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AI Reality Check & The IDMP Countdown
58 Plays3 days ago
Welcome back to the Ennovation Podcast, where we bring you the latest trends, insights, and expertise in life sciences and regulatory affairs. This time, I’m joined by Pierre Stanislawski, our IDMP expert, and Josh Keliher, who’s leading the charge on AI at Ennov. Together, we dive into two major questions: what does AI really mean for regulatory affairs? And is IDMP finally ready for launch?
Our Top Resources:
> What to look for in an AI vendor. Download the AI Vendor Checklist
> See what AI can actually do. Explore use cases in Life Sciences
> Simplify agency correspondence. Watch the webinar to see AI in action
> Know what’s coming next. View our updated EMA Timeline
> Current RIM not IDMP-ready? See how Ennov EASI can help
> Preparing for IDMP enrichment? Download the IDMP Starter Pack
Recommended
Transcript
Introduction to the Innovation Podcast
00:00:00
Speaker
Welcome to the Innovation Podcast, your go-to source for the latest trends, insight, and expertise in life sciences and regulatory affairs, all in one place. At Enove, we're dedicated to empowering life sciences organizations with innovative solutions to navigate the complexities of their industry.
00:00:19
Speaker
I'm your host, Alice, and I've worked in regulatory operations for eight years.
AI in Regulatory Affairs Overview
00:00:23
Speaker
My background is in regulatory information management, and I bring my experience working in large pharmaceutical companies to supporting clients here at Enove.
00:00:31
Speaker
This time on innovation, wondering what AI means for regulatory affairs. I catch up with Josh Kelleher to find out how AI could change the regulatory world. IDMP ready for liftoff at last.
00:00:44
Speaker
Pierre Stanislavski discusses the EMA's latest updates to PMS and what they mean for marketing authorization holders.
Meet the Experts: Josh and Pierre
00:00:50
Speaker
Pierre, Josh, can you tell us a little about yourselves? Yes, my name is Pierre Stanislavski. I am Product Director for Regulatory Solutions at Enhoff.
00:01:00
Speaker
So I develop both a an expertise in IT systems, development and setting up of technical environments, but also in the specific topics, regulatory managing regulatory data, like IDMP especially.
00:01:19
Speaker
Well, thanks for having me on, Alice. Just a little bit about me. I'm a senior solution consultant with Enob. I've been in the industry for about 20 years, most of which has really been in in regulatory.
00:01:29
Speaker
And my role is to work closely with customers to really see how our solutions can meet their needs. So that means doing demos and workshops, um that type of thing. But also then I work very closely with product management to really ensure the continued relevance of our solutions.
00:01:45
Speaker
Thank you so much, guys. Well, Josh, I'm sure I'm not the only person who's been hearing a whole lot about AI over the past
AI's Evolution and Impact in Regulatory Sectors
00:01:51
Speaker
few years. From web-based tools like ChatGPT to built-in assistants in things like Word, it feels like AI is becoming an everyday tool for so many of us.
00:02:01
Speaker
But I have to be honest, when I hear AI, I'm not always sure exactly what people mean. Can you give me a quick grounding? Sure, I mean, absolutely. Really, AI is is really basically the idea of machines modeling human intelligence, you know, whether it's around reasoning or perception or creation or kind of visual analysis.
00:02:21
Speaker
And it's actually really been around for a long time. I think the first models come out of the 50s. And really, if you think of any computer game, you know, even back from when you were kids, you're When you're competing against a computer opponent, that's a form of AI.
00:02:33
Speaker
It just happens to be that it was less sophisticated or really kind of had to be quite elaborately kind of coded to achieve it, but it's still conceptually AI. um But they were you know they were emulating human intelligence on on some level.
00:02:48
Speaker
ah So then in the past decade, we really saw things like natural language processing, the ability to process voice. You can think of like Google searches, for example, um as well as spoken word from like a Google tablet or something.
00:03:02
Speaker
And then also machine learning where we have large data sets and we try to really do sort of pattern recognition from large data sets. So that's kind of probably where we were about maybe 10 years ago, five, 10 years ago.
