level, but also in the US at the County level.
And we were also tracking information like travel bans or, government restrictions, or whether institutions and size allowed patients. This is our recruitment or even onsite monitoring. And so when we started out planning for phase two, three clinical trial for the vaccine program, we leverage all of these information, which was updated daily, up to weekly.
And we all violated with additional information that included population density, social economic scores, diversity scores, and even in the summer time civil unrest. And, we use a combination of actual effect weights that were reported, and also epidemiology modeling, like predictive analytics to look at, the forecast within the next six weeks of where the Audi attack rates would be changing in the US and the other, other countries that, where we were conducting our study and all of this information was used, at the beginning of the trial to really select the countries and the sites where we wanted to conduct this trial, but also doing the recruitment period to decide whether we needed to, slow enrollment in some region, and boost the enrollment in other regions. So, a lot of information that, is available from very different sources internal and external, and then putting all of this and analyzing it as quickly as possible to make a fast decision-making. Well, thank you, just to build on what Marie Pierre said, we've used, we've used mobility data, which is actually, you know, from your mobile phone, whether you be on Google or Apple, to look at traffic patterns and look at when people were moving around and where they're moving around, we could predict, areas that were moving into lockdown or otherwise.
So it's actually very interesting to start looking at, data that you talk about everybody is connected today. You know, that data is available out there, all albeit anonymized, but very, very helpful for us in terms of understanding, you know, being able to look at the case of Melbourne when Melbourne went into lockdown, we were able to look at that model and say, okay, well, there's, there's how the mobility moved. and, here, if we see it again in other areas that we could predict the same. So there's some very interesting data that you can now start to triangulate into the space. And obviously it's for a pandemic. I don't think we're going to be worried about patient movement in the future, but it's, it's caused us to think in very different ways around data and data that's available to us to try and help solve problems around where we're going to see people and find patients.
Yeah. When we, when we looked at where the pandemic was going, we actually looked at two, two algorithm in parallel. One was by the private industry. One was actually a government NIH sponsored.and so we, we, we were constantly monitoring both and the places where they overlap, we said, yep. That's where we go. okay. So let's go to a question about how do we keep these innovations? So what, what lessons, you know, everything you're sharing is, is historic and groundbreaking and, and is the best example of how technology can expedite research and expedite, expedite life saving vaccines, and how can we take these lessons? and, and what do you think will stay with us? How do, what, what lessons are you taking away from this experience for the future of biomedical innovation, Marie Pierre let's start with you. Well, so to begin with, I think we commend the FDA EMA and other regulatory agencies for really developing emergency guidance that enables us as sponsors to clinical trials during the pandemic. And for example,
we use remote monitoring extensively when travel was restricted and, monitors could not visit sites and also to go fast on for on-demand monitoring to meet accelerated timelines and ensure oversight of patient safety. And, and when certainly EMA has released an updated guidance that actually expands the scope of remote access of source data review and verification. And it's really a positive step forward for our ability to effectively manage trials when working now, a policy priority should be to update permanent regulatory framework to support instead of digital innovation, so that we can apply all the lessons learned from those COVID-19 trials to other therapeutic trials, and also globally through how many physician initiatives, it was amazing the interactions with the regulators, they were traumatically accelerated the review of the protocols, scientific advice. They were killed almost in real time.
And the virtual meetings, the regulators willing, enabled, like in decision-making. And I think that we need to move beyond COVID-19 and think about, every adopter, particularly in particular life threatening diseases, where we need to be able to collaborate with regulators in a similar manner. And we talked earlier about, voting submissions. so that's what we did to vines health facilities, but a great improvement we would be for regulators to review the same data in parallel to award submissions to different health facilities, right? So in fact, if simultaneous collaborative reviews by multiple regulators, they will not only bring back food to patients faster, but it will also help regulators in decision-making by learning from each other. so I really think that, fully embracing digital technology is absolutely critical to access speed and quality, and we saw amazing benefits, through our trials. And there is no reason that we should not be able to use this experience to catalyze changes in, in clinical development, including in the regulatory space.
