Quality assurance charting methods

Validation of extraction procedures is frequently lacking. A good assessment of quality assurance implies that the extraction recoveries are verified, e.g. by spiking of standard addition. A major drawback is that the spike is not always bound the same way as the compounds of interest. For the development of good extraction methods, materials with an incurred analyte (i.e. bound to the matrix in the same way as the unknown), which is preferably labelled (radioactive labelling would allow verification of the recovery), would be necessary. Such materials not being available, the extraction method used should be validated by other independent methods, e.g. by verification against known samples and by use of a recovery SPC chart. A mere comparison of extraction methods is no validation. 

The validation process begun in Phase I is extended during Phase II. In this phase, selectivity is investigated using various batches of drugs, available impurities, excipients, and samples from stability studies. Accuracy should be determined using at least three levels of concentration, and the intermediate precision and the quantitation limit should be tested. For quality assurance evaluation of the analysis results, control charts can be used, such as the Shewart-charts, the R-charts, or the Cusum-charts. In this phase, the analytical method is refined for routine use. 

This chapter deals with handling the data generated by analytical methods. The first section describes the key statistical parameters used to summarize and describe data sets. These parameters are important, as they are essential for many of the quality assurance activities described in this book. It is impossible to carry out effective method validation, evaluate measurement uncertainty, construct and interpret control charts or evaluate the data from proficiency testing schemes without some knowledge of basic statistics. This chapter also describes the use of control charts in monitoring the performance of measurements over a period of time. Finally, the concept of measurement uncertainty is introduced. The importance of evaluating uncertainty is explained and a systematic approach to evaluating uncertainty is described. 

This chapter has considered two key aspects related to quality assurance - the use of control charts and the evaluation of measurement uncertainty. These activities, along with method validation, require some knowledge of basic statistics. The chapter therefore started with an introduction to the most important statistical terms. 

The previous chapters of this book have discussed the many activities which laboratories undertake to help ensure the quality of the analytical results that are produced. There are many aspects of quality assurance and quality control that analysts carry out on a day-to-day basis to help them produce reliable results. Control charts are used to monitor method performance and identify when problems have arisen, and Certified Reference Materials are used to evaluate any bias in the results produced. These activities are sometimes referred to as internal quality control (IQC). In addition to all of these activities, it is extremely useful for laboratories to obtain an independent check of their performance and to be able to compare their performance with that of other laboratories carrying out similar types of analyses. This is achieved by taking part in interlaboratory studies. There are two main types of interlaboratory studies, namely proficiency testing (PT) schemes and collaborative studies (also known as collaborative trials). 

Analytical laboratories, especially quality assurance laboratories, will often maintain graphical records of statistical control so that scientists and technicians can note the history of the device, procedure, process, or method at a glance. The graphical record is called a control chart and is maintained on a regular basis, such as daily. It is a graph of the numerical value on the y-axis vs. the date on the x-axis. The chart is characterized by five horizontal lines designating the five numerical values that are important for statistical control. One is the value that is 3 standard deviations from the most desirable value on the positive side. Another is the value that is 3 standard deviations from the most desirable value on the negative side. These represent those values that are expected to occur only less than 0.3% of the time. These two numerical values are called the action limits because one point outside these limits is cause for action to be taken. 

Sampling is just the beginning of the analytical process. On the way from sampling to the test report a lot of different requirements for high quality measurements have to be considered. There are external quality assurance requirements on the quality management system (e.g. accreditation, certification, GLP), internal quality assurance tools (e.g. method validation, the use of certified reference material, control charts) and external quality assurance measures (e.g. interlaboratoiy tests). 

Analytical method validation forms the first level of QA in the laboratory. Analytical quality assurance (AQA) is the complete set of measures a laboratory must undertake to ensure that it is able to achieve high-quality data continuously. Besides the use of validation and/or standardized methods, these measures are effective IQC procedures (use of reference materials, control charts, etc.), with participation in proficiency testing schemes and accreditation to an international standard, normally ISO/IEC 17025 [4]. Method validation and the different aspects of QA form the subject of Section 8.2.3. 

The advantage of the following methods of graphical representation is the clear and simple presentation of the essential facts. Simple charts, like bar charts, x-y scatter diagrams or pie diagrams, which are also available in 3D-form are also suited to visual representation of data. They are not described because this section is devoted to treatment of multivariate data. Graphs for control charts, particulary for quality assurance and control, can be found in [FUNK et al., 1992 AQS, 1991]. 

Good laboratory—what it is, how to apply it, p. 125 e How to vahdate a method selectivity, hnearity, accuracy, precision, sensitivity, range, LOD, LOQ, ruggedness, p. 126 Quality assurance control charts, documenting, proficiency testing, p. 133 Electronic records, p. 135... 

Internal quality assurance is concerned with maintaining precision within a single laboratory, where the importance of keeping careful records, based on reference materials and using appropriate control charts is well established. External quality assurance involves reference materials obtained from an outside body, and is a method for testing the bias of laboratory results. In practice, internal quality assurance mainly has to do with laboratory precision, whereas external quality assurance mainly has to do with laboratory bias (ISO 5725,1986). 

In this section, discussion is limited to suppliers of reference materials which are available internationally, i.e. we are concerned here more with external quality assurance than with internal quality assurance. This is not intended to minimize the importance of reference materials for internal quality assurance indeed these are essential for monitoring the precision of the analytical methods and for establishing statistical control, e.g. using control charts. 

All of the hardware, software, clever methodologies and validation go for nothing if analysts are unahle to assure the end user of the data that the method was in control throughout sample analysis and that the results can he trusted within the stated uncertainty limits. In addition to within-run acceptance criteria based on analysis of QCs and calibrators within a single batch run, control charts are also often used as a means for assessing whether or not the method is in control. Use of only the former as quality control indicators can often lead to missing problems that affect even the most fully validated methods. 

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