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How the Pandemic Changed the Way Pharma Looks at Data Integrity in 2024


In this Pharmaceutical Executive video interview, Daniel Ayala, Chief Security and Trust Officer, Dotmatics, discusses data security threats from human error to AI tech and how COVID-19 impacted data integrity.

How has the pandemic changed the way Pharma is looking at data integrity in 2024?

Data integrity has always been a big topic in pharma. You know, the idea that research and testing has been clearly document has to be clearly documented, recreate double traceable, that's always been part of the ethos of the industry. And that's why that's why physical lab notebooks were so treasured for so many years, you know, shortage, destroying, when you really didn't have a way to, to, you'd had an immutable pathway to get you from the beginning of the study all the way to the end, to support your results. And you have to use that at every step along the drug discovery lifecycle from, from the earliest piece to the time you go to do a submission and afterward, even into the safety phases, with the rise of elf pens, the move to electronic versions has been ongoing, I think I put in my first ln 20, some 20 plus years ago now. So, it's an ongoing life, but the need for that assurance for the data hasn't changed, it is still important to understand all of the interactions that are had with that data and make sure that they stay, you know that they stay documented. When COVID came, though, there were a lot of other factors that did get amplified, you know, the move toward remote work.

First of all, a fast pivot to remote work meant that people who were used to sharing data, or storing data locally now had to make some of this accelerated move to it to online systems. With that came new processes, new technologies, new requirements to check that data as it's going in. So, data integrity isn't just about storing the data securely. And over time, it's also about making sure that it's input correctly, manual inputs, checking, looking at making sure that inputs that come in or reasonable as they come in, you know, that you're not asking for, you know, an input, that's five digits long, and you get seven letters, things like that to, to check along the way. And because of the other changes going on in society around us, everything required a little bit more scrutiny was also an increase in aggressive work to solve the problems of COVID. So, people were moving faster, that meant data going in faster data wanting to be processed faster, people wanting to get results faster. And the need then to be able to prove the results of those of that research faster and get to submission so that things could go to market, or the tests could become available, or that the results of studies like wastewater analysis, might then be able to be vetted by others.

All of this relies on good data, you know, the NIH, the NIH has made the statement, and I want to quote it correctly here, that that good science requires good housekeeping. And I think that's a really good foundation. And so, you have to not just put it in cleanly, you have to store it cleanly. You have to keep it cleanly and in COVID, everything was moving faster and faster than ever. We also saw a lot of an increase in the number of attacks, the number of attempts to get it research either before it came to market before it was made public, either through nation state actors or other bad actors that wanted to make either make it available, try and use it for their own purposes. Or just get it out and you know and jumpstart their own research. There's a lot of reasons why but we really did see an increase in attempts to get at that information because now it was very personal. It was about protecting their own populations. It was potentially also about money. There's a lot of reasons why that happened. But we definitely saw the increase in attempts to get the research. But the really interesting part is we also saw the increase of risk in manipulating data, what data that's in a system, it's hard to, it's hard to, in our, in our normal my normal day minds, try and contemplate why someone would want to do this.

So, what we also saw was a really big increase in the manipulation of data. It's hard to contemplate in our normal day lives, why someone would want to come into to a data repository and change test results or manipulate that kind of stored data. But that kind of data integrity was really key to patient safety, and product efficacy, as it has always been. But we definitely saw an increase in the number of attempts and attacks, to try and make those changes as well, during COVID. And so having good logging, good accountability, good recreate stability of all of that, it was really made very clear how important it was during COVID. To ensure that, that the products that were being put out to market the products that were being developed the therapies, the solutions, the tests, actually did what they claimed to do. And that's all rooted on good data, a foundation of good data, starting from the earliest phase of drug discovery, all the way through. But if you start with bad data to start or manipulated data, or data you can't trust all the rest of your research can become flawed.

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