And if we could have a more fit-for-purpose approach that serves patients better, I think your call for simultaneous collaborative review by regulators is something where we all will take away from this call. Raquel, So the big lesson for me, again, going to the same theme, or was that when we're all motivated and form a United front governments, regulators, Pharma, industry academia, we can accomplish amazing results. And I think Nelson Mandela said it best. It seems impossible until it gets done. And, really we, we got it done. And so that's the mint of the value of January and collaboration. And now that reflection for me is that we get lots of pushback when we want to disrupt the status quo. For example, we've talked for years about online,
Product labels or bringing the point of care to patients homes and nothing changed. And, you know, the silver lining of this pandemic is that COVID-19 was the biggest disruptor and accelerator of all Dines. That really forced us to look at alternatives, smarter ways to work out an issue. And I think we're at a point of no return. And once we see how efficiently we used some of these elements, we will probably keep it.
And then maybe the last point somebody mentioned that is how do we imagine the world post COVID-19? And it's something that at AstraZeneca we're really passionate about. And in fact, we were one of the first entities sign, the United nations global compact statements, which is truly urging all the governments around the world to align their COVID economic recovery efforts with the latest climate science. Because ultimately what we want to do is to operate in such a way that recognizes the connection between the business growth, the needs of people in society and the limitations of our planet. So that, that will be our next big challenge. That is indeed a global challenge for all of us. Terry, I guess just on a, on a broader.
Just outside of the call, the trial. I I've been leading the, the clinical relaunch for Janssen and looking at all of the other trials that we're trying to, progress through the pipeline as well. And I think, you know, the fact that we've gone to direct IP shipments, so we're shipping drugs direct to patients' homes that we've deployed home health nursing that's been mentioned already, you know, care to the home and telemedicine is now fully embraced at this point, whether it be, you know, at the sites themselves using their own technology or whether it's sponsored, provided, I don't think any of that is going to go backwards. That's, that's, that's there to stay in and it should be, it would be great to see things like econ electronic consent embraced globally.
I think things like GDPR and not getting into political discussions here, but GDPR can be a barrier, to, to, to progress. And we see a lot of resistance in the EU in particular to, to electronic consent. Whereas the, the us is certainly embracing it in a bit more progressive and that's eight, so it'd be good to see movement there. and I, I maybe would say something that, you know, just, just, just one point is that the higher cause really, such as the global pandemic has meant that we we've really, in J and J certainly we've, we've challenged our, our conservative approach to risk and engaged in a really good discussion to, to destruct, really firmly held beliefs in terms of how we've executed in the past and to allow us to go faster.
but it's really important to note that our J and J colleagues, our partners, the regulators, the governments have worked tirelessly really around the clock globally through days, nights, weekends, to deliver this vaccine. So I think it's not really realistic that we can shrink the time of drug development and delivery to one year for all trials. That's, that's not realistic because, you know, I don't see any company being in business if you're going to manufacturer at risk based on a phase one. because, you know, it's just that that's not going to happen. I'm talking about manufacturing at scale. You know,
obviously we manufacture for our clinical trials. you know, there, there is a process to follow. certainly we've learned a lot and I think there's a lot of things we can be doing in parallel. If, if we certainly got more engagement from the regulatory authorities on rolling submission and,, on parallel submissions that would re that would be very exciting, could reduce the time to market for, for drugs globally. But, there's a lot of learning here. I think there's, we have, we have a motto of, of no, no, going back. and,, and our hope is,
is that we don't go back in areas we've, we've taken. Significant steps forward, but certainly our teams do need a breather. the teams that have been working on the vaccine are, are, are flattened have been flat out for over a year. I'm sure it's the same in Pfizer, AstraZeneca and Madonna. And,, you know,
we're, we're now looking at how do we refresh those teams, give them a break and get them back to some semblance of normality, but normality will not be the same as it was before COVID and that's exciting. No, that's such a good point, Terry, that all of your organizations have really been on the front lines, fighting a war, in the trenches and that's taken their nights and their weekends and their holidays and their time from their family. And it's been a tremendous, collaborative sacrifice, that the industry has been able to meet these timelines. Shri, can you share with us some, some of the lessons to take away as well? Yeah, absolutely. so you know, I you're all in a new norm, but what I wanted to say is change is here to stay. And if you really look at digital technologies and adoption, it's all about change and change management. And I think that that,
has now front and center, you know, there's a change going on with regulators has been now there's a change going on in internal processes on how we are using it. And to your point, changing those processes, not adapting technology to an old process, but going into the new one and more and more with, all my colleagues talk about the data and insights coming in, how do we rapidly use that to be much more targeted and specific? So the audit a new norm, but one of the biggest impediments and the success factors has changed, how do we adapt to change and adapt change is going to be most critical to see how we're going to survive in the new norm. So really exciting times. And, but, but it's not about technology, it's about change and how to be managing. So that's,
the key takeaway for me with all of this trials and acceleration. and the trials. Thank you. Jessica. I think the most important technology that, that we, we, failed to mention is the fact that we're so productive because we all work remote. So tele anything has been acceptable. We're all here in a VTC.
