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Digital Transformation in Life Sciences with Validation 4.0

Chapter One: What is Pharma 4.0?

The turning points in industrial transformation history have been characterized by revolutionary-at-the-time tools: The first industrial revolution (1.0) used mechanized power by utilizing water and steam; then came the second (2.0), which enabled mass production through the use of electricity; and finally, the third revolution (3.0), which introduced computers and automation to production lines. These transformation periods marked by revolutionary technologies have contributed significantly to the rise in industrial productivity and efficiency.

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Although the latest industrial revolution doesn’t feel too long ago, we are at yet another turning point in history. Technologies such as connected devices and Industrial Internet of Things (IIOT) have increased the availability and the visibility of data, driving the 4th industrial revolution referred to as Industry 4.0.

In the consumer world, smart and connected technologies are easy to find. Automated digital assistants like the Google Nest, Siri, or Alexa communicate with IoT-connected products such as smart light bulbs or smart blinds to carry out the commands of the user.

But in the life sciences space, these technologies are yet to take lead, still dominated by Industry 3.0 technologies, or sometimes even Industry 2.0.

Why is that?

Why is the Life Sciences Industry still behind in Industrial Transformation?

Let’s look at what 4.0 would look like if we were to apply the current Industry 4.0 model to the life sciences and pharmaceutical industries. ISPE has applied the ICH guidelines to the Industry 4.0 foundation to illustrate an operating model for achieving the promises of this latest revolution. There are four components and two enablers.

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What’s most notable here is that digital maturity is only an enabler. It’s certainly an important aspect, but there are other pillars like the culture, organization and processes, and resources that contribute just as much to realizing Pharma 4.0. We cannot get there without changing the way we operate and do things.

Chapter Two: What does Validation look like for Pharma 4.0?

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So what is hindering the life sciences and pharmaceutical industry from adopting Pharma 4.0?

Answer: One key hindrance is stale approaches to Validation.

So, what needs a refresh?

Here are some important aspects to consider:

Quality by Design (QbD)

QbD is not new, but has it been fully embraced? The more we know about the key aspects of our products and processes, the better we can focus on defining appropriate control strategies that are value-adding, and understand what’s important to ensuring the quality of products.

Data Integrity by Design

With the utilization of new technologies, data is becoming even more centric to everything we do. Data is visible and available to more people across the value stream, and different interactions between various data sets happen more frequently. Creating data-centric approaches that have high data integrity is the foundation of validation 4.0.

Integrated Environments

IIoT technologies have enabled data from various endpoints to be captured, visible, and made easily available across the entire production line.

Modern Documentation

Unlike collecting data on a piece of paper in static format and translating that data into a digital system, modern documentation tools such as electronic batch records and digital history records allow for data to be automatically stored and pulled at any given moment. So much of data documentation is digitally native now.

Shifting away from the “Document” Mindset

Let's talk about modern documentations in more detail.

When we say documents, people traditionally think of paper, or in Industry 3.0, the electronic documentation of data or paper on glass.

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But if we are to embrace the fourth industrial revolution, we need to shift our focus to how data is captured and how it’s controlled. Using frontline operations platforms like Tulip, users can directly capture data in their apps by executing their process steps and generating the necessary “documentation” required by regulators. Documentation shouldn’t be a process laid on top of production, but rather, seamlessly integrated into the production process itself.

Chapter Three: What approaches can we take to fuel this transformation in Life Sciences?

Besides the general shift away from this traditional “document” mindset, let’s look at an integrated approach to fuel the Industry 4.0 transformation in Life Sciences and Pharma.

Integrated Approach

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With a traditional approach, operators will look at each piece of equipment and go through the IQ, OQ, PQ process in a serial fashion. Then they will move towards a computerized system of IQ, OQ, PQ, then maybe look at the process behind it.

However, with an integrated approach, operators can group all of these steps into one holistic validation process.

Validation Approach

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In this approach, we get to see the transition of traditional user requirement documents go from a bulletized type list to something more of a visual workflow. This way, operators can do risk assessments based on the context of where activities are happening in facilities and where the data is flowing, and appropriately design risk mitigation factors directly into their systems.

Integrated approach: QbD + DIbD

Let’s take a deeper look into the visual workflow style of the validation approach.

So this is an example of where we have the integrated approach, which utilizes quality by design and data integrity by design principles.

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In this dummy workflow where we have different process steps and sub-process steps, we have five subsequent data life cycles:

  1. Data life cycle creation process

  2. Data processing (where data is exchanged, measured, and calculated)

  3. User report (where GxP decisions are made)

  4. Data archiving and retrieval

  5. Data destruction

Seeing these aspects in the context of not only their data lifecycle but their process gives us greater visibility into the potential vulnerabilities and the criticalities of these types of data.

First, you can assess the vulnerability risk based on factors like the automation level, human interface, or manual data recording. This is a critical data point at which facilities need to maneuver high risk.

Next, you can also look at the data in the context of its lifecycle and process and assess its criticality.

Together, these two aspects can help define what steps might be critical or might require a risk mitigation step. And once you’ve defined your control strategies, you can execute your validation process and make sure that those control strategies are in place. As a result of this process, Validation then becomes a verification of your control strategy.

Chapter Four: Quick Recap

Here are the key points of this article. We covered:

  • The Integrated Approach, a non-serial process that doesn’t have to wait until the next step is executed

  • Adopting digital artifacts, instead of using paper-heavy processes

  • Both Quality by Design and Data Integrity by Design are incorporated into your products and processes

  • Focused Validation on value-added activities while demonstrating fitness for intended use

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