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1rem

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In this video interview, Mark Melton, vice president of biospecimen data and operations, Slope, highlights the need for improved management of the sample lifecycle.

Managing the Chain of Custody for Sample Collection

, Mark Melton, vice president of biospecimen data and operations, Slope, discussed navigating data challenges in clinical trials, emphasizing the need for understanding complex data sources and ensuring a proper chain of custody for samples. Melton highlighted the importance of data mapping to standardize reporting across different labs and the necessity of secure data transfer to protect patient privacy.

A transcript of Melton’s conversation with ACT can be found below.

ACT: What are some of the operational challenges with biospecimen collection and sample tracking? How can they be addressed?

 Then the other part is, you have to imagine, there's hundreds of patients coming in a week, sometimes more, depending on the trial, and it can be less, too, depending on the phase, but the ability to realize that one patient, if I come in for a trial or for a visit, one visit out of, say, 50 visits, I'm going to generate, at a very minimum, double digit samples that come out on that visit, and then one person turns into a multitude of outputs as it relates to samples. Then, how do we take that and have the ability to tie that all back to the patient, and ensure that when we go to submit data and say, “Hey, what we tested, this drug is working, or it's not working.” It's actually not so much about the science at that point, that part's been defined. It's everything that's leading up to that to ensure, again, I said this earlier. Is it the right sample? How often was that sample froze or thawed throughout the process? Did we have the right temperature? Was it sent to the right location? All of that starts from the beginning of the chain of custody, which is at the sites, which are often overlooked. Again, the problem being is there's so many of them. How do you standardize something that's across tens of thousands of different businesses? It's really challenging, so we have to think about it that way and that's the hardest part, in my opinion.

Then the secondary part is you have a lesser number of people who are processing those samples after it leaves the site. In my mind, that should be a little bit easier to regulate, because it's a smaller number of businesses that you have to do, so you impose standards on them around data standards or processing standards, or the availability of the data. I think that's the biggest thing to consider when we're looking at that, is the entire chain of custody and how we fit it together and ensuring that, you know, some of this is bigger than business. While we are treating sick people, all of these are for-profit businesses, so everyone has a right to try to protect their bottom line, but at the end of the day, some of things are bigger than that.

What we try to focus on at Slope, and what we should focus on as an industry is there are certain things that are bigger than business. I think everyone could agree, hey, if these people who are sick, who are coming in, some of them dying, knowing that they want three to five more years, but that their samples are going to contribute to a cure, maybe, or to a treatment that gives longer time span of life, that we should all collectively agree that we need to control that chain of custody from the onset all the way through testing. For me, that's the biggest focus we should have.

Videos

In this video interview, Mark Melton, vice president of biospecimen data and operations, Slope, talks challenges at the site-level with collection.

Ensuring Proper Standards for Biospecimen Collection and Sample Tracking

 I think the biggest part is, again, understanding the multiple stakeholders. For me, I always try to go back to 30,000 feet of if I was someone in a clinical trial, I want to understand, hey, I'm coming in, I'm giving my blood or my tissue or whatever it may be, depending on the therapeutic area. I think there's often an assumption when we all go to the physician, but particularly in clinical trials, I don't think it's really well understood by the public how those are handled. The biggest part is everyone understanding that, hey, we have a location, clinical sites that collect those and again, they send them out. How do we ensure that it's interconnected, regardless of the fact that we have multiple different companies facilitating one process, so you can imagine, even in one company, it's difficult to align on process and ensuring that we're hitting that.

The biggest thing is understanding what happens at sites. If we were to break this down, there's only a certain number of facilities that will test samples, while there may be a few hundred, it’s nothing in comparison to the sites, which are in the thousands, tens of thousands even globally. All of those sites are networks, sometimes they're independent entities, but understanding what their process is. They're the point of origin, so how can we ensure that they're collecting those properly? That starts with down to the inventory, so we send out all this inventory. If you can imagine, if you're a large cancer site, for example, I'm running 600 trials. I'm just making up a number, but I'm running hundreds of trials, and all of those trials are sending me, essentially material to collect those samples. How do we manage that material and then turn that material into blood or tissue or whatever sample type, and get it out? Then how do we track that through the life cycle? What we've done is kind of crazy. The science has gotten so specific, so the concept of precision-based medicine, meaning we're going to target a specific gene or protein, the science has gotten very specific and has advanced tremendously. It's outgrown the logistics of a trial which is kind of crazy to think about, that the science, and in my opinion, the hardest part, has somehow gotten to the point where we're getting very specific in what we're targeting. Yet the process of just tracking a sample from one location to the four or five other it touches is extremely difficult. It's interesting at the end of the day, if you look, regulators are coming in and now stepping in on how you test those samples, but the gap we have right now is they're not stepping in on how those samples are tracked and reported on. I think that that's the biggest thing we have to focus on, collectively as an industry, is, how do we step in and ensure proper standards to ensure a proper chain of custody?

