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Unlocking the Power of Real-World Data, A Conversation with Gadi Lachman, CEO and Founder of TriNetX
73 Plays6 months ago
Gadi Lachman, Founder and CEO of TriNetX, joins Frederic Laurier, Managing Director at Alantra, to delve into the essential role Real-World Data (RWD) plays in revolutionizing the healthcare industry. From unraveling the essence of RWD to illustrating its tangible impact through real-life examples, Gadi showcases TriNetX’s profound influence on pharmaceutical companies. TriNetX has continuously expanded its data repository while optimizing its customers’ clinical trial success, from protocol design to site selection, to patient recruitment.
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Transcript
Introduction to Gadi Lachman and TriNetX
00:00:06
Speaker
Welcome to another episode of Crossroads by Elantra, where we delve into the forefront of digital health. In this session, we have the privilege of hosting Gotti Lachman, the CEO and founder of Trinetics, a pioneering force in real-world data repositories for life sciences and healthcare.
Transformative Potential of Real-World Data in Healthcare
00:00:23
Speaker
Gotti will lead us through an in-depth examination of real-world data, or RWAD, explaining its profound impact on healthcare practices. Through compelling examples, he'll show us how this data is just not informative, but also transformative. Join us as Gotti shares insights into TriNetX global expansion journey, navigating the intricate web of regulations across different countries and the impact of AI. I hope you find this interview as ah enlightening and insightful as we did. Welcome to Crossroads.
00:00:54
Speaker
We are delighted to host Gadi Lachman, founder of CO and TriNetX on today's podcast.
Gadi's Career and TriNetX's Clinical Trials Solutions
00:01:00
Speaker
Gadi, thanks so much for carving time out your busy schedule. Hello, hello my friend. ah An honor to be on your podcast. Thank you for having me. Thank you for for having accepted. For the benefit of our listeners, Gaudí, can you quickly walk us through your well'll walk us through your bio? But after that, could you walk them through what trinetics is about? Hopefully I got most of your bio right. You were born and raised in Israel.
00:01:25
Speaker
you came to the u.s for your nba right around september 11th if i'm not mistaken pretty timely unfortunately after a stint in investment banking you move into the corporate world with a special focus on health care you. Before founding trinetics you worked at the likes of trisetto eliza and amwell again probably best to hear it from the horses mount so could you give us a short introduction on trinetics please.
Global Clinical Data and Drug Development
00:01:51
Speaker
Absolutely happy to so trinetics. A 10-year journey to build the world's largest platform for clinical trials, solving really the massive problems that could be done much better with access to global data and to very sophisticated way to generate insights from that data and software, topped with really, really smart people that can even help you ask the right questions. so
00:02:25
Speaker
Pharma industry, I don't need to say much about that. If you look at a large pharmaceutical company, they basically have two major businesses. One on the R and&D side where they develop new life-saving therapies. And then second, once those have been successfully completed, where they market them and, uh, and you can see what's working better, what's working less and and make adjustments to save patient lives. So tranetics was formed to democratize the world's data, to bring a massive, massive clinical global data set and access to patients to help pharma companies.
00:03:10
Speaker
deliver the development faster, cheaper, and with more quality. So to do a better job on the R and D side of their business. And then once those drugs are live and saving people lives to help pharma companies understand how their drug behaves in the real world with many more patients, many more countries than they were tested in. So this is Trynetics in a nutshell. So maybe taking a step back, you you did he just touched on real world. Can you describe to the audience in simple terms what real world data is all about, how it has evolved since the foundation of the company some 10 years
RWD vs RWE in Healthcare
00:03:49
Speaker
ago? And can you also shed some light on the differences between RWD and RWE, which stands for real world evidence? Shall we think of them as simple synonyms?