00:03:13
Speaker
But really in recent years, it's the development of these large language models, which is really taking a huge amount of data and trying to kind of contextualize it and kind of interrelated. So where you kind of break it into pieces that are you know, almost statistically kind of connected to each other. And then that gives um the ability to kind of make queries across a huge data set and really kind of make a lot more inferences from data that we haven't been able to do in the past or would have been more kind of technically elaborated in the past.
00:03:45
Speaker
So, and that's what's really enabling kind of this Gen AI revolution that everyone has heard about. So obviously things like ChatGPT and those tools, because they're prompt based, we just kind of interact with them almost as we would with a person.
00:03:58
Speaker
ah That really lets us do quite a lot of complex tasks, you know, as we're all starting to see, ah you know, maybe in our personal lives and increasingly professional lives as well. So really, I've only been hearing about it for a couple of years, but it's been around for a long, long time.
00:04:13
Speaker
Yep, that's right. Yeah, it's really something that people have been working on for, i mean, back when I was in university 25 years ago, there was the department that was doing, ah the computer science department was doing work in AI back then.
00:04:25
Speaker
um They're using kind of certain coding languages, but it's the technology has changed and the computing power has increased such that we can now do much more sophisticated things. We can handle huge amounts of data and kind of query across it much more rapidly.
AI's Role in Life Sciences
00:04:41
Speaker
So I know you're involved with the AI product development here at Anove. How did you kind of first get involved with that? ah sure So one of the things that drew me to end of was absolutely the fact that we have this department called Enove Labs, who and their role is really to innovate within the technology space. So they research all kinds of kind of upcoming technologies or nascent technologies, and their goal really is to then prototype it.
00:05:06
Speaker
really with the goal to kind of answering use cases that meet our customer needs. So to me that there was a department and they're really smart people. i think they all have PhDs. They're all really kind of quite brilliant people.
00:05:17
Speaker
um That emphasis on innovation was really interesting to me. um but you know, as I've been, you know, kind of a gamer and all those things back when I was a kid and I read lots of Isaac Asimov books. So kind of the idea of, of, uh,
00:05:30
Speaker
how computers and how robots um might emulate human behavior or not has always been kind of interesting to me. But yeah, it's really since come to Enove that i've I've gotten the opportunity to really interact more with the technology and and and really kind of speak a little bit into the and into the development of it here.
00:05:48
Speaker
And one of the things you've we've been doing is really looking at use cases that can be applied in in the regulatory world and and in in pharmaceuticals more more kind of extensively.
00:06:02
Speaker
um What AI developments have you really seen in that life sciences space? Well, it seems like it's really everywhere kind of in life sciences. Now, if you go to a conference, you'll see it's almost mandatory to have it on your banner or on your, your booth backdrop that you're doing something in the AI space.
00:06:19
Speaker
um So where the maturity is, you know, that's certainly something that's, ah that'll be interesting to kind of talk about and explore, but we're seeing, especially things around with the gen AI piece, the generation of content is becoming kind of pretty common where, or,
00:06:34
Speaker
being proposed is a common use case anyway. So maybe writing INDs or writing submission documents, that's getting increasingly common. ah There's a lot of players in the structured content authoring space who are also implementing or um you know deploying kind of AI solutions to be able to kind of sort of tie those little discrete pieces of content kind of together.
00:06:56
Speaker
um We also have seen kind of classifying content that's really based on, you know, there has been that machine language kind of initiatives in the past to do that. But of course, Gen AI is kind of accelerating that. So being able to ingest large amounts of documents, it's really common on the clinical space, like the ETMF space, especially.
00:07:12
Speaker
um And then increasingly what we're seeing, especially with customers is they're often deploying things like chatbots that are maybe able to assist employees as they have questions.
Evaluating AI Use Cases: Beyond the Hype
00:07:21
Speaker
um That's becoming more common. So that's also kind of ah another output that we're seeing.
