Complex doesn’t begin to describe IDMP–the new set of international standards for identifying and describing medicinal products, that is currently being rolled out in Europe in phases, despite a plethora of delays and revisions. Short for “Identification of Medicinal Products,” IDMP is actually meant to make the tracking of medicinal products more streamlined in a global market, by standardizing descriptions of substances including dose forms and units of measurement.
That may sound simple, but many life sciences and R&D organizations don’t have IDMP on their radar, and are not set up to meet these standards. In a 2017 survey of life sciences companies conducted during a Pistoia Alliance webinar:
- 42% of respondents said they knew very little about IDMP
- 25% said they had only a basic understanding of the upcoming global regulations
Just as surprising in our high-tech age, 40% said their regulatory and R&D divisions still use “unstructured paper and PDF-based reports” to exchange information on substances.
“The goal of IDMP is to make sure companies have a truly standardized description of their products. But most still store their data in all sorts of formats, files and data bases, which means an organization’s internal description of a product or substances may be very different from what the regulators want,” says Paul Milligan, senior product manager at Linguamatics, a text-mining software company based in Cambridge, England. (CCC and Linguamatics are partners—Linguamatics’ I2E software is integrated with CCC’s RightFind™ XML for Mining.)
The good news is, with foresight and the right systems in place, life sciences and R&D organizations will not only be able to comply with the new standards, but can reap benefits that translate into time saved, problems solved and the potential for more profits down the line.
I talked with Paul Milligan about what IDMP issues should be top of mind:
What are the problems life sciences and R&D organizations face when it comes to complying with IDMP standards?
Paul Milligan: The basic challenge is for companies to get their own internal data into a format that can then be shared with regulators. It’s not that companies haven’t been providing this information—they have. The problem is, the different sources of information necessary to meet the IDMP labeling standards have typically been siloed in different data bases. That creates a challenge in terms of bringing the necessary pieces of information together in a timely fashion that makes sense with a company’s workflow.
Meeting IDMP standards is going to require a big push from pharmaceutical companies, biotechs and other stakeholders to break these silos down and find a systematic way of overcoming the technical barriers. The good news is, once that happens, it will be easier for everyone involved to learn from and explore the data, spotting new patterns, speeding regulatory submissions, and tracking adverse events.
What else do companies need to do, beyond gathering and standardizing the required information?
PM: The whole idea of IDMP is that a broad set of data elements need to be tied in with the product, such as manufacturer, indication, adverse events, along with dosage strength and formulation. On a basic level, that means organizations will be a need to establish a scalable process where it’s easy to tell what information is going in and what is coming out, where they can extract information easily, and where everything is done systematically, so nothing is inadvertently omitted. After that, you have to be able to put the information into context, meaning that if you spot an adverse side effect somewhere, you’ll also want to know the drug that caused it, the dosage, and any information that can give meaning to the adverse event. These are prerequisites.
Is it possible for organizations to do this manually?
PM: It’s possible, but it would take a lot of people and time to gather the information, and there’s more chance of introducing human error. Pulling out the required IDMP data elements from regulatory text sources can be very time-intensive, and of course it needs to be kept up-to-date with new information. Here’s an example: Let’s say you need to review the literature for any new mentions of adverse events. If you do a standard keyword search, you type in an adverse effect and a drug and then you have to wade through all the documents to find the relationship between these two terms, otherwise you won’t be able to tell if a particular drug is causing the adverse effect.
If you’re using a machine-based approach to information extraction, you can immediately say, “We’ve found this term and it’s being reported as an adverse event caused by this or that drug.” Text mining can be a powerful way to pull out the adverse events in the data without having to read every document—the machine is doing the initial info-grabbing and summarizing—and specific, relevant documents can be read later.
Are there any unlooked-for benefits that could result from the push to satisfy IDMP?
PM: Organizations will be able to identify potential problems with products earlier in the development process. Text mining software, for instance, can rapidly sift through the scientific literature on a particular drug, extracting relevant notes on patients from clinical trials and identifying any adverse events sooner rather than later. That’s going to save organizations time, effort and money so they can focus their attention on what really matters—developing drugs, designing trials and getting the products submitted to regulators.
Organizations don’t want to throw out processes that have been working for them for years. How can new and old systems be easily integrated?
PM: By definition, people who pay attention to regulatory processes are cautious—and no one wants to have to reinvent the wheel to meet these new requirements.
One way for pharmaceutical companies to approach IDMP would be to have their normal team of reviewers looking at data and spotting errors and add in a layer of automation for super fast review cycles.