00:04:01
Speaker
Excellent. So I never heard the term RWD, real world data, in my life until I joined this subset of healthcare vertical, ah which is the life sciences industry. So what everybody else in any vertical, especially they also in healthcare, just calls data. In life sciences, there is data and there is real-world data and data is historically and mostly what's collected during a clinical trial or in a test for a new drug or new therapy. And then real-world data is what everybody else in healthcare calls data, which is the data generated from everyday care, normal people,
00:04:45
Speaker
any facility, any interaction with the health system. You get sick, you visit a hospital, everything that happened to you there. That's what this industry calls real-world data. I'm not sure I'm going to do justice to the difference between the D and the E, but really realistically, one is the data, the raw, the core asset itself, and all those facts that are being generated when humans interact with the healthcare care system. Evidence is what you can infer from the data what you can analyze the data what you can learn from the data is historically called evidence but for all intents and purposes is how what's happening in the real world with real patients and real medications and real surgeries and
00:05:30
Speaker
procedures and everything. How can that asset be helpful in the life sciences, life cycle of development and and marketing and selling of drugs? That's the industry of real world data, real world evidence where we operate. And terminology is just that, right? But what we care about is how this data can help, which leads me to the next question.
Real-World Data in Drug Research
00:05:52
Speaker
Can you explain what the main use cases are for your various data sets and perhaps provide real life examples and now they have positively impacted drug research and or patient outcomes?
00:06:06
Speaker
This is a great question, and I would answer everywhere. So I'll give you just some very specific examples. On the pharma R&D side, where they develop new drugs and therapies, and then on the post-approval side, where those are live in the marketplace, and there's a lot of insights that need to be generated there. So on the R and&D side, and I'll draw from the experience of genetics. We help large pharmaceutical companies and obviously support large CROs mainly on the R and&D side in the following three areas. So number one, feasibility and protocol design.
00:06:40
Speaker
We with our data and with our analytics and with our people help pharma design better and more efficient protocols that have inclusion exclusion criteria that on the one hand answer the scientific and commercial question of what needs to be the purpose of the drug, the label, everything that you need to hit your target for. But then on the other hand, maximizes the catchment, the amount of patients that are eligible for those studies and doesn't reduce them for the wrong reasons. So when pharma companies, and we became the factor of the way pharma is designing protocols, almost all large pharma and almost all large CROs, when they design the protocol and do their feasibility on the Tranetix platform,
00:07:29
Speaker
They maximize the inclusion exclusion criteria so that the largest amount of patients could be eligible for that study. These have massive implications down the road when pharma is coming to recruit those patients. Those studies are more successful. There's another big thing with protocols. They often get amended. Every time you amend the protocol, it's a setback of anywhere from six to 12 months in the development cycle of their drug. One last farmer told us and actually announced publicly that since they started to make all their protocols be written on the tranetics platform as a gate to becoming live protocols, they have reduced the number of amendments by half. So this is a massive, massive impact on the time it takes to bring a new drug to market faster. So protocol design feasibility is one very important area.
00:08:21
Speaker
where RWD impacts drug development. Second one is site selection. So we have hundreds of sites in more than 200 countries all over the world, from the US to Latin America, many countries in Europe and many countries in Asia and Africa. So we are helping pharma go to the sites that have the most patients. that are wanting of those studies and they will respond quickly, they will accept those studies more, they will find the PI, and all that process is just going to happen faster with sites where it's going to be more successful. I have a lot of examples there where pharma is awarded
00:09:03
Speaker
their studies to the tranetics network sites, those usually are being accepted within two or three weeks as opposed to the industry standard which is two to four months. I will also say that even a quick no is very valuable because nothing happened, nobody wasted time, all good, move on to look for other sites. There's another big difference with us which is Those trials are being awarded and offered to the clinical trials offices at those sites and they're finding the best PI for the job, who works at the site. So from a business standpoint, those relationships extend very deep to make the trial allocation process more efficient. And the third is patient recruitment. So we use our data and our sites use our data and our software.
00:09:52
Speaker
to find the most, the largest amount of patients eligible for that protocol. And then they go about recruiting them. Anecdotally, you know, we just learned for a large farmer, one of our largest sites in Belgium just recruited two patients of a study that was awarded to them through the Tranetix platform. And it's just an anecdote, but it's a very nice proof that when you use data, you use software and analytics. It actually moves the needle all the way from feasibility to trial allocation to sites and to patient recruitment. And that workflow just becomes more successful. So that's great job on the R and D side. I'm just proud of the team. The other one on the R and D side has to do with external control arms. So when you, in a lot of rare disease or oncology studies, there's ethical and and also operational questions.
