 Live from the MIT campus in Cambridge, Massachusetts, it's theCUBE, covering the 12th annual MIT Chief Data Officer and Information Quality Symposium, brought to you by SiliconANGLE Media. Welcome back to theCUBE's coverage of MIT CDOIQ here in Cambridge, Massachusetts. I'm your host, Rebecca Knight, along with my co-host, Peter Burris. We're joined by Dr. Santikari. He is the Vice President and Chief Data Officer at ERT. Thanks so much for coming on the show. Yeah, thanks for inviting me. We're gonna call you Santikari. That's what you go by. So start by telling our viewers a little bit about ERT, what you do and what kind of products you deliver to clients. Yeah, I'll be happy to do that. ERT is a clinical trial support company and we are a global data and technology company that minimizes risks and uncertainties. Within clinical trials for our customers. And our customers are top pharma companies, biotechnology companies, medical device companies, and they trust us to run their clinical trials so that they can bring their life savings drugs to the market on time and every time. So we have a huge responsibility in that regard because they put their trust in us. So we serve as their custodians of data and the processes and the therapeutic experience that you bring to the table, as well as compliance related expertise that we have. So not only do we provide data and technology expertise, we also provide science expertise, regulatory expertise. So that's one of the reasons they trust us. And we also have been around since 1977. So it's almost like over 50 years. So we have this collective wisdom that we have gathered over the years and we have really earned trust in this space and because we deal with safety and efficacy of drugs. And these are the two big components that help FDA or any regulatory authority for that matter to approve the drugs. So we have huge responsibility in this regard as well. And in terms of product, as I said, we are in the safety and efficacy side of the clinical trial process. And as part of that, we have multiple product lines. We have respiratory product lines. We have cardiac safety product lines. We have imaging. As you know, imaging is becoming more and more so important for every clinical trial. And particularly on oncology space for sure to measure the growth of the tumor, that kind of thing. So we have a business that focuses exclusively on the imaging side. And then we have data and analytics side of the house because we provide real time information about the trial itself so that our customers can really measure risks and uncertainties before they become a problem. At this symposium, you are going to be giving a talk about clinical trials and the problems of the missteps that can happen when the data is not accurate. Lay out the problem for our viewers and then we're going to talk about the best practices that have emerged. Yeah, I think the clinical trial space is very complex by its own nature. The process itself is very lengthy. If you know one of the statistics, for example, like it takes about 10 to 15 years to really develop and commercialize a drug. And it usually costs about two and a half to three billion dollars per drug. So think about the enormity of this. And so the challenges are too many. One is data collection itself. Your clinical trials are becoming more and more complex, becoming more and more global so that getting patients to the sites is another problem, like patient selection and retention, another one. Regulatory guidelines is another big issue because not every regulatory authority follows the same sets of rules and regulations. So and cost. Cost is a big imperative to the whole thing because the development lifecycle of drug is so lengthy. So and as I said, it takes about three billion dollars to commercialize a drug and that cost comes down to the consumers, that means patients. So the cost of the healthcare is growing, is skyrocketing. So and in terms of data collection, there are lots of devices on the field, as you know, wearables, mobile health. So the data volume is a tremendous problem. And the vendors, like each pharmaceutical company, use so many vendors to run their trials, CROs, like clinical research organizations. They have like EDC systems. They can have labs, so you name it. So they outsource all these two different vendors. Now, how do you coordinate and how do you get them to collaborate? And that's where the data plays a big role because now the data is everywhere across different systems and those systems don't talk to each other. So how do you really make real-time decisioning when you don't know where your data is and data is the primary ingredient that you use to make decision? So that's why data and analytics and bringing that data real-time is a very, very critical service that you provide to our customers. When you look at medicine, obviously, the whole notion of evidence-based medicine has been around for 15 years now or so. And it's becoming a seminal feature of how we think about the process of delivering medical services and ultimately paying for them and everything else. And partly that's because doctors are scientists and they have an affinity for data. But if we think about going forward, it seems to me as though learning more about the genome and genomics is catalyzing additional need and additional understanding of the role that drugs play in the human body. It almost becomes an information problem with a drug. I don't want to say that a drug is software, but a drug is delivering something that ultimately is going to get known at a genomic level. So does that catalyze additional need for data? And is that changing the way we think about clinical trials? Especially when you think about, as you said, it's getting more complex because we have to make sure that a drug has the desired effect with men and women, with people from here, people from there. Is that, are we going to push the data envelope even harder over the next few years? Oh, you bet. And that's where the real world evidence is playing a big role. So instead of patients coming to the clinical trials, clinical