Next week, a virtual conference will feature a prerecorded presentation by Redica Systems Senior GMP Quality Expert Jerry Chapman, one of our Industry Experts.

Jerry took a few minutes to answer some questions about his talk.

Conference Details

Date: Wednesday, March 17
Event: virtual 2021 PDA Annual Meeting 
Presentation by: Redica Systems Senior GMP Quality Expert Jerry Chapman
Presentation: “Enabling Quality and Regulatory Professionals with Structured Data and Actionable Insights Using AI”
Session: “A6: Leveraging Data Analytics along the Product Lifecycle”
Time: Begins at 12:30 p.m. EST
Moderator: Novartis Manufacturing Science and Technology Global Head of Systems and Standards, Arne Zilian, PhD

Interview with Jerry

It is exciting that you will be presenting at the virtual 2021 PDA Annual Meeting.  Can you describe your presentation?  What will it cover?

Jerry Chapman

Jerry: Our entire industry has been migrating our procedures, documents, and methods from paper-based to digitized formats.  However, the analysis of agency enforcement documents such as 483s and GMP warning letters has remained a manual process using hard copies and a highlighter.  This is no surprise, as experience is required to accurately identify and categorize the information.

Redica Systems has developed an algorithm to evaluate and tag documents to facilitate expert analysis and replace the hard copy and highlighter process.  Data scientists and industry quality experts worked together to train an AI system to parse, identify, categorize, and organize the key elements from enforcement documents, freeing up experts’ time for other focused activities.  Informed by the AI algorithm, experts can develop insights and take actions to mitigate risks that the AI discovered buried in the data. 

In this presentation, I discuss the natural language processing tools that we used and why, and how we built our AI “expert models,” which can analyze enforcement documents the way an expert would.  I will demonstrate the utility and accuracy of the expert models with two case studies, one of which analyzes the nuanced differences between compliance gaps in compounding versus traditional manufacturing facilities and another use case that extracts data integrity signals directly from warning letters.

[Related: Gain access to the same data Jerry used for his presentation with a free trial of our platform.]

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Why is it important for quality leaders in the pharma GMP sector to use data analytics?  If I am in charge of quality for a site, how can leveraging inspection data help me?

Jerry: The use of data adds credibility and predictability to proposals and creates more reliable predictors of outcomes.  Specifically regarding GMP inspections, what is even more important than the rules, regulations, and guidance from regulatory agencies is how they are enforced during manufacturing site inspections.

In other words, what do FDA and other agencies expect to see when inspecting a production facility? Those expectations are difficult to find and always changing.  The only place to find out that is expected as current—the “C” in CGMP—is by evaluating enforcement reports such as FDA 483s and warning letters and looking for trends and commonalities in inspection reports for products and processes similar to the ones in your facility.  That exercise is time-consuming and difficult and requires expertise that is not easy to come by. 

At Redica Systems, we enable that analysis using our proprietary algorithms.

If I am a quality leader and want to learn more about using inspection data, where should I start?  What resources are available to me?

Jerry: Redica Systems has built the largest integrated dataset of inspection and enforcement documents in the world.  These are the datasets I use in my presentation for the case study analyses.  Our data library includes all FDA inspected and registered sites since 2000, comprising 20 times more data than has been released on, which we have obtained by targeted Freedom of Information (FOI) requests. 

But once you get the inspection data, do you have the experts to analyze it?  And if so, do they still print hard copies and use highlighting markers to analyze them?  Redica Systems has better ways to do this. We have built and implemented proprietary algorithms using machine learning and honed by industry experts.  These expert models evaluate inspection documents the way an expert would, revealing not only obvious agency findings but also issues that experts would see, “hiding in plain sight.”

Who would benefit from attending your presentation next week?  What GMP roles can take advantage of data analytics?

Jerry: Anyone who has an interest in learning how to find out how GMP guidance and regulation translate into expectations in agency inspections would benefit from hearing the presentation.  Also anyone who conducts or supervises or manages the preparation or conduct of internal and external inspections would benefit. 

But once you get the inspection data, do you have the experts to analyze it?

The presentation will include case studies examining the differences in compliance positions of 503B outsourcing companies and pharma sterile manufacturing companies, as well as a deep dive into the inspection landscape for data integrity citations in warning letters by international geography, so anyone with an interest in those topics would also benefit.

As someone with years of GMP industry experience who now works with data, how do you see data analytics changing the industry in the next five years?

Jerry: As our industry continues to become more global and complex with intricate international supply chains and changing regulatory requirements in countries where firms are marketing their products, it becomes more and more difficult to keep current with regulatory agency expectations.  The only way to ensure you stay up-to-date is by understanding and leveraging large datasets and the kinds of accompanying analytics that Redica Systems has available.

The trend in the pharmaceutical industry is to move toward what has been called “pharma 4.0.”  This encompases an advanced business model for mature pharma organizations in which digitalization of all records and control strategies and enablers, data integrity “by design,” and the embodiment of the Pharmaceutical Quality System as described in ICH Q10, among other features, are key.  

The only way to ensure you stay up-to-date is by understanding and leveraging large datasets

This type of model has proven to be a successful operating model in other industries.  A hallmark includes interconnected systems and data analytics used for decision-making.  The ability to bring large amounts of information to bear on strategies as they are being designed and on problems as they emerge will be critical.  This includes the use of “big data”—datasets that are too large or complex to be dealt with by traditional data-processing methods.

Redica Systems enables pharma, medical device and food manufacturers to use our unmatched integrated data libraries and AI to reduce risk in large manufacturing environments and ensure regulatory compliance.  

We facilitate pharma, medical device and food manufacturers’ usage of large data sets and AI to reduce risk in large manufacturing environments and ensure regulatory compliance.  The company’s unique platform provides customers with insights to continuously improve compliance, reduce supplier risk and increase team efficiency. 

We have worked closely with our customers in order to shape Redica’s strategic focus, which involves using actionable data intelligence to build up to an entirely new way of thinking about, simplifying, and guiding regulatory compliance.

[Related: Gain access to the same data Jerry used for his presentation with a free trial of our platform.]

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