00:10:48
Speaker
Do you want, can you recruit patients for the control arm of the study? And just as an example, if you recruit a patient for the placebo arm, you're pretty much taking responsibility on the outcome. And as much as this is good for mankind and for future generations, for that particular patient, it's a very difficult, ethical question. So we have just delivered also another example two very large control arms for an oncology study for a top five pharmaceutical company in Germany and other countries in Europe.
00:11:23
Speaker
And we have actually recruited a lot of patients for those control arms. And this is just makes you feel good because you use normal patients, normal care in all those key countries in Europe, and you don't have to spend the effort on recruiting that control arm. You can just focus on people taking your experimental drug. So that's another very powerful use case that I just like seeing. I think think it's just better for mankind makes you feel You know, good at the end of the day that you're doing things like that. And then on the post-approval side, we help pharma answer a ton of clinical
Role of Real-World Data During COVID-19
00:11:59
Speaker
questions. So who's taking my drug versus the four other competitive drugs? Are my drug being taken by more elderly people, younger people?
00:12:11
Speaker
different ethnics make up what is the difference in the usage of my drugs and also what is the outcome differential between my drug and other pharmaceutical drugs or other procedures, you know, my drug versus surgery versus other types of care and so on and so forth. so We see all the drugs that are in the market. We at tranetics are the first to see them launched because we go after clinical data that's directly connected to the hospitals and we can help pharma learn what really is happening. It's also something very interesting. When you do a clinical trial, phase two, phase three study,
00:12:52
Speaker
200 patients, 400 patients, maybe a few thousands of patients in that study, which is a very lab environment because it was very hard to get those patients into those studies. But now your drug has been approved. And something that was tested on a couple of hundreds or a couple of thousands is now being taken by tens of thousands, hundreds of thousands of people. And it's also people that you didn't have in your study. So let's say you tested it on patients ages 30 to 60, just as an example. Now there's an 80-year-old patient that's taking your drug. what' how What's the impact on that 80-year-old person? Because now this drug is in the wild and it's being, you know,
00:13:34
Speaker
It's helping people that maybe their makeup was not exactly what you had in your study which is fine, but helping farmer learn with real world data it's almost like a continuation of the clinical trial if you will but now in the wild and the last one i want to make on that i think it's a feedback loop to some extent right exactly it's ah amazing exactly what you said it's a feedback loop and it's also a way to accelerate the design so and and the delivery of new life-saving therapies into the market because, for example, with COVID, you didn't have 12 to 15 years to develop those drugs, those vaccinations that it usually takes. So you just do the development faster. You're making sure it doesn't kill people, it's not poison, there's no bad effects, all good. But then you unleash it to the wild. Why? Because of the urgency, because people are dying.
00:14:26
Speaker
And then in the wild, you're doing your almost continuation of your clinical trials now with hundreds of thousands of people. So we were part of that. We were part of the development of almost any COVID vaccination or drug. And we helped with this quick feedback loop, to your point, showing pharma what's working, how is it working, risks associated with things and so on and so forth. I think it's huge because in the future, with heavier usage and of real-world data on the back end, you potentially could accelerate from 12 to 15 years to maybe three to five years, and they just rely more heavily on RWD at the back end to make sure the right things are happening.
Patient Privacy and Global Data Challenges
00:15:07
Speaker
and I mean, magic happens only if you get the data. You you can only get the data from patients. And or today, you've collected information on over 200 million patients, which is just mind boggling in terms of of just sheer size in 20 plus countries. Initially, were were they the hospitals hard to convince in terms of granting you access? And as it become easier, as you you became a known quantity to hospitals, Beautiful question. So any and every innovation, you first start with those that are, you can say, more brave, willing to risk see the problem and understand they need to do something to fix it. And then after you get
00:15:52
Speaker
some adoption, it goes viral and it becomes much easier because, you know, the first hospital in Italy is much more difficult to get than hospital number nine in Italy because then they trust the process. And the so I'd say this, we have a very, very heavy investment and focus on patient privacy, on regulations around patient and data privacy. Health care all over the world gdpr hepa all that on the one hand we're very very conservative company and our hospital hco partners really really appreciate that and then on the other side of that you need to bring a lot of data.
