Correcting the Most Common Causes of Pharma Process Deviations
Keeping your pharmaceutical manufacturing processes under control is important not only to ensuaddre a quality product, but also for regulatory compliance. Process or raw material deviations can affect the downstream quality of a product and could mean tossing out an entire batch or end product if process corrections aren’t made soon enough — or if you can’t document that a correction was made before it affected your critical quality attributes.
This article is posted on our Science Snippets Blog.
Using tools and processes designed to identify and overcome process deviations can help you keep all your product production processes in compliance.
Keeping Processes Under Control
Your pharmaceutical production must follow a strict manufacturing process that adheres to the design space filed when the drug or product was approved. If the manufacturing process deviates from this design space, the product may not be safe for use by patients and therefore cannot be sold. Under current regulatory standards, such as those outlined in a recent regulatory guidance from the FDA, you must be able to show that your entire production run fulfilled the specifications of your validated process.
We recently discussed some of the top causes of pharmaceutical deviations. Now let’s take a look at what to do about them. How can you effectively correct process stordeviations before they have negative consequences for your product?
Ways to Correct Process Deviations
What are some ways you can correct your process in time to prevent downstream interuptions? From planning to production, a cohesive approach to continuous process improvement can help you take control of your processes.
• Identify the Root Cause
The first step is to make sure you have identified the root cause of a process deviation or problem. The root cause is the underlying reason that allowed the event to occur. Sometimes this can be understood at the time of the event, but often it requires an in-depth technical assessment of all the relevant facts. This sort of analysis can help you put a process into place that allows corrections to be made in real-time in the future.
Finding the true root cause is critically important. Failing to identify and correct the true root cause of a problem, especially one that could be readily solvable, could lead huge losses in revenue. You may be able to identify root cause is using a simple analysis tool such as the “5 whys,” or other tools as a fishbone diagram, or a more thorough approach like Kepner-Tregoe analysis or Six Sigma process improvement.
Read more about continuous process improvement and root cause analysis in this article: How data analytics tools support the five DMAIC phases of Six Sigma
• Evaluate Contributing Factors
Like finding the root cause, having a clear understanding of the contributing factors for a deviation is important to be able to prevent future problems. Contributing factors are elements that (in addition to the root cause) were necessary for the event to occur or that increased the event’s impact.
You’ll need to do root cause analysis for contributing factors as well, and document the corrective and preventive actions (CAPAs). Addressing these issues helps limit the likelihood or impact of similar events recurring in the future. The 5-Whys tool is a useful mechanism to distinguish contributing factors from root causes.
• Outline Effective Corrective Actions
Your investigation into a root cause or contributing factors for a process deviation shouldn’t end there. Make sure you take the next step of prescribing corrective and preventative actions (CAPAs). After all, your main purpose for investigating a deviation is to prevent the recurrence of the event, and this can only happen if you connect the specific cause to an appropriate CAPA. You may need to change your process as result, or conduct a risk assessment to determine whether or not an interim control is needed while the corrective action is being implemented.
• Change Your Process to QbD
In addition to outlining corrective actions, your investigation into root causes and contributing factors for process deviations could lead you to change your overall process. Continuous process improvement supports the notion that quality is tied to an iterative, systematic approach to development and production. So ideally, you want to start your development off on the right foot using a Quality by Design (QbD) approach that begins with predefined objectives and emphasizes process understanding and process control. Design of experiments (DOE), risk assessment, and process analytical technology (PAT) are tools that may be used in the QbD process.
• Eliminate Human Error
Keep in mind that industry regulators expect your processes to be robust and relatively free from the influence of human error. Consider this statement from Chapter 1 of the European GMPs:
Regulators see human error as a last resort. They expect that you can – and have – eliminated any possible process issues and confirmed that all necessary actionable tools were available to the operator. And ultimately, your goal is to remove any chance of human error from the process: through training, automation, corrective actions, backup processes, and accurate monitoring.
• Ensure Proper Training
Your team must be trained to identify process deviations early. A key factor in whether your production will continue to meet the standards of compliance is how well your staff is able to correct a process deviation early enough to prevent the process from being outside the approved design space. This involves both training in managing the process correctly, as well as an understanding of what sort of corrections to make.
Some elements that support effective training are:
- A process that includes monitoring for deviations
- Step-by-step corrective actions for any process deviations
- Ensuring that your operators are not expected to manage too many tasks at once
- Adequate time in the process to prevent rushing
- A system for backup or assistance to correct process deviations
• Use Real-Time Analytical Tools
If your process models are statistically accurate enough, you will be able to determine when a production process is deviating from any normal operating condition, and perhaps even predict when a current process might start deviating from accepted conditions.
That’s why a multivariate data analytics tool for real-time process monitoring is so important. A real-time analytics tool allows your production floor staff to know when a process is performing optimally or to see immediately when a deviation occurs. This early warning allows them to take the necessary steps right away to correct any issues that might cause a batch to be rendered unusable or to stop contamination of a downstream process.
A real-time data analytics solution utilizes regression models to summarize all of the individual data points from various operations into multivariate models that can be monitored in real time. This becomes very efficient in the control room because instead of looking at a large number of individual parameters or signals, you have a small set of summary parameters that let you monitor all the variables at the same time.
• Consider hybrid modeling approaches (and AI)
A hybrid modeling approach combines the elements of root cause analysis with in-depth knowledge of cell processes. For example, combining knowledge of the life cycle of the cell with historical data can create a robust model detailing what the normal behavior of the process is. Then, to find the root cause, AI can be used to map out the characteristics of previous known failures along with the parameters of an existing process to find the most likely culprit of any process deviations.
Using tools and processes designed to identify and overcome process deviations can help you keep all your production processes in compliance – saving you time, money and resources in the long-term and protecting your reputation in the market as a provider of high-quality, trustworthy pharmaceutical products.
Want to Know More?
Read this quality-by-design case study that shows how a multivariate study can be used to successfully predict formulation robustness of a biopharmaceutical product with a specified shelf life.