I'm able to join a wonderful panel in Europe, and I'll still make it home to lunch with an almost zero carbon footprint and no jet lag. And, I think it's made all of us a lot more productive, and I'm sure that there are very strong elements of that that will, we'll be here for the long run. Thank you. So we're getting some really robust, comments from the audience, many of them thinking, and current congratulating, you and your teams on the work that's been done, noting the amount of evolution and organizational change that needed to happen inside your companies to make this happen and, and the big steps forward that that delivered. we're also getting questions about how tech will continue to drive delivery and care of these vaccines that last mile and how it will be able to, enable a more continued focus on, on diversity and inclusion. could,
could you say a few more words about, how we focus mostly on how technology has been used in development and speed and change? Let's just spend a minute now, before we get to the end of our time together on, on technology that is used closer to the point of care for the patient. That's a challenging one for us to envision given that most of what we do in our industry is, is so centralized. we're thinking about ways in the distant future.
Maybe not so distant future where actually a distributed manufacturing will get products better. We certainly have applications that are fully personalized. We're working on a personalized cancer vaccine.
That would be very much tailored to each individual patient. but I don't think we, as an industry in our collective mindset are quite there in terms of the distribution models, if you will. And I, I, maybe I just add the distribution model in this case, it's a very different model than normal. And obviously look, I, we, we deliver right now to distribution centers, and the governmental agencies will actually handle the distribution from there. And I'm not, I, I'm not saying anything about, they obviously have challenges in terms of making sure that gets rolled up, but we're not, we're not engaged in that. So our typical model is to deliver to,
you know, the pharmacies to the, to the hospitals, to wherever, but in this circumstance, we're actually selling large volumes of vaccine to governmental agencies delivering to distribution centers. And the furthest distribution is from there. I think the question really is, is better pause towards the governmental agencies then ourselves right now. So not, not trying to Dodge the question, but we're not responsible for that right now. Well, and I think that's the question then we'll take up in the next session of this series hosted by the foundation. So we'll go to one other question that's coming in from the audience that I want to cover is a little more personal. What does it meant to each of you to be part of this, remarkable experience in your career? If you could say a few words about what that's meant to you professionally or personally, to be part of seeing the technology available today, actually be used to deliver such value right now.