00:16:38
Speaker
to those that are the researchers on the clinical side, pharma, HCOs, CROs, because without that, they cannot do their job. And without that, there will not be new life-saving therapies. So we walk a fine line between patient privacy, privacy laws, which we respect tremendously, and the need to put massive amounts of de-identified and anonymized data in the hands of researchers. So we do that. We do it very carefully. We have a very big infrastructure to protect that privacy and and comply with the GDPR and HIPAA and other regulations in all those countries. And ah which country is the hardest to deal with data privacy, get approval, just comply, right? The EU as a reputation of being the hardest, has that been your experience? So I love the EU and I love GDPR because
00:17:33
Speaker
People often think that GDPR is harder than HIPAA, which in in a couple of instances, it's just more detailed or maybe more strict. What I like about GDPR is that the EU are pushing forward the usage of data for clinical research. So they put it as a target, something that they want to facilitate and push. And they're saying, here is our framework. It's GDPR, it's other country-specific privacy laws. And if you comply with this framework, and if you make the right definitions of who you are and what is the level of anonymization of the data you're dealing with end-to-end,
00:18:17
Speaker
You're good. You're good to go. We designed the architecture in such a conservative way that we want to comply with the most strict country out there.
Harmonizing Global Data for Pharma Research
00:18:27
Speaker
And if you do it, then you're good with all the countries. Does it make it a higher bar to have an integrated clinical trial design across boundaries, across geographies? You know, if, if you have to deal with GDR constraints in the EU and you have to deal with the constraints imposed in the US being HIPAA or others, does it make it harder for you to kind of have it in one single repository so that you can offer it to big pharma that wants to run trials across boundaries?
00:18:58
Speaker
This is great because part of our mission in Tranetics is data harmonization, standardization of data and processes and languages. So it's very, very difficult to install our solution and harmonize it even in two instances in the US. You just need to do a bunch of translation and mapping and this and that, and we're very good at that. But it doesn't make it easy. Now we need to harmonize data from Japan to Germany, to Italy, to Spain, to the UK, to Poland, to Brazil, to Israel, to the US, Taiwan, and is ah is a massive undertaking. so But this is where we spend most of our time.
00:19:43
Speaker
And we became good at that in harmonizing the data. So when a pharma user is logging into the Tranetix platform, or when a pharma customer is buying data from Tranetix, they rely on us to already do the quality and harmonization and standardization work across all those countries. And what we serve them is something that is very easy and simple for them to work with, because we have already done all the mapping. So it's a great question because it's a huge challenge. And it's actually also fascinating. Even after you harmonize, you see that very, I mean, simply as a standard of care is very different between countries. You know, as a same disease patient, you walk into a US doctor office and a UK and an Italian doctor office and you may get similar suggestions or not.
00:20:39
Speaker
depending on a lot of things. And so we can see that we can see the the cohorts of of those disease patients and the different treatments that they're taking in in all those countries. But that's okay. That's okay. Because at the end of the day, once we harmonize the data, the pharma business is really local. And pharmacies, each country as its own market where they have to develop and then they have to sell and compete and market their drugs. So they're they're benefiting a lot from the fact that we have the country-specific data because if you just take data from the US and think it's applicable across the planet, it's just not. So as much as we think the world is one place and one big happy village,
00:21:26
Speaker
It's actually very, very local. So we make that jump, you know, local versus harmonized sets pretty well. And so it's just very, very valuable to any researcher. and pharma is the one in market where really they see the planet as one market, one integrated market to your point. it's ah Healthcare tends to be localized, but for pharma, they really want information from you all the main geographies.
AI and ML in Clinical Data Interactions
00:21:54
Speaker
You mentioned before you know when we did the prep call,
00:21:58
Speaker
that you're no technologist. And so understand that both of us have ah have common limits in the field of technology. But I would hate not to ask you about what AI is meant for your business in terms of a data management, right? You talked about harmonizing the data as the the newest tools have helped at all with that on that front. And secondly, how are you thinking of integrating integrating AI in the decision making process for clinicians? Beautiful, beautiful. So I'll take a step back to ah to tell you where I think it fits. Every company in MySpace, but also in verticals, you can analyze in as in answering the questions of what does it do and how does it compete and how does it differentiate in each of the following three layers, data, software analytics, and services.
