Why the FDA Encourages Continuous Manufacturing Supported by Data Models

Data Analytics

May 11, 2021 | 8 min read

The FDA and other regulatory agencies encourage pharma and biopharma companies to adopt continuous manufacturing using data models because it improves process control, reduces variability and improves product quality and consistency.

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The FDA encourages manufacturers to adopt continuous manufacturing using data models, because it improves process control, reduces variability and improves product quality and consistency.

Companies looking for ways to shift from traditional batch processing to continuous manufacturing rely on advanced data analytics from integrated systems like those supporting digital transformation. But even without a fully integrated digital system of production, data analytics tools that use real-time monitoring and control provide dramatic results. The main advantages include setting the stage for modularity, increased automation and flexibility due to a smaller footprint, as well as more consistent quality of the drug product.

Another important area of focus in scaling up from batch to continuous production is to ensure the product continues to meet its critical quality attributes (CQA). Using a Quality by Design (QbD) approach makes scale-up from a smaller model to mass production more reliable. QbD relies heavily on data analytics, including for Design of Experiments (DOE), and creating production models that ensure accurate scale-up.

DOE has become a key tool in implementing QbD in bioprocess development. Applying this approach to optimize bioprocesses early on leads to major benefits in product performance together with improved economics based on limited resources.

At the same time, QbD and continuous manufacturing rely on rigorous models developed from Multivariate Data Analysis and Real-Time Process Monitoring. This means that a future-proof approach to continuous manufacturing and regulatory approval relies on a data-based approach grounded in the use of the latest technology and digital transformation.

Regulatory Support for Continuous Manufacturing

The regulatory bodies such as the FDA see the promise of these processes and actively encourage companies to implement this manufacturing strategy. This is illustrated by the recently (2019) published draft guidance from the FDA for the production of small molecule drugs using continuous processes and the active discussion with the (bio)pharmaceutical industry through their Emerging Technology Program.

The FDA considers multivariate models to be surrogates for traditional release tests [1], for example, a stand-in for dissolution. Christine Moore, as acting director of the FDA, described the use of real-time release testing (RTRT) as:

“The ability to evaluate and ensure the quality of in-process and/or final product based on process data” that “typically include a valid combination of measured material attributes and process controls.”

Moore asserted that real-time release testing provides for increased assurance of quality as well as increased manufacturing flexibility and efficiency. This means:

Shorter cycle time
Reduced inventory
Reduction in end product testing
Reduction in manufacturing cost

It also allows manufacturers to leverage enhanced process understanding to make corrective actions in real-time.

The Trend Toward Continuous Manufacturing in Pharmaceuticals

BCC Research's report on Continuous Manufacturing for Pharmaceuticals valued the market size for continuous manufacturing at $2.3 billion in 2018, and it is expected to grow at a CAGR of 8.8% through 2024 to reach $3.8 billion in 2024.

From a business perspective, continuous manufacturing promises to reduce costs and help products get to market faster. Better control means improved product quality and easier standardization, making it easier, for example, to manufacture biosimilars. It also becomes the foundation for a robust quality by design (QbD) strategy.

This transition to more data-driven approaches and using continuous process manufacturing are sometimes referred to as pharma 4.0. The core of this shift lies in being able to use data analytics to create forecasting models for processes and process parameters. This means you will not only be able to understand what is happening in your processes as they are running, but also be able to predict the outcomes.

Other data-drive benefits of pharma 4.0 include:

Model predictive control (MPC) – paving the way for more autonomous processes
Holistic overviews – giving you a view of your processes remotely, from anywhere
Predicting “soft” aspects – being able to predict quality in real-time based on online parameter estimations
Hybrid models – combining the mechanistic understanding of cell metabolism with data to create realistic models (or digital twins)
Increase understanding – data modeling will also improve the understanding of underlying kinetics of advanced biotransformations, such as protein separation and purification

In the end, we expect all of this together will be combined into a closed-looped model of data-based predictive control strategies getting closer to a fully automated process.