00:07:25
Speaker
in the AI space in our ah our industry. Yeah, it definitely feels like when I've been to conferences and things like that, you do see suddenly everyone has AI, there's an AI track, you know, there's there's loads of these tools popping up. Exactly, yep.
00:07:38
Speaker
and And I have to confess, I've sometimes been a bit skeptical of those in the past. It feels a bit like, you know, ChatGPT launched a couple of years ago and suddenly AI was like the magic wand tool. Like it's absolutely everywhere.
00:07:52
Speaker
So how do you kind of figure out which of those use cases are going to be really valuable? Which ones are ones that you should dig into more? Which ones are the ones that maybe they're not mature enough yet or they're not going to wind up being...
00:08:08
Speaker
the cases where AI is really going to make that huge difference. Yeah. And that's, the that's the, I think tricky part of where AI is in its current development. There is obviously it's being used as, as a lot of ah of a marketing tool, but then in reality, will it really kind of help or at what point in its development, will it actually be helpful?
00:08:29
Speaker
And in how many cases is it just applicable to a very narrow use case or, you know, might be useful in kind of one country, but not others, um or one type of documents, but not other types of documents. So I think that that maturity curve, I mean, we're still on the the maturation curve, I think.
00:08:46
Speaker
We really try to assess it based on um really having a use case that is is really based in the real world and is able to really accelerate people's job in the real world and really trying to make sure that we're not just doing things for kind of a marketing reason, but really trying to you know, take away maybe some of the burdens that people might have in the in the regulatory space. And there's there's a variety of use cases and some things may be quite simple, like, you know, a natural language based search, right? Something like a Google search.
00:09:16
Speaker
People are really used to that. um That really lowers the threshold of entry for a casual user. If they can just say, hey um hey, what are the products that we have approved in this market? You know, not needing to run a query or know where to go to find that.
00:09:30
Speaker
um that That is kind of a very kind of simple way to do it, making that data more accessible. um We're also you know doing things like in the quality side, especially this is interesting, and it's actually training that in the quality side, but also that applies to to clinical and regulatory as well.
00:09:45
Speaker
But being able to generate questionnaires, so when you're doing quality, you have to assess competency of users. And one of the burdensome tasks is that for each s SOP or each document, you have to create a list of questions that are really going to establish whether that person is is effectively trained or is effectively understood the document.
00:10:05
Speaker
I have definitely, definitely been there myself. It's one of those things you're like, oh, I'll just do a few questions. How long can it take? And then like an hour later, you're still trying to make sure that you've got enough things to make sure people will really, really have...
00:10:18
Speaker
you know, have taken in that training properly. So yeah, that would be very cool if you could get a hand with that. Exactly. And I mean, you know, then of course, when we create the questions, we're bringing our own bias into it you know, well, what do I find interesting in this training? Or what do I think they should know?
00:10:32
Speaker
um but then we just use AI to generate automatically then these questions. So instead of taking yeah an hour, I think one of our other colleagues said it would take two hours to generate 15 questions or two and a half hours to generate 15 questions that are really good questions.
00:10:45
Speaker
And so now, of course, we can do it in a couple of minutes. And then, of course, you can take it and edit those questions. So that type of use case where it's answering a real need, especially if there's something that's like a burdensome manual task or um just something that takes a long time that It's value add, but it's, is it two and a half hours of somebody like yourself's value add?
00:11:08
Speaker
um Or would it be better if we generate the questions and then you can spend half an hour, 15 minutes reviewing them, tweaking them, adapting them. So that that's that definitely kind of a general approach we take um with ah with how we're implementing things.
Assessing AI Value in Organizations
00:11:24
Speaker
Some other ones, you know we're doing things like the automation of you know case entry entry. We're doing that in Cosmeto Vigilance side, but of course, going to be with complaints as well. you get a customer complaint that comes in. Being able to read an email and just extract the key data points and launch a case based off of that, um that type of thing. Taking in ECTD documents, you know, obviously if you have a company who is a large part of their business is to buy in assets. So they buy in a certain product or they resell a product that's approved in the U.S. in some other market.