trial is going to the patient. So it is becoming more and more patient-centric. Interesting. And the early part of protocol design, for example, the study design, that the step one, right? So the more and more real world evidence data is being used to design the protocol, the very first step of the clinical trial. Another thing that is pushing the envelope is whole artificial intelligence and other data mining techniques. And now people can use to really mine that data, the YAMR data, prescription data, claims data. So those are real evidence data coming from the real patients. So now you can use these artificial intelligence and machine learning techniques to mine that data and then really design the protocol and the study design instead of flipping through the YAMR data manually. So patient collection, for example, is no patients, no trials, right? So gathering patients and the right set of patients is one of the big problems. It takes like elongated time to bring those patients and even more troublesome is to retain those patients over time. So these two are like big, big things that takes long time and site selection as well, like which site is going to really be able to bring the right patients for the right trials. So two quick comments on that. So one of the things, when you say the patients, when someone has a chronic problem, chronic disease, they tend, when they start to feel better, as a consequence of taking a drug, they tend to not take the drug anymore and that creates this ongoing cycle. But going back to what you were saying, does it also mean that the clinical trial processes, because we can gather data more successfully over time, it used to be relatively segmented. We did the clinical trial and it stopped and then the drug went into production and maybe we caught some data. But now because we can do a better job with data, the clinical trial concept can be sustained a little bit more. That data becomes even more valuable over time and we can add additional volumes of data back in to improve the process. Is that shortening clinical trials? So tell us a little bit about that. Yes, so as I said, it takes about 10 to 15 years if we follow the current process, like phase one, phase two, phase three and then post-marketing, that is phase four, right? I'm just not taking the pre-clinical side of this house into picture. That's about 10 to 15 years, about $3 billion kind of thing. So when you use this kind of AI techniques and the real-world evidence data and all this, the projection is that it will reduce the cycle by 60 to 70%, the whole study begin to end time. So from 15 down to four to five? Exactly, so think about the two advantages. One is obviously you are creating efficiency within the system and this drug industry and drug discovery industry is ripe for disruption because it has been using that same process over and over for a long time. It's like it is working, so why fix it? But unfortunately it is not working because the healthcare cost has skyrocketed. So these inefficiencies are going to get solved when we employ real-world evidencing into the mixture, real-time decision-making, risks analysis before they become risk. Instead of spending one year to recruit patients, you use AI techniques to get to the right patients in minutes. So think about the efficiency again and also the home monitoring or M health type of program where the patients don't need to come to the clinical sites for checkup anymore. You can wear wearables that are FDA regulated and approved and then they're going to do all the work from within the comfort of their home. So think about that and another thing is like very terminally sick patients for example. They don't have time nor do they have the energy to come to the clinical site for checkup because every day is important to them. So this is the paradigm safe that is going on. Instead of patients coming to the clinical trials, clinical trials are coming to the patients. And that said, that's a paradigm shift and that is happening because of these AI techniques, blockchain, precision medicine is another where you don't run a big clinical trial anymore. You just go like micro trial. You just group small number of patients. You don't run a trial on breast cancer anymore. You just say breast cancer for these patients. So it's a micro trials. So that needs- But that can still be aggregated. Exactly, still need to be aggregated but you can get that early results quicker. So that you can decide whether you need to keep investing in the trial or not. Instead of waiting 10 years only to find out that your trial is going to fail. So you're wasting not only your time but also preventing patients from getting the right medicine on time. So you have that responsibility as a pharmaceutical company as well. So yeah, it is a paradigm shift and this whole industry is rife for disruption and here it is right at the center. We have not only data and technology experience but as I said, we have deep domain experience within the clinical domain as well as regulatory and compliance experience. You need all these to navigate through these turbulent water of clinical research. Wow. Makes sense. Revolutionary changes taking place. It is, it is and the satisfaction is you are really helping the patients. So, you know, and then you were- And helping the doctor. Helping the doctor. At the end of the day, the drug company does not supply the drug. Exactly. The doctor is prescribing based on knowledge that she has about that patient and that drug and how they're going to work together. And another good statistic is that in 2017, just last year, 60% of the FDA approved drugs got supported through our platform, 60%. So there were, I think, 60 drugs got approved. I think 30 or 35 of them got used, our platform to run their clinical drug. So think about the satisfaction that we have. Job well done. Exactly. Well, thank you so much for coming on the show, Santi. It's been really great having you on. Thank you very much. Yes. Thank you. I'm Rebecca Knight for Peter Burris. We will have more from MIT CDOIQ and theCUBE's coverage of it just after this.