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Pulse Infoframe | Femida Gwadry-Sridhar, Founder & CEO

Speakers

Dr. Femida Gwadry-Sridhar

Dr. Femida Gwadry-Sridhar

Founder & CEO, Pulse Infoframe
Pulse Infoframe has developed a robust platform for accelerating evidence generation and collaborative research through quality real-world data (“RWD”) at scale.

Femida Gwadry-Sridhar  0:01  
Thank you, everybody, it's really hard to be a follow up back to Dr. Joyce, because it's like everybody's left and gone to the after party. So thank you so much for staying. Let me tell you a little bit about why Pulse Infoframe was started. Early in my career in the Faculty of Medicine. What I saw was everybody who had data, clinical trial data or any other data, we were really not sharing it. It was in people's pockets on their thumb drives. And they were so afraid to let it go in case somebody else published ahead of them. And what I really wanted to do with Paul's was to start to democratize the data, but do it in a way that was transparent, so that people knew that if they shared their data, it wouldn't just disappear, and in fact, really create a new paradigm for collaboration.

So what do we know so far, we know that Pharma has a critical growing need for real world data. Most of the data today have low quality often, or the data are too narrow because they come from different data sets that may not align with what the disease interests are. There are errors in the data that there's no way to correct in part because the data is static, and you can't go in and manipulate them. And finally, the most important thing is that many of the datasets today do not have patient reported outcome measures. They don't consider the impact on patients. And that's critical as we look at value based payments and value based outcomes. So essentially, what happens is that pharma waste time and money trying to piece together multiple different sources, and therefore face development, approval and reimbursement risk.

So what do we do at Pulse Infoframe, we provide pharma the data that they need, across the entire development cycle, creating an evidence generation platform, what we do is we build patient registries. And we build these patient registries, considering the clinical data, the patient reported outcome measure data, the biomarkers, etc. And what we do is we build very thoughtful data dictionaries before we even start collecting. Because collecting data is not just about being opportunistic, and collecting whatever is out there. It's like a recipe. If you collect ingredients, and you put them in a basket, if you don't know what you're going to do with them, they're not going to result in anything meaningful. And so this is very much science driven. The data are then curated from the sites that we work with, and include patients of interest so that you don't have that loss of signal. And ultimately, they have to be live and regularly updated. Because medicine doesn't stand still. They include patient reported outcome measures so that you can actually continue to see how patients are doing over time. And most importantly, these data are mapped to an FDA and EMA ready standard, which is an ontology called OMAP. So essentially, we have a single source of complete high quality data within one platform. Currently, we have 18 disease registries across 80 sites globally with 23 pharma customers, we started in oncology, went into rare diseases and continue to expand our platform supporting chronic disease. You heard a number of presentations today about dialysis, kidney disease, heart failure, and the challenges that these patients have, you might ask yourself, why rare disease, there are over 7000 Rare Diseases, one in 10 people have a rare disease. Three out of 10 children with a rare disease die before the age of five, very few drugs are on market and are approved by the FDA. So what I would say is rare is not that rare. And the reason we did rare and rare oncology and oncology is that by having a platform, we're able to support the needs of many.

Our approach is unique, where we don't just look at existing data, such as electronic health record data, or pathology and claims and billing. That's important. There are important nuggets of information there. But what we do is we have novel data, I describe to you that we build these data dictionaries. And that's important because they address the questions that are of interest. We also look at existing data sources from other patient registries. Remember, I started my story by telling you everybody has a little bit of data that they've stuffed in their pocket, we need to find those data and make sure that we're able to ingest them and bring them in and use them so that all of that knowledge is not lost. patient generated data through patient reported outcome measures, etc. All of these data come together and are put in our platform. And they're harmonized, curated, and normalized. And ultimately, then we bring value to each of our stakeholders in the ecosystem, three different end users, pharmaceutical companies and biotech key opinion leaders and advocacy. Each of them have value, and they have to have value because no single party can have all of the value that comes from this.

The platform is also very intuitive. And we spent a lot of time and effort on user experience. And why is this important? Because patient engagement is not just something you say you do, you have to actually do it. And to do it. You need to understand what patients need from their perspective. And so what we do

us, we make sure that all of our end users get access to data in a de identified way, aggregate with analytics and visualization tools so that they can truly understand the value and insights and can respond to it. And that's very important.

We have traction with a committed blue chip, customer base, major pharmaceutical companies, biotech, emerging companies, advocacy groups, as well as major academic centers. We have some momentum indicators, nine years plus of longitudinal data, multi year contracts 32,000 unique patients, which amounts to about 150,000 patient years, we have support for pharma throughout the drug development cycle, collecting data, real world data to support natural history studies, and pulling the data through so that when their drugs are approved, we actually have the data to support pricing reimbursement. And all of this now is done in a single platform so that the data are not siloed disease by disease. So we're able to create across diseases.

What is our growth strategy, we're expanding our existing disease registries, because once it's built, we're able to deploy it in the cloud globally. We're GDPR and HIPAA compliant, as noted, and that's important, because most of these patients are not just siloed in North America or Europe, they're all over the world. We're cross selling within our existing client base. Many of our customers in pharma biotech come to us for multiple diseases, and many of the academic centers we work with, they also are working with us across diseases. We're expanding to adjacent diseases because we're able to leverage data elements that are common amongst diseases. Our business model is software as a service data as a service. And then ultimately, our sites are available for real world data studies that are both longitudinal, and could be retrospective or prospective. We also have some very strong and impressive partnerships with our partners in France Quinten, who provide us with AI quality metric, the founders of SF 36 Cytel, who are now private, equity backed and have very, very strong methodological expertise and the Critical Path Institute, through our channel partners chips, we continue to expand our footprint.

we're uniquely positioned to capture a growing market, of the 1 billion plus annual real world dividends, market evidence requires high quality data. Data does not just automatically translate to evidence.

We have the richest most mature data and a clear therapeutic focus. So again, I posit this is not just about how much data you collect, but the meaningfulness of the data and maturity and the richness. We have a really strong team, we have a strong senior management team who have been at many other companies, scale technology companies, and come from healthcare, our advisory board have been founders at other data companies, and also have exited several companies and bring a breadth and depth of experience that is really helpful. As we continue to scale.

How would we were raising $10 million and how we're going to use the funds. You'll see on the left hand side, we have some strong growth catalysts, which include a large tam data enrichment over time as we continue to grow and scale. But most importantly to our platform is science lead. And it is a state of the art platform that allows the automatic curation of data and mapping without the risk of human intervention along the way, not that human intervention isn't important, but it has to be aligned to where it's most necessary. And that's along the data science, where we need to make sure that the data that we're getting out is relevant to what we want to answer. I thank you for your time and if you have any questions, feel free to reach out to me or my CFO who's with me in the audience today. Thanks so much.

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