00:11:56
Speaker
and then they may be ingesting a large amount of content into the system. So we're using AI tools to automate that. So instead of having to add each one and classify it or do a migration project, you know let AI actually read the document say, oh, this applies to ibuprofen. It's my 200 milligram tablet that I've just bought.
00:12:13
Speaker
um It's this type of document. It's a 3-2-P-3-1 document for this manufacturing site. So it's able to kind of take away, again, some of that important work, but one that often takes a lot of somebody's time to actually, you know, do all that mapping.
00:12:31
Speaker
Yeah, like we all want our documents classified correctly so that we can find them again, but equally the effort of like putting on that metadata and making sure it is all correct. If we can get a hand with that, I don't think there's many people who would be complaining about not having to spend quite so long on that work bit of work.
00:12:46
Speaker
Yeah, I don't know a lot of people that just like to enter information into systems that has their day to day work. Yeah, so I think it's it's a useful case. So we've talked a bit of various use cases and kind of how people can start looking at those.
00:13:02
Speaker
Once they've kind of got some of those use cases in mind, how would you really recommend organizations assess the value of AI and and how are they going to make that work for their organization? um I think it's ah it's it's ah it's a really critical question because there can be with AI because there's so much in the media and around us and you look on LinkedIn and there's AI everything everywhere. You go to conferences, it's everything AI. So there can be a real fear of missing out.
00:13:29
Speaker
Yeah, we've got to get in on this. You know, you just want to catch up. Well, we've got to do something. We've got to implement AI. um Otherwise, we're going to fall behind. Yeah, it's got we've got to launch an initiative. But I think it's worth taking a step back and really um not just hopping on the bandwagon, but really assessing where are the pain points in my organization. i mean it's really standard you know business analysis type work. it's It's really understanding you where in my processes are people spending an inordinate amount of time on things.
00:13:58
Speaker
Where is there ah lack of surveillance into data that maybe would be really useful to have? um and And really even trying to tie that to some sort of kind of cost, that can be really useful also for justifying and assessing, you know, the effort to put in the AI, is it going to be justifiable? Am I really going to get out of it what I need?
00:14:19
Speaker
um So it's really better to think of AI as a tool to apply to your business, much like we would other software systems or other um you know processes or even hires of employees. you know They're the things that we when we choose to hire somebody, we say, OK, have this need in my business and I think that this person is going to help.
00:14:37
Speaker
It's the same thing really with AI. So really identify where you're getting those inefficiencies or heavy manual work or places kind of requiring a lot of that oversight. um and start there.
00:14:48
Speaker
So, and once you've defined what you really need, then you kind of look for the tools that are going to meet the job and you then assess, well, um you know, is the cost of implementing this justifiable? Is the risk of implementing this justifiable? um You know, maybe you don't want it to be,
00:15:06
Speaker
the the way that you do like screening of patients for clinical trials, maybe it's good to have a person who does that. ah But, you know, but gather that data across different sources, you know maybe that's something that AI can help with. So it's balancing kind of cost and risk and benefit ah in one place. Yeah, for sure.
00:15:24
Speaker
And then yeah I think you have to be pretty... um ah pretty rigorous, I think, with the vendors that you partner with. um I think it's important to really know, you know, how are they validating what they're doing from an AI perspective? Because that's obviously a challenge. It's, you know, their the models can evolve and they can kind of drift,
00:15:44
Speaker
So that's a critical piece. How do they ensure security? you know Are they using public or private models? um How are they ensuring that they're using ai kind of in an ethical way?
00:15:56
Speaker
ah so I think it's it's important to kind of in the same way that you would query a vendor about their IT security and their financial um stability and their internal quality processes, it it would be another area that you really want to kind of investigate.
00:16:10
Speaker
um Again, it's part of managing the risk of it. Yeah, no, that makes so that makes sense.