In this video interview, Mark Melton, vice president of biospecimen data and operations, Slope, highlights the clinical research industry’s need for more regulation around collecting data.

Creating a Global Standard for Data Sources

ACT: How can sponsors better navigate some of the current data challenges in clinical trials?

 If there was one area I would challenge the industry in, it’s ensuring that as quickly as possible, that we have the ability to, number one, establish a chain of custody, but number two is also, how do we ensure inside that chain of custody that the data that's being reported out before the sample is ever tested to give a result for whatever that assay or test may be, so all the data leading up to that is accurate. Right now, the biggest challenge in the industry is the fact that with these different data sources, they have different databases that report differently. Imagine you have six labs that speak different languages. How do you translate that? There's a concept called data mapping where you ingest that data, you map it to a common language, and then you're able to compare it. I think that's the biggest area that we have to regulate. We have to ensure that regulators are recognizing this problem, like the FDA, EMA, or whatever other governing bodies throughout the world, that ensures a standard, and right now we don't have a standard.

In this video interview, Mark Melton, vice president of biospecimen data and operations, Slope, discusses how different collection methods are creating complexities.

Understanding Different Challenges Around Data Collection

 The first thing of any problem is understanding, you know what the problem is, and what that means is understanding every facet of it as much as you can. This issue is really complex because it involves multiple stakeholders for data, multiple sources of data, so it's not like you're getting data from one database, right? Also, moreover, there are different companies you're dealing with. As much as we'd like to think there's a ton of regulation around data reporting for, at least for sample data, there's not, so you have to understand how each database functions, what goes into that data reporting ahead of time, and then understand how you're going to navigate it. I think the biggest challenge I see, because I've spent the past decade in this and specializing in this area, is that there's a host of different approaches, there's different viewpoints and different concepts, but I think we should simplify it. I think the simplifying answer is we're in the process of treating people who are sick, we're collecting their samples, we're giving them experimental drugs, so at the very least, we need to ensure that, as soon as possible, we have the ability to ensure that the data that we're collecting and specifically around samples, that it's the right sample to the right patient. Basically, data follows samples, so at the site, samples are collected, and they go to a host of different places that are in the hospital, people don't realize that, especially in the clinical trial, it goes to commercial labs with top scientists in the world who are going to take those samples and eventually test them. You can't put name, date of birth, and those sort of things on this, it’s pseudo-anonymized data. You have to have the ability to compare what happened, say, at a hospital system, and then when those samples go downstream, ensure that's the same sample, but moreover, what they'll end up doing is processing those samples into derivatives. Think about when you go get blood work at a doctor, they're not actually looking at their blood, they're going to look at derivatives of that blood, like serum or plasma as an example. The ability to have that chain of custody is huge. You have to understand, in every location you're sending it to, how do they handle that chain of custody, but most importantly, how do they report on it? How does that fit into how you're handling data?

In this video interview with ACT editor Andy, Studna, Silvio Galea, chief data & analytics officer, WCG, highlights the need for relevant, quality data and the challenges with needing to use multiple technologies. 

Prioritizing Data Quality Amidst Complexity in Clinical Trials

, Silvio Galea, chief data & analytics officer, WCG, discussed the challenges in the clinical trial industry, emphasizing low awareness among both patients and providers. He highlighted operational issues, including the difficulty in identifying and engaging patients, data interoperability, and ensuring data quality. Galea stressed the importance of user experience in electronic data capture tools and the need for industry standards. Prioritizing data collection should focus on safety, efficacy, and efficiency, ensuring data quality, and avoiding workflow disruptions.

A transcript of Galea’s conversation with

ACT: On the clinical operations side, how should stakeholders be prioritizing the data they receive considering how many new solutions and data collecting mediums have entered the market recently?