Using Advanced Data Models in a Regulated Environment

It’s clear now we live in a world of data and the possibilities of data are endless. But what does it mean to use this in a regulated environment?

First of all, you need to make sure your data and your processes for collecting data (from research and development through prediction) are compliant. It’s essential, especially in highly regulated industries such as life science or pharma, that you follow all of the required steps to ensure the integrity of your data at every stage. This is more successful when you use tools and products designed to keep your data compliant.

>> Make sure your data is GAMP 5 compliant

GAMP 5 is a set of guidelines for manufacturers and users of automated systems in the pharmaceutical industry. These guidelines describe a set of principles and procedures to help ensure that your products have the required quality. They also cover all aspects of prediction and data management from the raw materials to facilities and equipment to the training and hygiene of your staff.

>> Follow the ALCOA principle of the FDA

In addition to quality controls of GAMP 5, data integrity needs to be ensured as well, and this follows the ALCOA principle of the FDA. ALCOA is an acronym that stands for attributable, legible, contemporaneous, original, and accurate.

Attributable. Your data must be stored so that it can be connected to the individual that produced it. Every piece of data entered into the record must be fully traceable with time and origination stamps.
Legible. Your data needs to become permanent, readable and understandable to anyone using the record. And this also applies to any metadata attached to the record.
Contemporaneous. Data must be fully documented at the time it’s generated.
Original. You need to have a copy of the original record or a certified copy, and the data record should include the first data entered as well as all successive entries following that.
Accurate. Your data needs to be correct, complete, truthful and reliable.

Following FDA Guidance

The FDA encourages manufacturers to adopt continuous manufacturing practices for two main reasons:

It reduces manufacturing costs (which may reduce drug costs for consumers)
It improves process control (which improve product quality and consistency)

This helps in reducing variability and the risk of adverse reactions. From the consumer’s perspective, it may increase the speed at which drugs become available.

In their draft guidance, the FDA encourages the use of multivariate data models when creating prediction processes. In Section III.B.2. on Process Monitoring and Control, the FDA encourages the use of models to ensure data provides value.
Continuous manufacturing relies on multivariate data models that can predict process changes using real-time data input. These models summarize and transform data from univariate sensors into multivariate trend graphs, making it easier to analyze.
Some of the ways multivariate data can be used to help improve process control for continuous manufacturing include:

early detection of equipment malfunction
insight into the raw material impact
in situ assessment of process stability
predictive monitoring

Model Validation

An essential step in continuous manufacturing in regulated environments (or, in any manufacturing environment) is validating the accuracy of your model.

The model validation strategy ensures that you are continuously updating your production review methods. A successful prediction model must also consider how to handle batch data sets, and ensure they are used to confirm the reliability and robustness of the model. You also need to consider how to handle discrepancies between quantitative and qualitative models.
When validating a model for use in a regulated industry, you’ll have an extra layer of questions that need to be addressed. Some key considerations of model quality will be:

Is the model complexity correct?
Is the model capable of differentiating between good and bad?
Can it avoid having false negative or positive?
Is the model capturing a sufficient amount of variation?
Is the prediction model robust enough? Precise enough?

And, in a regulated environment, you also have the administrative part to consider. This covers user access. For example, what kinds of rights do the end-users have? Does everyone have the appropriate level of control for handling the process? What does the system look like? Are any special SOPs required?

At the moment, there are no real guidelines from regulatory bodies for model validations, but there are best practices and examples from different parts of the industry, which can provide good insight into the line of thinking within the regulatory bodies.

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Sources:

Christine M. V. Moore , Regulatory Perspective on Real-Time Release Testing (RTRT), AAPS Annual Meeting Washington, DC, 27 October 2011

Quality Considerations for Continuous Manufacturing , Guidance for Industry , DRAFT GUIDANCE , U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), February 2019
https://www.fda.gov/media/121314/download

BCC Research, Continuous Manufacturing in Pharmaceuticals: Implications for the Generics Market, global report, July 2019