Responsible AI Use in Regulated Industries
00:16:15
Speaker
I think that kind of responsible use of AI is is so key. And when you're looking with vendors and making sure you're digging into what is their ethos around AI development, have they got that background and that rigorous kind of approach to AI, particularly when we're working in life sciences, which you know as we're all aware is such a highly regulated industry.
00:16:40
Speaker
And we have to be able to demonstrate that we as companies that also have that approach when a ah regulator comes in and audits, for example. Yeah, absolutely. and It's something that you know we take really seriously at Enav. We operate absolutely in compliance with the EU AI Act.
00:16:57
Speaker
So that was a an act that um deployed by the by the European Commission to kind of say how AI should be kind of implemented. And we really kind of try to make sure that we we are consistent with that. So really being transparent about the use of AI so that users know when something has been generated by AI um for So really, and we ah especially ah think of AI as really intelligent assistants at this stage. So we want to provide useful outputs that then a human will assess.
00:17:26
Speaker
So a key thing for us is keeping a human in the loop. That's kind of one of that one of our kind of principles behind it as well. um Then there's, of course, the you know assessing the risks appropriately. So are these use cases ones that are going to ultimately put you know patient safety at risk? Because that is ultimately the...
00:17:43
Speaker
um The key thing in our industry is, will people make decisions that have a long-term consequence down the line? that's That would be the absolute worst thing to have happen. But then also, is there going to be a deleterious effect on an organization? you know Will people make decisions that end up resulting in a lack of compliance and then all of that cascade of of challenges? So we want to make sure that it's done in a little way that's um transparent, that it's secure. We deploy in fully private um and of AI models, AI servers.
00:18:13
Speaker
We can also integrate with a customer's own um LLM or their own kind of AI system. We really want to kind of make sure it's secure, transparent, accountable. People know that this data is coming from AI. And then the decision ultimately lies with a person, not an automated process.
00:18:30
Speaker
Well, thank you, Josh, so much. i've I found this really interesting and I'm really looking forward to learning more about AI and and seeing that develop. So I know that you and some of your other colleagues have done some brilliant webinars recently looking at specific use cases for AI.
00:18:45
Speaker
I actually watched one recently about how it can assist with the management of agency correspondence, which was super, super interesting and relevant. So as usual, we will link those.
00:18:57
Speaker
um And we'll also have some other resources aimed at helping companies kind of look at where they are in their AI journey and what AI can do to support them.
00:19:10
Speaker
So thank you so much, Josh. Thank you. It's been a real pleasure to to chat with you as ever.
Understanding IDMP and EMA Updates
00:19:28
Speaker
Pierre, thank you for joining us. I know you've been focusing on IDMP for quite some time now, is that right? Yeah, so when I joined anov almost 18 years ago, we were working on a publishing tool, so publishing the ECTD formats to provide the data to the health authority.
00:19:46
Speaker
And this is when we started to think about RIM solution. There were very little RIM solutions on the market at the time. and initially developed so in 2016 our first rim solution and initially we thought we would just develop directly the idmp without xmpd but because it was delayed several times we we developed xmpd in 2020 but really started working on the idmp standard ah for almost nine years now ah so the characteristics is we've developed both systems in a common framework so that was
00:20:23
Speaker
actually very useful to have those two use cases of structured data formats that would be submitted to do the Health Authority from the RIM so that we can optimize different production of structured data from the same system.
00:20:40
Speaker
Since then, on the IDMP, we've participated to all tests that EMA proposed. So they proposed several UAT. on their their system, the system that we put in place for SPOR.
00:20:53
Speaker
So one for RMS, the Referential Management Service, and OMS for organizations, where we made the very early tests. Then later SMS, and today the PMS database, which is the main database for product information, where we also do a UAT this year.
00:21:14
Speaker
And I should say also that we were among the very few vendors that produced examples of PMS ah fire messages ah when EMA had test period at the time, a couple of years ago.
00:21:30
Speaker
So IDMP has really been on the regulatory agenda for quite some time. Like you say, back in 2016, it felt like it was something we were going to be moving to fairly quickly, but we've been stuck in sort of waiting mode for a while.
