Oliver Stauffer

	Package integrity, which assures product stability long after the product and package leave the warehouse and continues through the supply chain, is of crucial importance. Package integrity testing assures the package barriers are intact and will protect the product from environmental contamination or leakage for the shelf life of the product. Taking the right approach to establish package quality testing protocols and implementing the proper test method is no longer an option but is necessary to reduce patient or consumer risk by delivering high-quality, defect-free packaging.  

	A critical packaging failure that occurs on product destined for the market has a compounding impact.  An asset quickly becomes a liability, hammering shareholder equity two-fold. Valuable engineering and operations resources are taken away from revenue-generating activities and put toward mitigating quality issues. A high level of package quality assurance, however, is aligned with patient needs and defends against costly quality deviations. Reliable and robust package testing solutions are often non-destructive and quantitative in nature, providing the right information when it is needed most.   

 <Chapter 1207> Sterile Product Packaging-Integrity Evaluation will be published in the first supplement to 

34 on Feb. 1, 2016, and will become official on Aug. 1, 2016. Related subchapters will be published at the same time.

<Chapter 1207> Sterile Product Packaging-Integrity Evaluation was recently rewritten, graduating it from a soft guide on container closure integrity to a more detailed document that establishes best practices for container closure integrity testing (CCIT).  The document presents relevant methodologies that can be deployed for CCIT, giving industry experts and FDA a menu of testing capabilities, but the chapter recommends deterministic methods that are quantitative and definitive in measuring the integrity of a package. Deterministic methods are deemed to have fewer input variables that could impact test results, and may also provide more definitive quantitative results. The chapter defines probabilistic methods as those that have more undefined input variables and greater opportunity for subjective interpretation.  Studies have shown that CCIT methods deployed at different sites by different operators may produce variation in test results.  Methods that rely heavily on operator intervention increase the risk of error in performing a test or variable results due to subjective interpretation.  The bottom line is that deterministic methods produce more reliable test results and put the patient first. 


	Package integrity testing can be performed using a variety of destructive or non-destructive methods. Some methods have little recourse outside a destructive test. Peel and burst, for example, are direct and explicit measures of package quality as it is being destroyed. Once the sample has been subjected to the test, that specific sample no longer can be used to perform supplemental tests and the data gathered are final. Any further investigation or a failure analysis of the sample is not an option. With destructive test methodology, valuable information is not always available as feedback from quality to manufacturing. 

	There are a number of reasons to consider non-destructive package testing, including the reduction of production line waste. The underlying motivation is to obtain reliable and accurate information. When implementing a non-destructive test method for package integrity testing, the tools available to improve production line quality expand greatly.  Samples can be tested, retested, and returned to the production line. There is no cost to testing additional product, therefore, testing frequency can be increased. 

	If a tested sample shows as defective, nothing about the sample has changed, and it holds valuable information. Using a non-destructive method to trace the cause of failure and correct the issue is now possible, and process and quality control can be achieved.


	Quantitative measures of package quality are just as important in the pursuit of quality and are a greater focus of the new 

 chapter. Quantitative measurement of a characteristic will typically provide more accurate information when observing something. While subjective attribute results provide color and description to a situation, these types of data may introduce far more uncertainty to a scientific measurement process. Many variables affect test results, yet some variables such as operator interpretation are difficult to control.

	Methods that rely on subjective interpretation suffer from a variety of biases. Confirmation bias dictates that an observation will be influenced by an operator’s preconceived notions.  If an operator assumes samples are good, they will be less likely to identify defects and vice versa. The Hawthorne Effect dictates that operators under observation will perform more effectively than if there is no immediate oversight. Significant research also shows time on task greatly reduces the ability for operators to detect certain defects.  The human psyche has a significant impact on measurement processes when subjective methods are deployed. Quantitative test methodology aims to remove these sources of variability.

	Without accurate and reliable test data, organizations cannot make the right decisions.  The foundation of Six Sigma quality improvement is based on quality of “measure” (“M” in define, measure, analyze, improve, and control [DMAIC]).  If the basis of the Six-Sigma process is based on soft or attribute-based data, it will be challenging to analyze the test results and achieve control of the process (“A” and “C” in DMAIC, respectively).  Deterministic methods lay the foundation for quality.  Quantitative test results allow for a wide variety of statistical tools that provide unbiased and clear results.  Distribution curves can determine if a process is within performance limits, and test results driven by hard facts will command clear actions at the operator level without subjective interpretation. 

	Deterministic methods that rely on quantitative measures can be traceable in time and space.  A test method performed on one location should be repeatable at another location around the world and produce comparable results.  Comparing results across locations as well as time is important for performance tracking.  Performance levels of a process in one time period can be compared with later time periods.  The timeless nature and reproducibility of test data is a powerful tool for any global operation in the pursuit of improving quality. 

 Chapter 1207 lists a number of container closure integrity test methods.  It provides typical test-method sensitivity that an end-user can reasonably achieve, the type of data produced, and the effect a method may have on the container-closure system.  The document highlights key quality criteria that need to be understood when choosing the correct test method, prompting the end user to ask the right questions for their own container-closure system and how a test method may relate.  The chapter does not eliminate the use of subjective, destructive, or probabilistic test methodology, but it does highlight the need for reliable and accurate test data, as well as robust test methodology.

	Technological solutions available to assure container closure integrity have seen rapid development.  The evolution of 

 1207 is an effort to inform industry professionals about the options that exist, and to initiate a drive towards more reliable measures of quality in a patient-centric environment.  Each container tested provides valuable knowledge to apply to the manufacturing process, and knowledge is power.

Oliver Stauffer is vice-president and COO at PTI, Packaging Technologies & Inspection.

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	When referring to this article, please cite it as O. Stauffer, "Using Deterministic Container Closure Integrity Testing," 

Articles

The revised USP Chapter  Sterile Product Packaging-Integrity Evaluation gives best practices for obtaining reliable data in container closure integrity testing and recommends quantitative deterministic methods rather than probabilistic methods.

Advancing Container Closure Integrity Testing

 Chapter <1207> Sterile Product Packaging-Integrity Evaluation will be published in the first supplement to 

Chapter <1207> Sterile Product Packaging-Integrity Evaluation was recently rewritten, graduating it from a soft guide on container closure integrity to a more detailed document that establishes best practices for container closure integrity testing (CCIT).  The document presents relevant methodologies that can be deployed for CCIT, giving industry experts and FDA a menu of testing capabilities, but the chapter recommends deterministic methods that are quantitative and definitive in measuring the integrity of a package. Deterministic methods are deemed to have fewer input variables that could impact test results, and may also provide more definitive quantitative results. The chapter defines probabilistic methods as those that have more undefined input variables and greater opportunity for subjective interpretation.  Studies have shown that CCIT methods deployed at different sites by different operators may produce variation in test results.  Methods that rely heavily on operator intervention increase the risk of error in performing a test or variable results due to subjective interpretation.  The bottom line is that deterministic methods produce more reliable test results and put the patient first. 

 Chapter <1207> lists a number of container closure integrity test methods.  It provides typical test-method sensitivity that an end-user can reasonably achieve, the type of data produced, and the effect a method may have on the container-closure system.  The document highlights key quality criteria that need to be understood when choosing the correct test method, prompting the end user to ask the right questions for their own container-closure system and how a test method may relate.  The chapter does not eliminate the use of subjective, destructive, or probabilistic test methodology, but it does highlight the need for reliable and accurate test data, as well as robust test methodology.

The revised USP Chapter 1207 gives best practices for obtaining reliable data in container closure integrity testing.