So let me start, Oh, sorry. No, go ahead. So no, I would, I wanted to start on the personal side. I actually lost a relative due to COVID and I know many families that have been devastated by it. So I'm extremely proud to be part of this effort to bring hope and healing to so many, as a clinical. Scientist, it reminds me every day or the simple truth that we are never done learning and we have to follow the science and really keep up with the innovation, you know, catching up with the data that, that was a good quote from one of my colleagues professionally. I think it's,
it's been the biggest adrenaline rush to lead a world-class medical team during this diamond and seeing really the accelerated evolution of, of healthcare right before my eyes. And, you know, when they say that a good leader has to be comfortable in uncomfortable situations, well, I think this was the ultimate test for that. going back to what you were mentioning, Jessica, of course the work is not over because truly the value of the vaccine is realized only after it's been injected. And so to me, ensuring a broad and equitably, an equitable distribution and really building trust and partnerships with, with the underserved communities will be the next critical frontier for us. And maybe, you know, the, the message for me would be, it's great to have all this technology, but, nothing substitutes in this case, the human, the human touch to really change the beliefs and the behaviors. I'm happy to go next, Jessica. Cause I think we all,
we all have a version of that for me personally, it's, it's a tremendous sense of, privilege and humility to have been, played a part in this, coupled with as immense, a sense of responsibility given, what we know our technology can enable and now bringing it to bear, to actually, have an impact and,,you know, vaccine development is usually impersonal because you never know who's going to benefit well in this case, you know, this is personal, this cuts close to home. I've seen my mom, who's 80, get a vaccine and MRN a vaccine. I've seen my daughter-in-law, who's a physician in the front lines in New York city get vaccinated. so this one really cut home.
And I'd say the last piece is just the, the opportunity it's given me to meet and collaborate. So productively with so many people. And I'm not just talking about, you know, the people in the company, but, NIH, government officials, regulators, investigators, people just out there in the world who pinged us routinely to ask questions or tell us about their experiences. It's just been a tremendous sense of privilege to be part of it. I know we're probably short on time, but I just go, I mean, the level of collaboration that I've seen across industry, I've seen a share, you know, put the IP aside, share all of the insights, what we're seeing share, you know, trends share, you know, all of the stuff that can help us, not only get the COVID vaccine, Run, but also help the ongoing clinical.
Trials. And it's been, it really has been immense. And I, and I hope that continues really for myself personally have been effective from a family perspective. but very excited to be part of, of contributing towards this, this greater culture. Thank you Terry. Shri Yeah. As a, you know, digital officer,
you could be in many different industries, but I'm more humbled and privileged to be here. And so forwarding such an effort. Cause all my colleagues said, this is unprecedented at times and, and being part of the solution rather than part of the problem is a great feeling and in a more, more to be done as everybody said, and a long way to go, but really humbled to be a part of that journey. Thank you, Marie Pierre? I agree. It's been incredibly inspiring journey, deeply satisfying experience to make a difference in people's lives, to be part of a breakthrough joint, this pandemic.
And we're so very humbling to think, how to work every day, could save so many lives and to see people being able to return to work, kids, to go to schools, families, to reunite and to bring some normalcy back to the world. So this, this is a development story that has no pattern in history. We will, it took on what nine months we'll need to bring the solution to the world and it will have a global impact, not just on public health, but economic recovery and, and also social connectivity. And I couldn't be more proud of, of, all of the people who really rose to the challenge.
And we worked at the speed of science. I really like what Terry was saying about how flat have the teams are and how much we need to refresh them. they, they've risen to the challenge. They work at the speed of science to deliver this vaccine, to match the needs of society. And when the world needed science
the most people really stepped up. So it was very, inspiring. And with that, we have reached our time for today's session.
So I would really like to thank all of our panelists and the organizations that you represent, for the immense efforts that you've put into responding to this horrible unwanted catalyst that, you know, we, none of us wanted this to happen and the way our organizations and our world had to respond and using technology to achieve the impossible, has been as has been amazing. And the fact that you are all here today, sharing your learnings and what worked and didn't work. This is also part of that continued collaboration that, designed to make us all stronger moving forward. So thank you to all of you panelists, to everyone who's joined us for this hour, of course, to our translators and to Bruno Cohen and the Galien Foundation, a recording of this session will be made available. And of course. We look forward to welcoming you at a future Galien session.
Thank you and goodbye. Thank you for your participation. And please note the next Galien forum and Galien award ceremony to be held on October 28 in New York City. Looking forward to welcoming you to celebrate the golden Jubilee of Prix Galien.
2021-02-19