 Ideally we wouldn't have to prioritize the data we're collecting, because it should all be equally valuable, right? The challenge with that is that it's not coming in as a single stream, tying back to the lack of interoperability and the sheer number of technical solutions that are out there. I keep hearing metrics like 13-15 different technologies that CRC needs to work with to make sure that we're operating in accordance to the protocol. I mean, that's crazy. Do we use 13 different technologies on a particular day? Then you've got to do this across multiple trials. That’s one issue that's leading the need to prioritize data as you rightfully ask, but if we have to do a straight up prioritization of data, it has to be the data hat ties to the key facets of our industry; safety, efficacy, and efficiency. Are we collecting the right data? Is it the data that's needed to meet the goals. Is it being collected safely? Is there adequate quality checks in place to make sure that we're not putting any part of the process at risk and is it being processed in an effective, simple, issue-averse way? [It’s about] making sure that we're not introducing issues into the workflow that is going to put the entire integrity of everything that's happened at risk and we have to go back and redo any parts.

In this video interview with ACT editor Andy, Studna, Silvio Galea, chief data & analytics officer, WCG, highlights the need for relevant, quality data and the challenges with needing to use multiple technologies.

In this video interview with ACT editor Andy, Studna, Silvio Galea, chief data & analytics officer, WCG, discusses how sponsors can ensure that patients are properly reporting data.

Ensuring Patient Engagement and Data Integrity in Trials

ACT: What are some ways sponsors should be empowering their patients to ensure they are returning quality data?

 I mean, obviously we need to, first and foremost, ensure that the firewall between the sponsor and the individual is there and upheld. There's a lot that the sponsors can do to ensure that the engagement on the patient side is going to be there, making sure that there's a call for comprehensive education and training, that the information on how to use these tools and capabilities is clear and that the education material and the certification process is not just relevant, but seamlessly and quickly done. We talked about the expected experiences in the B2C space to make sure that these collection tools are user friendly, that the data has integrity, that it has the right type of checks in place, so that we collect qualitative data, that there's regular communication and support between the individual, the tool, and whoever's overseeing the data collection, so that if I have a question, I could get immediate answers, whether that's through reference materials, through simplistic AI, or other things. I could go on, but I'll focus on those top three to four items.

In this video interview with ACT editor Andy, Studna, Silvio Galea, chief data & analytics officer, WCG, touches on the usability of clinical technology.

Harmonizing Data in Clinical Research

ACT: Do you think decentralized data collection tools such as eConsent and electronic patient-reported outcomes (ePROs) increase patient centricity or do they create more burden by adding complexity? Why?

 Yeah, I think that, obviously, the ability of having electronic data capture capabilities is important and beneficial. I do think that there are two hurdles that we need to overcome. The first one is the user experience of these applications, right? I think we as consumers living in a digital world have a particular bar to which we expect to interact with technology, and that bar from a B2C consumer perspective, has to be held in the B2B, or in the medical, or in the record collection, or in eCOA (electronic clinical outcome assessment), ePROs, and others. The expectation is that I'm interacting with it, with an application that Apple's research and the same level of integrity, user experience, speed, and feedback should be there. First of all, these technologies should be at a certain level of usability. Secondly, from the back end, we're collecting this data, we don't have yet an industry standard for how this is all collected. On the healthcare side, with Medicare, about 10 years ago, the government put forth the FHIR (Fast Healthcare Interoperability Resources) standard for how data interoperability should work. That's great from an EHR (electronic health record)-medical record perspective, but we don't necessarily have one in the life sciences space right now that we could all harmonize against, so there's a lot of work being done in the back end to take my format of data, make sure that the semantics equal your format and semantical structure, not just the syntax, but the semantics, and then make sure that I could harmonize it and present it as a unified data set. From the patient's perspective, making sure that it's something I want to use, and it's not a hurdle, and there's no barrier of me entering data in a qualitative fashion, and then on the back end, it's if the data is there, how do we bring it together so that we could measure it quickly and efficiently and not have the data harmonization collection be a white space between the milestones of the clinical life sciences space?

In this video interview with ACT editor Andy, Studna, Silvo Galea, chief data & analytics officer, WCG, highlights challenges with patient data from an operational perspective. 

Navigating the Complexities of Patient Data in Clinical Trials

ACT: What are some challenges you are currently seeing in the industry around patient data?

