The key to controlling any process resides in understanding its variability. How much do we know about the variation of our processes or systems, and are we in a state of control? These questions require answers to achieve success in the execution of the equipment cleaning process. Understanding, detection, response, and control — from input to output of variation — consumed the focus of the revised FDA process validation guidance document.
So how do you control variation? You have to enable a risk management system. To be highly effective, a risk management system must be initiated early during the design and development of the equipment cleaning process (ECP).
A rigorous application of risk management tools during Stage 1 (process design stage) will help you assess, understand, and ultimately control the level of variation in systems and processes. Therefore, critical quality attributes and critical process parameters should be established during risk assessment exercises.
Using Risk Management to Develop DoE Studies
In addition to risk assessment exercises, experiments should be conducted to attain data about the cleaning process being developed and understood. These stage 1 process design studies should utilize a statistical design of experiments (DoE) where appropriate. DoE is “a structured, organized method for determining the relationship between factors affecting a process and the output of that process.”
As stated in the FDA process validation guidance, “Risk analysis tools can be used to screen potential variables for DoE studies to minimize the total number of experiments conducted while maximizing knowledge gained.” Further, the guidance says that “the results of DoE studies can justify establishing ranges of incoming component quality, equipment parameters, and in-process material quality attributes.”
At the time of the process design, it is generally recognized that not all possible sources of variability will be known. However, if risk management is exercised to develop insightful DoE studies, they should help develop a low variability process.
Comparative Cleanability Studies
Additionally, comparative cleanability studies could be performed to compare products. Typical evaluation factors include (but are not limited to):
Solubility of active
Cleanability of the active concentration
Complexity of the cleaning procedure
It is recommended to perform studies and plot data, normalizing them when appropriate so they can be presented on the same plane and previously unknown sources of variability can be found.
Critical Cleaning Validation Tasks
The use of data collection, analysis, and evaluation are the most critical tasks in a cleaning validation program. The earlier practitioners start realizing this, the better understanding of variability they will achieve.
Editor's note: This blog post is part one of three-part series examining the three stages of cleaning validation. Links to the other posts are listed below.
Industry Expert Igor Gorsky has been a pharmaceutical industry professional for over 30 years. He held multiple positions with increasing responsibility at Alpharma, Wyeth and Shire. He worked in Production, Quality Assurance, Technical Services and Validation including an Associate Director of Global Pharmaceutical Technology at Shire Pharmaceuticals. He is currently holding a position of Senior Consultant at ConcordiaValsource, LLC. His over the years accomplishments include validation of all of the aspects of pharmaceutical and biotechnology production and quality management, technical support of multi-billion dollar drug product lines and introduction of new products onto the market. He had published articles and white papers in pharmaceutical professional magazines and textbooks. In addition he had been a presenter at Interphex. He is also very active with PDA participating in several Task force groups authoring PDA Technical Reports 29 (Points to Consider for Cleaning Validation) and 60 (Process Validation). He is leading PDA Water Interest Group. He holds a BS degree in Mechanical and Electrical Engineering Technology from Rochester Institute of Technology.