00:21:44
Speaker
so for those who are just sort of refocusing on IDMP, can you give us a brief introduction to what it actually means for them? Yes, IDMP is an acronym that means identification of medicinal products.
00:21:57
Speaker
ah So it's an ISO standard that aims at describing basically drug product information in a structured format. So what it does, it provides both concepts and also different components and fields that are make up a medicinal product.
00:22:17
Speaker
So for example, the packaging, the pack sizes, ah the composition, ah the dose form, route of an administration or education, all this kind of information.
00:22:28
Speaker
ah are provided with some guidance on how they should be managed ah in the IDMP format. So what what it does is that it helps harmonize ah the definition of product information ah across systems, but also across domain, like from clinical to regulatory to pharmacovigilance.
00:22:49
Speaker
And one of the use case ah is for pharmacovigilance already to improve the signal detection. But also there's going to be a concrete application for the end users, like the capability to scan ah QR code on your box to contain your drugs, to be able to access to an electronic leaflet on your smartphone.
00:23:10
Speaker
This is one of the concrete examples that the health authorities are putting in place. Yeah, I've seen the EMA developing some of their electronic product information. It's looking really cool.
EMA's Agile IDMP Implementation
00:23:22
Speaker
So this past year has really felt like a big one for IDMP. After a long time watching and waiting, it feels like all of a sudden the EMA is moving really fast and there have been a lot of changes happening quite quickly.
00:23:33
Speaker
yes that's right. So if we get in back in time a little bit, um we had first a step when EMA had this PMS database, which is the main database that contains product information.
00:23:46
Speaker
They had it live internally only, and they started to use it for their systems. like the electronic application form and the PLM portal. And then in 2022, EMA decided to adopt an agile framework and an agile methodology. So instead of having a big milestone of when the whole system would go live, they had ah more short-term steps shared visibility on their progress.
00:24:17
Speaker
So that was very interesting for us because it gave us more information sooner to be able to start preparing our systems ah to comply to whatever they will ask in the future.
00:24:29
Speaker
So there was a PMS, first go-live with the PMS database, just read only, which was ah fed by information that EMA had from XMPD and Ciamed, one of their internal database. So that was already very useful. It took place a ah around end of Q2 this year, it was already very useful because it helped us assess the ah difference between the RIM information and the PMS information and start preparing ah the submission, the enrichment that EMA would ask.
00:25:07
Speaker
So now what EMA is doing is they will ask successively steps where MAH would need to enrich the information that they have migrated.
00:25:20
Speaker
So the first enrichment is about back sizes and manufacturers. So yeah, suddenly we've been asked really for the first time to submit data for IDMP in the form of this enrichment.
00:25:34
Speaker
So what are those next steps that MAHs need to think about to get ready for that enrichment? Yes. So IDMP started relatively slowly, ah may give the impression that it's not really an emergency and that people still have time to submit, to comply with whatever regulation will come. But actually, it is quite urgent to have a RIM that is IDMP ready first, because EMA will ask more and more enrichment data into the PMS database.
00:26:08
Speaker
And if you're not prepared enough, you might have to do this manually, which would be really a loss of time and and resources. So the next, the first risk, the first step that and MA should think about he make sure the data is available.
00:26:26
Speaker
The IDMP required data is ah in some format, format in the right format and available, and that they start connecting with the PMS ah look at the submission deadlines and ah plan for this capability.
00:26:46
Speaker
It could be there is a first milestone at the end of 2025, and there will be, this is for critical medicine products only, and there will be a second milestone at the end of next year for all the rest of the product on a certain scope of the data.
00:27:01
Speaker
So MAH should really start checking that have everything in place to be able to answer to all those needs. And I think you spoke there about there is that manual alternative to work through the ah product, the PMS user interface, but I've seen some demos of that, that the EMA has done. It it does look quite manually heavy and quite clunky.