 If you look now at the at the operational viewpoint, there's a ton of data providers out there. Everybody wants to help you identify and get counts, and that's great, but, here's the issue with that. Number one, this is just an aggregate, and it's deidentified, and rightfully so. This is very private, sensitive data. It cannot be exposed willy-nilly. Even if we do the identification, how do we then go about getting in touch with the individual? How do we go about seeing if they're eligible for the trial? How do we go about having you that in-depth conversation with them, one that's meaningful, empathetic, and sensitive, versus it just being kind of like a cold call recruiting or an ad on Facebook. When you ask about what are some of the challenges in the industry, we've got a macro and a micro situation. We don't have a good path forward in the middle, just from identifying, reaching out, engaging, shaking hands, touching, and talking with potential participants. Then there's the issue over time. I spent some time at a payer and had the opportunity to do a lot of data interoperability. One of the things that I learned is that as a data provider, I only have a small segment of any individual's data; yourself, myself, our families, our friends see different doctors and different facilities and different states and different specialties across their life. I'm struggling to make sure that my kid’s healthcare data is all in one system, much less mine where—not to age myself—but, some of my records are on paper, on those little yellow pads, where it's like, did you have this immunization? Did you have that? Even if you have access to patient data, how do you know that you have access to all of it, and that you've got the right, holistic, longitudinal view of an individual. There's that challenge also, there's the macro, micro, and then longitudinal challenge. Then you're getting data from who knows how many different systems and how quality is the data? Who entered the data? How did they enter it? Was it a doctor who's focused on you as they're typing, or was it a doctor that was typing with their back towards you and the information that you were giving them was towards somebody's back? The quality is also not there. I could go on, but I think if I was to summarize—with patient data—it's making sure that I'm not looking at patient data from the perspective of clinical trial research space, number one, awareness; number two; quality, number three; being able to securely and within the private space, engage, reach out, and touch the patient in a in a way that's ethical, has sensitivity, has integrity, is safe, is secure, and is comforting.

In this video interview with ACT editor Andy, Studna, Silvio Galea, chief data & analytics officer, WCG, highlights challenges with patient data from an operational perspective.

 In this video interview with ACT editor Andy Studna, Erin Erginer, director of product, Certara looks forward and shares her insights on the future of data management. 

Regulatory Outlook on Data Management

, Erin Erginer, director of product, Certara discussed the growing complexity and volume of data in clinical trials. Data is now captured from biospecimens and digital health technologies, including advanced genetic and imaging data. Regulatory agencies require standardized data submission for analysis, but do not dictate collection methods, leaving pharma companies to develop their own standards. Lack of standards leads to inefficiencies, with only 20% of studies meeting deadlines, causing significant delays and costs. Improper data collection can lead to missed endpoints and regulatory issues. Collaboration among pharma companies and external vendors is crucial for improving data standards and trial efficiency.

A transcript of Erginer’s conversation with

ACT: Looking forward, what are some actions you would like to see regulatory bodies take to address the challenges we’ve discussed?

 Well, I think that regulatory agencies have done a good job ensuring that the teams are using those submission standards and requiring that data integrity and being able to demonstrate how your workflow is going through, but I really do think that it comes down the remit of the pharma industry to be able to collaborate internally with each other and work together along with the external vendor data providers in order to create those data standards that can be used. Really solving this problem is going to mean that you are going to begin able to conduct trials more efficiently and ultimately get the drugs to the patients as they need them.

In this video interview with ACT editor Andy Studna, Erin Erginer, director of product, Certara looks forward and shares her insights on the future of data management.

In this video interview with ACT editor Andy Studna, Erin Erginer, director of product, Certara discusses how improper data collection can extend timelines and incur additional cost. 

Consequences of Improper Data Collection

ACT: What are some consequences of data not being collected properly?

 I think there are a lot of pretty strong indicators that the industry is struggling right now to deal with all of this data. Only one in five studies are able to meet their original planned deadlines. Those deadlines are typically an average of over a year beyond what their predicted time was going to be, so that's huge cost to the industry, and obviously delays in getting patients the drugs that they need. There's also an increase of end-of-study timelines in the last five years of 32%. Again, each of those days of delays cost an average of $600,000 to $8 million a day, so that's having a big consequence. Those consequences are pretty clear when you look at that information. In addition to the impact on timelines and cost, one of the most significant ones I've seen happen several times in the past is that if you don't have a good understanding of all the data that's being collected at the very beginning and ensure that you're collecting everything that's required to be reported at the end, you can miss information. If a scientist, if the biostatistician doesn't have a piece of the formula he needs, and able to do whatever the algorithms are and actually make calculations, you can't go back and collect that data again. It doesn't exist, so you just aren't able to report those endpoints that are very critical to the trial. The consequences for timelines as well as reportability of endpoints are pretty serious.

Data Management

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