Mitigate Risk With Package Quality
By Oliver Stauffer, VP/COO, PTI Packaging Technologies & Inspection
In the life sciences industry, quality is the drumbeat for profitability. Most quality initiatives are passive and driven by organizational jargon. How about real progressive change for once? Package testing and container closure integrity are cornerstones for quality. Without a quality package, there is no product. In fact, a bad package is a serious liability. Product that may be assumed good will fall short of enduse requirements, will be exposed to contaminants, and is a common cause for regulatory action. When developing new products, clarity around package quality control can better define the process requirements and set the stage for successful product launch and profitability throughout the product life cycle.
Now more than ever, innovative package designs are being used to differentiate and simplify pharmaceutical product delivery and end use. With new designs come new challenges to validate package stability and performance. A product development team must understand very clearly what level of package failure is critical to the quality of the product over the duration of the product’s shelf life. Key questions to ask include, “At what point does my package stop working?” “At what point does my barrier fail to fulfill its purpose?”
When assuring package quality, you must understand what quality actually means for your package system and product. Quality is often defined as whether or not the package has maintained a sterile barrier or has been breached.
Answer these two guiding questions when defining package quality for your specific application. (1) What is critical to the quality of the product? (2) What is critical to the package and delivery system that may affect the quality of the product? For a parenteral closure system it can be quite simple — leaks below 10 micrometers in diameter still pose a significant risk to product sterility. For medical devices the answer is far more complex. A balance of risk assessment and best-available technology usually prevails when defining quality.
Follow The DMAIC Process For Quality
In following the Six Sigma DMAIC process, once quality is defined (D), it should be measured (M). Measurement can be subdivided into categories: subjective/objective, qualitative/quantitative, continuous/discrete/attribute data. The life sciences industry is experiencing a significant shift away from the qualitative methods requiring human intervention. Quantitative and more automated test solutions are taking over as quality control solutions due to the reliability and accuracy of information. In today’s market where we battle to find the smallest microbes and assure the sterility of product at the highest confidence levels, the answers lie in accurate and precise quantitative data.
Sensory technologies continue to improve. New test-method designs are continuing to evolve, finding ways to challenge similar quality standards but with better precision and accuracy. A method that may have been implemented a decade ago with mediocre success may have matured in capability and reduced in capital cost. Technologies are now available that can pinpoint the location of micron-size defects on parenteral vials and prefilled syringes nondestructively.
Analysis (A) is used every time an audit occurs, by internal QA or external regulatory agency. During an FDA audit, handing over accurate quantitative assessments of the package and delivery system is far more powerful than a clipboard with handwritten notes and a signature. Quantitative data will provide the manufacturing team a high level of confidence when managing quality deviations or when defending the quality standards by which they stand.
Invest the time to define quality, and then investigate different quantitative and objective measures of quality. Improving (I) the process involves having the right technologies that are focused on measuring the quality of the package and the product. Today’s sensory technologies increase sampling capabilities, and quantitative methods improve access to data mining and analysis.
Control (C), the final stage of the DMAIC process, relies on the foundation of the first two steps. If you invest the time to define quality (D), you understand what you need to measure. If you implement an objective quantitative measure of quality (M), you will eventually achieve an optimal state of quality control.