00:27:23
Speaker
So what we're sort of talking about here is making sure that your RIM systems or you've you've got a system in place that lets you do some of that in a more automated way, right? Yes, exactly. And it's all question of volume because if you have ah maybe only five or ten per registration, you might still handle it manually.
00:27:43
Speaker
But the number of registration can go very quickly if you have multiple products and and you register in multiple countries. So it's you can easily have several thousands or hundreds of thousands of registration.
00:27:57
Speaker
And then you really need need need a system to be able to submit this information ah using the API. So even small companies really should consider something better than the manual approach.
00:28:13
Speaker
Definitely. I think the natural and analogy there is XCVMPD. You know, I've seen companies that are smaller, they manually manage all that XCVMPD. But as you increase that volume, it really doesn't work as well to do it that way.
00:28:27
Speaker
You've got a lot more error, a lot more more time invested if you try and do it all manually. And actually, speaking of XEVMPD, one of the things I was surprised to see was how much focus there still is on XEVMPD. You know, it's really the source of a lot of that PMS data that's coming in.
00:28:42
Speaker
And that feels to me like a big change since the EMA first started talking about their target operating model, right? Yes, that's true. So initially, ah we had the idea that IDMP was going to replace XEVMPD and that we would not need to focus on it very much.
00:28:59
Speaker
But it turns out today that the at least for the first steps, the XMPD and IDMP are quite correlated. For example, ah you may ask as a first step to make all the XMPD submission at the pack size level.
00:29:12
Speaker
So that's already a requirement to start using PMS database and enrich the data. And so, ah It's true that at some point XMMPD will be decommissioned, but it's still several years away from now because one of the reasons being that EMA still has different systems that rely on XMMPD and the transition will take some time.
00:29:39
Speaker
So for the moment, XMMPD and IDMP will be two parallel ah requirements and submission process. So people are really gonna have to consider how they're going to submit to both of these systems.
00:29:53
Speaker
And PMS is really just another place for and MAHs to be managing their data. What actions would you recommend that they take right now?
Preparing for IDMP Compliance
00:30:04
Speaker
So the important thing is to check whether they're ready for the EMA enrichment deadline i three levels. One is the data, the second is the tool, and the third is the expertise.
00:30:18
Speaker
So one of the things we recommend, very simple, is to download our IDMP starter pack, which would guide you through all those different steps to preparing for IDMP.
00:30:29
Speaker
And it will also help you evaluate your readiness in terms of data. For example, the question should be, do I have all the IDMP fields according to the IDMP guidance, implementation guidance, chapter two?
00:30:44
Speaker
do i have Do I use all the control terms that are required, the control vocabularies, ah both in the RMS, but also the organization identifiers and the substance identifiers?
00:30:56
Speaker
And then do I have a process in place to extract and generate the data in the right format, validate it and submit to EMA? And of course, the EMA is user acceptance testing for that right API for PMS. That's starting soon as well, right?
00:31:11
Speaker
Yes, it's ah right now it's planned for Q2. ah It may be a little bit delayed, ah but we hope as we hope it comes out as soon as possible because a lot of pharma industry ah users would like to use the API and avoid doing a manual submission.
00:31:30
Speaker
um We also expect that EMA proposes batch upload tool ah using an Excel spreadsheet. but it will not be as convenient to use as the API. API will be much more powerful.
00:31:46
Speaker
Pierre, thank you so much. I know we'll definitely be discussing IDMP again in the future. And I'm really looking forward to seeing how it's going to continue to evolve over the next year or so. Thank you. And of course, up on the NAV website, as always, we've got some fantastic resources for IDMP. So we've got a timeline so you can make sure that you are ready for all those EMA timelines that Pierre has been mentioning.
00:32:09
Speaker
And we've got that brilliant IDMP starter pack really guiding you through getting ready and making sure you've got everything you need to meet those timelines as well. Thanks for tuning in to the Innovation Podcast. At Inove, we help over 450 life sciences companies streamline compliance, enhance efficiency, and achieve their regulatory goals with our unified platform.
00:32:31
Speaker
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