So continuing this module, dealing with regulatory compliance when we're doing data management, we've got two acronyms left. One is GCP, for good clinical practice, the other one is Part 11. Good Clinical Practice is really an international set of quality standards for designing, conducting, and reporting trials. GCP is mandatory, if we're working with FDA Drug, and IND/IDE Studies. There are numerous guidelines specified in the, in the GCP documentation. All for the purpose of understanding when you need it, how to comply. And there's a lot of information specifically around educating investigators and sponsors about their roles and responsibilities when conducting clinical trials. Like our other regulatory work that we've mentioned earlier, there's a lot of information out there on the web. There's a lot of documentation on GCP. One of those I'm showing here, I pulled it off of the FDA website. It's freely available And it goes through, in great detail, the information that industry individuals might be looking at first when sort of trying to understand GCP and all of the rules and regulations there. Again, we'll see a little bit later that that we can drill down into each of those areas and there's a lot of a lot of information needed, a lot of decisions, a lot of documentation needed, for each of the different components. But at it, at it's heart, GCP really has 13 core principles. I won't go to the trouble of reading each of those here. I'll leave it to you to read those, and maybe hit the pause button if you'd like to stop. But I will say that all 13 of those principles really map to two specific concepts. One of those concepts is respecting rights of participants. The other one is conducting sound research. And when we say sound re, research, we mean accurate and verifiable. Research. So 13 principals all mapping to two, distinct, two very important concepts when conducting clinical trials. Those 13 principals, pick any one of them, and drill down a little bit, and you'll see that it's a little more complicated. All 13 principals. It's hard to argue with them. They're all important. It's easy to see why those would be extremely important in, in conducting, designing and conducting and reporting trial information. However, you pick any one of those and you drill down and try to figure out, well, how would I know whether I'm doing that one just right. And that's where Part Eleven, sorry, that's where good clinical practice come, comes into play. It's a little bit more tricky. again there are lots of documentation out there. I pulled up a website here from the European Union that, that talks about GCP and How same sort of thing that I just showed you on the FDA site. Again, going back to the FDA site, there's a lot of information there. within that specific FDA site, you look, look down at the different topical sub, subcategories, and you see, there's a lot of them. And there's a lot needed there. Because it really is quite, quite important work. And just making sure that you're doing everything needed, everything possible. And you're dotting every i, crossing every t. when you're conducting this very important, very expensive, and very impactful potential work. That, that it's done correctly, and that it can be relied upon. So, within GCP there are just, just a tremendous amount of work to do. There's a tremendous amount of documentation. There's a tremendous amount of training, et cetera. But, if you are doing, clinical trials if you're doing drug studies for the FDA where there will be an FDA submission. you're required to to, to, to comply with good clinical practice. Even in scenarios where you're not required, it's again one of those areas where all of those principles are very good. It's a very good idea to go down deep into those documents. Go down deep into those concepts. And it will absolutely improve the way you're conducting research. So, one topical sub area associated with GCP that's very relevant to data management teams is a section called 21 CFR part 11. CFR here stands for the code of federal regulation, so this is again a mandate. Basically part 11, there's a lot of confusion sometimes on when you need it, when you don't need it. We'll say that like FISMA it can, it can be be quite burdensome to, comply with. So this when you need it, when you don't need it. Is important. [BLANK_AUDIO] Oops, going backwards there. Alright, so going back to the FDA website. There's some information here, you know, that you can pull up around electronic data and Part 11. drilling down on that in other documents. And there, there's a lot of information out there. I've pulled up one here and again referenced it down the left. I've pulled this one up really because it had a pretty, pretty decent definition of when you need it, and when you don't. But I'll note to the top of this the language that's at the top, there's some ambiguity on, you know, even on the part of the FDA on how strict the requirements are and how much verification is needed. But looking into that document around how they defined the scope of part 11. they say here, in this documentation, that some have understood the scope of Part 11 to be very broad. We, however, believe that some of those broad interventions could, could lead to unnecessary controls and costs. And could discourage innovation. as a result, they clarify that you know, they actually mean, the interpretation of Part 11 can be quite narrow. Under the narrow interpretation of the scope of Part 11, with respect to records required to be maintained, under predicate rules are submitted to FDA. When persons choose to use records in electronic format, in place of paper format, Part 11 would apply. So backing up, like the thisma work, there's a lot of documentation, there's a lot of validation of systems if you're going to be using an electronic system in clinical trials work, in work that, will eventually be submitted to the FDA, to the regulatory groups, upstream. And so it's important that you understand how to make that work. It's also important, ahead of time, to know that there is significant cost, there is significant effort there. That they define it here as, as being invokable. When the person, or the investigators choose to use records, in electronic format, in place, of paper format. So, they also go on to say that on the other hand. When persons use computers to generate paper reports for electronic reports. And those paper records meet all the requirements of predicate rules. In other, in other words, the documentation requirements for a study. as long as there is another source document as opposed to just the electronic copy that you're using for collection and, and management of your clinical trial data. Then Part 11 Rules do not apply. So it all boils down to the the, being able to sort of document and have it, having the ability to monitor. Back to the very beginning of when data are collected. If those data are on paper. If they're put into a folder, a document binder for an individual for that particular trial. And the information is collected in, and managed electronically then there is no strict requirement for a part eleven system. However if the data, the first time the data are being generated, it's being put directly into the electronic system and there is no other source document, then there is a requirement that that system be certified, it be validated, it be tested, it be documentated, documented. Even a good idea in part eleven cases to, to have an auditor come in and do a mock audit and, and certify that everything looks okay before collecting data for a clinical trial. So I've, I've said a lot of words around this. you, you, you probably get the idea again that it's quite an onerous process. It can be quite expensive. because there is a lot of documentation around the system, not just the software, but the whole infrastructure, related to this electronic system that's collecting data, if it's going to be the only source document for the for, for the study. Because going back, it has to be either has to be accountability, and so if a number gets entered as a one, there has to be absolute assurance that it, that as it's pulled out that it remains a one. That there are no data changes in, in line. Or if there are, that there's sufficient documentation, just as if there would be with a paper trail with good clinical practice. That, that those data changed, who when and why those data changed. I would say, again, however here, there are some great ideas, audit trails, electronic signatures, some of which are also covered in the HIPPA rules, but some are not. So it's a great idea, whether or not you need to comply with Part 11, to go through and look at those, principles, look at those modules, look at those requirements for at least the technical components or the work flow pieces of part 11. And make sure you're looking at those as best practices, even though they might not be mandated for your particular study or trial. So again I'll, I started with a few thoughts and I'll just end with those same thoughts. Just because you can, doesn't mean you should Trust is hard to earn and requires you to really think about it and become, become more than just a compliant person. But, but a real thinker. And you want to, want to be sure that you're always looking out for the best interest of study, of your volunteers, of your researcher, of, of your institution. And, a lot of these principles that we've covered. A lot of them that are covered in detail. In, in these particular regulatory components They will help you do that. finally as I've stated several times, just because you don't have to doesn't mean you should, so, should, should not. So you know, it's a great idea to look at these and implement them anyway where you think they, they should, should be considered best practices. An again, we've talked about some of these early on in other lectures as we've come along, an thinking about data capture systems an best principles. So at, at, at the conclusion here, I'd just like to restate as we started we were going to do a high level, regulatory considerations for managing data. And also for electronic data systems.