00:22:55
Speaker
So for example, tranetics differentiates with data because we have the only global network out there that has very rich clinical data. For us, the second layer, software slash analytics, this is where you allow people to interact with the data and generate insights. And the last layer of services is that sometimes you have to deliver a complete project, A to Z, And we are able to do that as well by having very smart people help you write the question, design the the study, and then deliver it to you where they need to take care of the software and they need to take care of the data. So AI, ML, huge advancements. We use a lot of AI and ML in many, many different ways. raise I'll just give you a few examples. We use it in how we mine unstructured data. so we go So when you have structured data,
00:23:52
Speaker
after a few mapping, you know, iterations, it's good to go. When you're dealing with unstructured, the more sophisticated your AI, ML is the more significant level of insights and data you can pull from the unstructured data, such as physician notes and things like that. So we use it there. So it's a, it's a use case of mining data. The other use case we're implementing now a lot of ML into our UI. So we will enable. the pharma user to interact with our data and with our networks in in ways that are much more natural to them. In other words, having a conversation with the data. A lot of the AI ML for us is in a way of interacting with the data, UI, but much more expanded.
00:24:41
Speaker
if you will, and also on the generation of reports and and and answers and this and that massive value I see in AIML. So I'm very excited about that. And I'm very excited about that, not as an engineer, because I'm not. or a clinician because i'm not that as well but as a business person i just think it makes this unknown scary world of clinical data and in the world of algorithms and and sophisticated analytics.
00:25:13
Speaker
something that More or less sophisticated user can now generate those insights and i think it's just gonna exponentially increase the amount of insights correlations you know learnings that are gonna be generated. From the data we already have so that's another very exciting point because. What i already have in train edX can do so much more with those level of analytics that are coming into market now so we are extremely excited about that as as well as our customers.
00:25:44
Speaker
Good to
Pharma Expectations and Industry Consolidation
00:25:45
Speaker
look. One last question, or maybe two questions combined into one, and then we'll let you go, Gadi. You've been extremely generous at your time. So aside from supplying data, what else do you think pharma companies are expecting from vendors such as yours? Do you feel that at some point they would see consolidation amongst data vendors in a favorable light? Would they expect it? Great question. So first of all, let's say pharma is expecting me to deliver data. to deliver insights and to deliver patients into their clinical trials. So we do all of those three, and this is extremely, extremely valuable for them, especially when you look at the global scale and the footprint and doing it consistently in all those countries. It's a great question about consolidation. Every industry goes through cycles, a lot of innovation, a lot of smaller things. Then there's a funnel. Few survive and few and few get profitable.
00:26:43
Speaker
We are growing fast and we are profitable and this is very important to keep running a sustainable business. I definitely expect consolidation in my space to happen. It's just natural. Bigger is better. I think my pharma customers, my CRO customers, even my SEO customers would love less vendors and more use case and more value from each of them. Also, when you combine forces strategically, then there is an integration at the back end, business processes, data and software that just happens because you are closer to all of these and there's just a lot of value that's created by there.
00:27:23
Speaker
A little bird is telling me that maybe maybe you're going to be part of that consolidation play, but we'll we'll we'll see. Gadi, a real pleasure having you again on the podcast. Thank you so much for having taken the time, and I look forward to seeing you very, very soon. My friend, thank you for having me. It's been an honor. Talk soon. Thank you, sir.
Future of TriNetX and Digital Health
00:27:43
Speaker
Thank you for listening to another episode of Crossroads by Elantra. Today, we dove deep into the world of real world data, or RWD, and explored the significant impact TriNetX is making. I would like to highlight a few key points from our conversation to underscore the importance and the impact of these topics.
00:28:01
Speaker
First off, um RWD provides immense value to pharmaceutical companies. It helps streamlines protocol design, increase efficiency and aids in site selection for clinical trials, as well as supporting patient recruitment. We can't overlook the huge impact TriNetX has by providing pharma companies with data and how the medications perform in the real world, affecting millions of lives. The value of these insights is truly incredible. Lastly, let's not forget about the future. As we gather more data and leverage AI, the potential benefits for society are immense. We're on the brink of tremendous advancement and it's exciting to think about what lies ahead.
00:28:41
Speaker
If you'd like to learn more about digital health, please subscribe to this podcast and feel free to reach out. Thank you for joining us on this episode. Until next time, stay curious and stay informed. Take care.